Detection and description of keypoints from an image is a well-studied problem in Computer Vision. Some
methods like SIFT, SURF or ORB are computationally really efficient. This paper proposes a solution for
a particular case study on object recognition of industrial parts based on hierarchical classification. Reducing the number of instances leads to better performance, indeed, that is what the use of the hierarchical
classification is looking for. We demonstrate that this method performs better than using just one method
like ORB, SIFT or FREAK, despite being fairly slower
2D FEATURES-BASED DETECTOR AND DESCRIPTOR SELECTION SYSTEM FOR HIERARCHICAL R...gerogepatton
Detection and description of keypoints from an image is a well-studied problem in Computer Vision. Some methods like SIFT, SURF or ORB are computationally really efficient. This paper proposes a solution for a particular case study on object recognition of industrial parts based on hierarchical classification. Reducing the number of instances leads to better performance, indeed, that is what the use of the hierarchical classification is looking for. We demonstrate that this method performs better than using just one method
like ORB, SIFT or FREAK, despite being fairly slower.
Computer Vision: Visual Extent of an ObjectIOSR Journals
The document discusses analyzing the visual extent of objects in images using computer vision techniques. It performs two analyses: 1) Without knowing object locations, it determines which image parts contribute most to object classification. 2) Assuming known object locations, it evaluates the potential of object vs. surround and object interior vs. border. The analyses find that object classification performance improves significantly when the object location is known. Descriptors from the object interior contribute more than those from the border. Knowing object locations allows separating relevant from irrelevant image regions.
A Literature Survey: Neural Networks for object detectionvivatechijri
Humans have a great capability to distinguish objects by their vision. But, for machines object
detection is an issue. Thus, Neural Networks have been introduced in the field of computer science. Neural
Networks are also called as ‘Artificial Neural Networks’ [13]. Artificial Neural Networks are computational
models of the brain which helps in object detection and recognition. This paper describes and demonstrates the
different types of Neural Networks such as ANN, KNN, FASTER R-CNN, 3D-CNN, RNN etc. with their accuracies.
From the study of various research papers, the accuracies of different Neural Networks are discussed and
compared and it can be concluded that in the given test cases, the ANN gives the best accuracy for the object
detection.
Improvement of Anomaly Detection Algorithms in Hyperspectral Images Using Dis...sipij
Recently anomaly detection (AD) has become an important application for target detection in hyperspectral remotely sensed images. In many applications, in addition to high accuracy of detection we need a fast and reliable algorithm as well. This paper presents a novel method to improve the performance of current AD algorithms. The proposed method first calculates Discrete Wavelet Transform (DWT) of every pixel vector of image using Daubechies4 wavelet. Then, AD algorithm performs on four bands of “Wavelet transform” matrix which are the approximation of main image. In this research some benchmark AD algorithms including Local RX, DWRX and DWEST have been implemented on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral datasets. Experimental results demonstrate significant improvement of runtime in proposed method. In addition, this method improves the accuracy of AD algorithms because of DWT’s power in extracting approximation coefficients of signal, which contain the main behaviour of signal, and abandon the redundant information in hyperspectral image data.
This document summarizes a research paper on background subtraction techniques for motion detection in video. It describes a proposed technique that stores and compares past pixel values to the current value to determine if a pixel belongs to the background or foreground. It also discusses using a k-means algorithm and Gaussian mixture model to build a probabilistic background model and classify pixels. The paper evaluates different shadow detection approaches and finds RGB color spaces perform best for segmentation and shadow removal.
Land Boundary Detection of an Island using improved Morphological OperationCSCJournals
Image analysis is one of the important tasks to obtain the information about earth surface. To detect and mark a particular land area, it is required to have the image from remote place. To recognize the same, the accurate boundary of that area has to be detected. In this paper, the example of remote sensing image has been considered. The accurate detection of the boundary is a complex task. A novel method has been proposed in this paper to detect the boundary of such land. Mathematical morphology is a simple and efficient method for this type of task. The morphological analysis is performed using structure elements (SE). By using mathematical morphology the images can be enhanced and then the boundary can be detected easily. Simultaneously the noise is removed by using the proposed model. The results exhibit the performance of the proposed method. Keywords: Remote Sensing images ; Edge detection; Gray- scale Morphological analysis, Structuring Element (SE).
ROLE OF HYBRID LEVEL SET IN FETAL CONTOUR EXTRACTIONsipij
Image processing technologies may be employed for quicker and accurate diagnosis in analysis and
feature extraction of medical images. Here, existing level set algorithm is modified and it is employed for
extracting contour of fetus in an image. In traditional approach, fetal parameters are extracted manually
from ultrasound images. An automatic technique is highly desirable to obtain fetal biometric measurements
due to some problems in traditional approach such as lack of consistency and accuracy. The proposed
approach utilizes global & local region information for fetal contour extraction from ultrasonic images.
The main goal of this research is to develop a new methodology to aid the analysis and feature extraction.
A Pattern Classification Based approach for Blur Classificationijeei-iaes
Blur type identification is one of the most crucial step of image restoration. In case of blind restoration of such images, it is generally assumed that the blur type is known prior to restoration of such images. However, it is not practical in real applications. So, blur type identification is extremely desirable before application of blind restoration technique to restore a blurred image. An approach to categorize blur in three classes namely motion, defocus, and combined blur is presented in this paper. Curvelet transform based energy features are utilized as features of blur patterns and a neural network is designed for classification. The simulation results show preciseness of proposed approach.
2D FEATURES-BASED DETECTOR AND DESCRIPTOR SELECTION SYSTEM FOR HIERARCHICAL R...gerogepatton
Detection and description of keypoints from an image is a well-studied problem in Computer Vision. Some methods like SIFT, SURF or ORB are computationally really efficient. This paper proposes a solution for a particular case study on object recognition of industrial parts based on hierarchical classification. Reducing the number of instances leads to better performance, indeed, that is what the use of the hierarchical classification is looking for. We demonstrate that this method performs better than using just one method
like ORB, SIFT or FREAK, despite being fairly slower.
Computer Vision: Visual Extent of an ObjectIOSR Journals
The document discusses analyzing the visual extent of objects in images using computer vision techniques. It performs two analyses: 1) Without knowing object locations, it determines which image parts contribute most to object classification. 2) Assuming known object locations, it evaluates the potential of object vs. surround and object interior vs. border. The analyses find that object classification performance improves significantly when the object location is known. Descriptors from the object interior contribute more than those from the border. Knowing object locations allows separating relevant from irrelevant image regions.
A Literature Survey: Neural Networks for object detectionvivatechijri
Humans have a great capability to distinguish objects by their vision. But, for machines object
detection is an issue. Thus, Neural Networks have been introduced in the field of computer science. Neural
Networks are also called as ‘Artificial Neural Networks’ [13]. Artificial Neural Networks are computational
models of the brain which helps in object detection and recognition. This paper describes and demonstrates the
different types of Neural Networks such as ANN, KNN, FASTER R-CNN, 3D-CNN, RNN etc. with their accuracies.
From the study of various research papers, the accuracies of different Neural Networks are discussed and
compared and it can be concluded that in the given test cases, the ANN gives the best accuracy for the object
detection.
Improvement of Anomaly Detection Algorithms in Hyperspectral Images Using Dis...sipij
Recently anomaly detection (AD) has become an important application for target detection in hyperspectral remotely sensed images. In many applications, in addition to high accuracy of detection we need a fast and reliable algorithm as well. This paper presents a novel method to improve the performance of current AD algorithms. The proposed method first calculates Discrete Wavelet Transform (DWT) of every pixel vector of image using Daubechies4 wavelet. Then, AD algorithm performs on four bands of “Wavelet transform” matrix which are the approximation of main image. In this research some benchmark AD algorithms including Local RX, DWRX and DWEST have been implemented on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral datasets. Experimental results demonstrate significant improvement of runtime in proposed method. In addition, this method improves the accuracy of AD algorithms because of DWT’s power in extracting approximation coefficients of signal, which contain the main behaviour of signal, and abandon the redundant information in hyperspectral image data.
This document summarizes a research paper on background subtraction techniques for motion detection in video. It describes a proposed technique that stores and compares past pixel values to the current value to determine if a pixel belongs to the background or foreground. It also discusses using a k-means algorithm and Gaussian mixture model to build a probabilistic background model and classify pixels. The paper evaluates different shadow detection approaches and finds RGB color spaces perform best for segmentation and shadow removal.
Land Boundary Detection of an Island using improved Morphological OperationCSCJournals
Image analysis is one of the important tasks to obtain the information about earth surface. To detect and mark a particular land area, it is required to have the image from remote place. To recognize the same, the accurate boundary of that area has to be detected. In this paper, the example of remote sensing image has been considered. The accurate detection of the boundary is a complex task. A novel method has been proposed in this paper to detect the boundary of such land. Mathematical morphology is a simple and efficient method for this type of task. The morphological analysis is performed using structure elements (SE). By using mathematical morphology the images can be enhanced and then the boundary can be detected easily. Simultaneously the noise is removed by using the proposed model. The results exhibit the performance of the proposed method. Keywords: Remote Sensing images ; Edge detection; Gray- scale Morphological analysis, Structuring Element (SE).
ROLE OF HYBRID LEVEL SET IN FETAL CONTOUR EXTRACTIONsipij
Image processing technologies may be employed for quicker and accurate diagnosis in analysis and
feature extraction of medical images. Here, existing level set algorithm is modified and it is employed for
extracting contour of fetus in an image. In traditional approach, fetal parameters are extracted manually
from ultrasound images. An automatic technique is highly desirable to obtain fetal biometric measurements
due to some problems in traditional approach such as lack of consistency and accuracy. The proposed
approach utilizes global & local region information for fetal contour extraction from ultrasonic images.
The main goal of this research is to develop a new methodology to aid the analysis and feature extraction.
A Pattern Classification Based approach for Blur Classificationijeei-iaes
Blur type identification is one of the most crucial step of image restoration. In case of blind restoration of such images, it is generally assumed that the blur type is known prior to restoration of such images. However, it is not practical in real applications. So, blur type identification is extremely desirable before application of blind restoration technique to restore a blurred image. An approach to categorize blur in three classes namely motion, defocus, and combined blur is presented in this paper. Curvelet transform based energy features are utilized as features of blur patterns and a neural network is designed for classification. The simulation results show preciseness of proposed approach.
Hardware Unit for Edge Detection with Comparative Analysis of Different Edge ...paperpublications3
Abstract: An edge in an image is a contour across which the brightness of the image changes abruptly. In image processing, an edge is often interpreted as one class of singularities. Edge detection is an important task in image processing. It is a main tool in pattern recognition, image segmentation, and scene analysis. An edge detector is basically a high pass filter that can be applied to extract the edge points in an image. This topic has attracted many researchers and many achievements have been made. Many researchers provided different approaches based on mathematical calculations which some of them are either robust or cost effective. A new algorithm will be proposed to detect the edges of image with increased robustness and throughput. Using this algorithm we will reduce the time complexity problem which is faced by previous algorithm. We will also propose hardware unit for proposed algorithm which will reduce the area, power and speed problem. We will compare our proposed algorithm with previous approach. For image quality measurement we will use some scientific parameters those are PSNR, SSIM, FSIM. Implementation of proposed algorithm will be done by Matlab and hardware implementation will be done by using of Verilog on Xilinx 14.1 simulator. Verification will be done on Model sim.
A NOVEL BACKGROUND SUBTRACTION ALGORITHM FOR PERSON TRACKING BASED ON K-NN csandit
Object tracking can be defined as the process of detecting an object of interest from a video scene and keeping track of its motion, orientation, occlusion etc. in order to extract useful
information. It is indeed a challenging problem and it’s an important task. Many researchers are getting attracted in the field of computer vision, specifically the field of object tracking in video surveillance. The main purpose of this paper is to give to the reader information of the present state of the art object tracking, together with presenting steps involved in Background Subtraction and their techniques. In related literature we found three main methods of object tracking: the first method is the optical flow; the second is related to the background subtraction, which is divided into two types presented in this paper, and the last one is temporal
differencing. We present a novel approach to background subtraction that compare a current frame with the background model that we have set before, so we can classified each pixel of the image as a foreground or a background element, then comes the tracking step to present our object of interest, which is a person, by his centroid. The tracking step is divided into two different methods, the surface method and the K-NN method, both are explained in the paper.Our proposed method is implemented and evaluated using CAVIAR database.
Uniform and non uniform single image deblurring based on sparse representatio...ijma
Considering the sparseness property of images, a sparse representation based iterative deblurring method
is presented for single image deblurring under uniform and non-uniform motion blur. The approach taken
is based on sparse and redundant representations over adaptively training dictionaries from single
blurred-noisy image itself. Further, the K-SVD algorithm is used to obtain a dictionary that describes the
image contents effectively. Comprehensive experimental evaluation demonstrate that the proposed
framework integrating the sparseness property of images, adaptive dictionary training and iterative
deblurring scheme together significantly improves the deblurring performance and is comparable with the
state-of-the art deblurring algorithms and seeks a powerful solution to an ill-conditioned inverse problem.
Object based Classification of Satellite Images by Combining the HDP, IBP and...IRJET Journal
This document presents a method for unsupervised object-based classification of very high resolution panchromatic and multispectral satellite images. It uses hierarchical Dirichlet process (HDP) and Indian buffet process (IBP) to determine color frequencies for different areas after image segmentation using k-means clustering. Support vector machine (SVM) classification is then applied based on the color frequencies to classify the image objects into land cover classes. The hierarchical structure of the model transmits spatial information between image layers to provide cues for classification. The method aims to solve problems with traditional probabilistic topic models and achieve effective unsupervised classification of high resolution satellite images.
REGISTRATION TECHNOLOGIES and THEIR CLASSIFICATION IN AUGMENTED REALITY THE K...IJCSEA Journal
The registration in augmented reality is process which merges virtual objects generated by computer with real world image caught by camera. This paper describes the knowledge-based registration, computer vision-based registration and tracker-based registration technology. This paper mainly focused on trackerbased
registration technology in augmented reality. Also described method in tracker- based technology, problem and solution.
NEURAL NETWORKS FOR HIGH PERFORMANCE TIME-DELAY ESTIMATION AND ACOUSTIC SOURC...csandit
Time-delay estimation is an essential building block of many signal processing applications.This paper follows up on earlier work for acoustic source localization and time delay estimation
using pattern recognition techniques in the adverse environment such as reverberant rooms or underwater; it presents unprecedented high performance results obtained with supervised training of neural networks which challenge the state of the art and compares its performance to that of well-known methods such as the Generalized Cross-Correlation or Adaptive Eigenvalue Decomposition.
This document discusses a new methodology for shadow removal from images based on Strong Edge Detection (SED). The SED method recognizes strong shadow edges by analyzing image patch characteristics and features to classify shadow edges. Spatial smoothing is also used. Entropy and standard deviation results of the SED method and an earlier Patch Based Shadow Edge Detection method are calculated and compared, showing the SED method performs better with lower entropy and higher standard deviation. The SED method detects shadow edges more accurately than the previous patch-based approach.
The document proposes a new method to identify aggregate scanning strategies from eye tracking data by automatically clustering and aggregating groups of matching scanpaths. It begins by converting scanpaths to sequences of viewed area names. These sequences are then represented in a dotplot where matching sequences are identified using linear regressions. Matching sequences are then used to hierarchically cluster the scanpaths. Aggregate scanning strategies are generated for each cluster and presented interactively to identify strategies within groups and conditions. This allows discovery of both bottom-up and top-down matching strategies to better understand group scanning behaviors.
A Survey on Approaches for Object Trackingjournal ijrtem
This document summarizes various approaches for object tracking in video sequences. It discusses common object detection methods like temporal differencing, optical flow and background subtraction. For object representation, it describes shape-based, motion-based, color-based and texture-based approaches. For object tracking, it analyzes target representation and localization methods like blob tracking and mean shift, as well as filtering and data association approaches like the Kalman filter and particle filter. It provides comparisons between Kalman and particle filters, and between contour tracking and visual feature matching methods. The document concludes that further research could improve computational efficiency and decrease tracking time for diverse video content.
Schematic model for analyzing mobility and detection of multipleIAEME Publication
The document discusses a schematic model for analyzing mobility and detecting multiple objects in traffic scenes. It aims to not only detect and count moving objects, but also understand crowd behavior and reduce issues with objects occluding each other. Previous work on object detection is reviewed, noting that most approaches do not integrate detecting multiple objects simultaneously or address problems of object occlusion. The proposed model uses background subtraction and unscented Kalman filtering to increase detection accuracy and reduce false positives when analyzing image sequences of traffic scenes to detect multiple moving objects. It was tested in MATLAB and results showed highly accurate detection rates.
TRACKING OF PARTIALLY OCCLUDED OBJECTS IN VIDEO SEQUENCESPraveen Pallav
The document describes a proposed algorithm for tracking partially occluded objects in video sequences. The algorithm uses background subtraction and morphological operations to create a binary mask and detect regions of interest. It then applies Lucas Kanade optical flow tracking and maintains a dictionary to track multiple objects across frames. The algorithm was tested on standard and custom databases and was able to track objects when partially occluded by combining color, motion, and feature cues. Potential applications of the algorithm include human-computer interaction, anomaly detection, traffic surveillance, and robot navigation.
Enhanced Optimization of Edge Detection for High Resolution Images Using Veri...ijcisjournal
dge Detection plays a crucial role in Image Processing and Segmentation where a set of algorithms aims
to identify various portions of a digital image at which a sharpened image is observed in the output or
more formally has discontinuities. The contour of Edge Detection also helps in Object Detection and
Recognition. Image edges can be detected by using two attributes such as Gradient and Laplacian. In our
Paper, we proposed a system which utilizes Canny and Sobel Operators for Edge Detection which is a
Gradient First order derivative function for edge detection by using Verilog Hardware Description
Language and in turn compared with the results of the previous paper in Matlab. The process of edge
detection in Verilog significantly reduces the processing time and filters out unneeded information, while
preserving the important structural properties of an image. This edge detection can be used to detect
vehicles in Traffic Jam, Medical imaging system for analysing MRI, x-rays by using Xilinx ISE Design
Suite 14.2.
Gesture Recognition using Principle Component Analysis & Viola-Jones AlgorithmIJMER
Gesture recognition pertains to recognizing meaningful expressions of motion by a human,
involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent
and efficient human–computer interface. The applications of gesture recognition are manifold, ranging
from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on
gesture recognition with particular emphasis on hand gestures and facial expressions. Applications
involving wavelet transform and principal component analysis for face and hand gesture recognition on
digital images
Robust Clustering of Eye Movement Recordings for QuantiGiuseppe Fineschi
Characterizing the location and extent of a viewer’s interest, in terms of eye movement recordings, informs a range of investigations in image and scene viewing. We present an automatic data-driven method for accomplishing this, which clusters visual point-of-regard (POR) measurements into gazes and regions-ofinterest using the mean shift procedure. Clusters produced using this method form a structured representation of viewer interest, and at the same time are replicable and not heavily influenced by noise or outliers. Thus, they are useful in answering fine-grained questions about where and how a viewer examined an image.
A Case Study : Circle Detection Using Circular Hough TransformIJSRED
This paper presents a case study on using the circular Hough transform (CHT) to detect circles in binary images. The CHT works by detecting edges using algorithms like Canny edge detection, and then applying the Hough transform to find parameter triplets (x, y, R) that correspond to circles. An accumulator matrix is used to tally parameter combinations that correspond to edges, with the highest tallies indicating detected circles. The paper applies this method to detect coins in an image, finding circles with 95% accuracy. It concludes the CHT is an effective algorithm for circle detection, though future work could optimize it and improve accuracy.
Algorithmic Analysis to Video Object Tracking and Background Segmentation and...Editor IJCATR
This document discusses algorithms for video object tracking and background segmentation. It analyzes the drawbacks of existing algorithms like partial least squares analysis, hidden Markov models, and CAMSHIFT that are used for object tracking. These include high computational complexity, sensitivity to changes in lighting/scene, and inability to handle occlusion. The document proposes using a Gaussian mixture model (GMM) and level set method for background segmentation, where each pixel is modeled as a mixture of Gaussians. It also proposes using wavelet transforms, specifically the Haar wavelet packet transform, for object tracking. The key advantages of these approaches are their ability to handle multi-modal backgrounds, occlusion, and noise in video signals.
This document summarizes a research paper that proposes an improved deconvolution algorithm to estimate blood flow velocity in nailfold vessels more accurately. The paper describes limitations in existing algorithms related to blurring and proposes using deconvolution and other image enhancement techniques. Results show the new algorithm takes less time (20-21 seconds vs 42-43 seconds) and tracks particle movement more accurately, allowing more precise flow measurements. This helps diagnosis of diseases. Future work could involve additional segmentation and machine learning to further automate and improve reliability.
Wireless Vision based Real time Object Tracking System Using Template MatchingIDES Editor
In the present work the concepts of template
matching through Normalized Cross Correlation (NCC) has
been used to implement a robust real time object tracking
system. In this implementation a wireless surveillance pinhole
camera has been used to grab the video frames from the non
ideal environment. Once the object has been detected it is
tracked by employing an efficient Template Matching
algorithm. The templates used for the matching purposes are
generated dynamically. This ensures that any change in the
pose of the object does not hinder the tracking procedure. To
automate the tracking process the camera is mounted on a
disc coupled with stepper motor, which is synchronized with a
tracking algorithm. As and when the object being tracked
moves out of the viewing range of the camera, the setup is
automatically adjusted to move the camera so as to keep the
object of about 360 degree field of view. The system is capable
of handling entry and exit of an object. The performance of
the proposed Object tracking system has been demonstrated
in real time environment both for indoor and outdoor by
including a variety of disturbances and a means to detect a
loss of track.
2D FEATURES-BASED DETECTOR AND DESCRIPTOR SELECTION SYSTEM FOR HIERARCHICAL R...ijaia
This document proposes a hierarchical recognition system for industrial parts that selects the optimal 2D feature-based detection and description method based on the part characteristics. The system first classifies new parts into groups that behave similarly under different recognition pipelines. It then uses the pipeline that on average performs best for that group, improving overall accuracy compared to using a single pipeline. The proposed approach is evaluated using leave-one-out cross-validation to test different pipelines on labeled image data of the industrial parts.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document provides a summary of feature extraction techniques in both the spatial and transformed domains. In the spatial domain, traditional methods like Canny, Sobel, and Harris detectors are discussed. The paper then focuses on the Scale Invariant Feature Transform (SIFT) technique developed by David Lowe, which extracts local features that are invariant to scale, rotation, and illumination changes. Several variants of SIFT are also examined, including PCA-SIFT, ASIFT, and A2SIFT, which improve robustness, distinctiveness, and performance. In the transformed domain section, the document explores techniques that use Fourier and wavelet transforms for feature extraction.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document provides a summary and analysis of various feature extraction techniques in both the spatial and transformed domains. In the spatial domain, traditional methods like Canny, Sobel, and Harris detectors are discussed. The paper then focuses on the Scale Invariant Feature Transform (SIFT) algorithm developed by David Lowe, which extracts key local image features that are invariant to scale, rotation, and illumination changes. Several variants of SIFT are also examined, including PCA-SIFT, ASIFT, and A2SIFT, which improve upon SIFT's robustness and performance. In the transformed domain section, the document explores feature extraction methods that use transforms like the Fourier and wavelet transforms to extract image features.
Hardware Unit for Edge Detection with Comparative Analysis of Different Edge ...paperpublications3
Abstract: An edge in an image is a contour across which the brightness of the image changes abruptly. In image processing, an edge is often interpreted as one class of singularities. Edge detection is an important task in image processing. It is a main tool in pattern recognition, image segmentation, and scene analysis. An edge detector is basically a high pass filter that can be applied to extract the edge points in an image. This topic has attracted many researchers and many achievements have been made. Many researchers provided different approaches based on mathematical calculations which some of them are either robust or cost effective. A new algorithm will be proposed to detect the edges of image with increased robustness and throughput. Using this algorithm we will reduce the time complexity problem which is faced by previous algorithm. We will also propose hardware unit for proposed algorithm which will reduce the area, power and speed problem. We will compare our proposed algorithm with previous approach. For image quality measurement we will use some scientific parameters those are PSNR, SSIM, FSIM. Implementation of proposed algorithm will be done by Matlab and hardware implementation will be done by using of Verilog on Xilinx 14.1 simulator. Verification will be done on Model sim.
A NOVEL BACKGROUND SUBTRACTION ALGORITHM FOR PERSON TRACKING BASED ON K-NN csandit
Object tracking can be defined as the process of detecting an object of interest from a video scene and keeping track of its motion, orientation, occlusion etc. in order to extract useful
information. It is indeed a challenging problem and it’s an important task. Many researchers are getting attracted in the field of computer vision, specifically the field of object tracking in video surveillance. The main purpose of this paper is to give to the reader information of the present state of the art object tracking, together with presenting steps involved in Background Subtraction and their techniques. In related literature we found three main methods of object tracking: the first method is the optical flow; the second is related to the background subtraction, which is divided into two types presented in this paper, and the last one is temporal
differencing. We present a novel approach to background subtraction that compare a current frame with the background model that we have set before, so we can classified each pixel of the image as a foreground or a background element, then comes the tracking step to present our object of interest, which is a person, by his centroid. The tracking step is divided into two different methods, the surface method and the K-NN method, both are explained in the paper.Our proposed method is implemented and evaluated using CAVIAR database.
Uniform and non uniform single image deblurring based on sparse representatio...ijma
Considering the sparseness property of images, a sparse representation based iterative deblurring method
is presented for single image deblurring under uniform and non-uniform motion blur. The approach taken
is based on sparse and redundant representations over adaptively training dictionaries from single
blurred-noisy image itself. Further, the K-SVD algorithm is used to obtain a dictionary that describes the
image contents effectively. Comprehensive experimental evaluation demonstrate that the proposed
framework integrating the sparseness property of images, adaptive dictionary training and iterative
deblurring scheme together significantly improves the deblurring performance and is comparable with the
state-of-the art deblurring algorithms and seeks a powerful solution to an ill-conditioned inverse problem.
Object based Classification of Satellite Images by Combining the HDP, IBP and...IRJET Journal
This document presents a method for unsupervised object-based classification of very high resolution panchromatic and multispectral satellite images. It uses hierarchical Dirichlet process (HDP) and Indian buffet process (IBP) to determine color frequencies for different areas after image segmentation using k-means clustering. Support vector machine (SVM) classification is then applied based on the color frequencies to classify the image objects into land cover classes. The hierarchical structure of the model transmits spatial information between image layers to provide cues for classification. The method aims to solve problems with traditional probabilistic topic models and achieve effective unsupervised classification of high resolution satellite images.
REGISTRATION TECHNOLOGIES and THEIR CLASSIFICATION IN AUGMENTED REALITY THE K...IJCSEA Journal
The registration in augmented reality is process which merges virtual objects generated by computer with real world image caught by camera. This paper describes the knowledge-based registration, computer vision-based registration and tracker-based registration technology. This paper mainly focused on trackerbased
registration technology in augmented reality. Also described method in tracker- based technology, problem and solution.
NEURAL NETWORKS FOR HIGH PERFORMANCE TIME-DELAY ESTIMATION AND ACOUSTIC SOURC...csandit
Time-delay estimation is an essential building block of many signal processing applications.This paper follows up on earlier work for acoustic source localization and time delay estimation
using pattern recognition techniques in the adverse environment such as reverberant rooms or underwater; it presents unprecedented high performance results obtained with supervised training of neural networks which challenge the state of the art and compares its performance to that of well-known methods such as the Generalized Cross-Correlation or Adaptive Eigenvalue Decomposition.
This document discusses a new methodology for shadow removal from images based on Strong Edge Detection (SED). The SED method recognizes strong shadow edges by analyzing image patch characteristics and features to classify shadow edges. Spatial smoothing is also used. Entropy and standard deviation results of the SED method and an earlier Patch Based Shadow Edge Detection method are calculated and compared, showing the SED method performs better with lower entropy and higher standard deviation. The SED method detects shadow edges more accurately than the previous patch-based approach.
The document proposes a new method to identify aggregate scanning strategies from eye tracking data by automatically clustering and aggregating groups of matching scanpaths. It begins by converting scanpaths to sequences of viewed area names. These sequences are then represented in a dotplot where matching sequences are identified using linear regressions. Matching sequences are then used to hierarchically cluster the scanpaths. Aggregate scanning strategies are generated for each cluster and presented interactively to identify strategies within groups and conditions. This allows discovery of both bottom-up and top-down matching strategies to better understand group scanning behaviors.
A Survey on Approaches for Object Trackingjournal ijrtem
This document summarizes various approaches for object tracking in video sequences. It discusses common object detection methods like temporal differencing, optical flow and background subtraction. For object representation, it describes shape-based, motion-based, color-based and texture-based approaches. For object tracking, it analyzes target representation and localization methods like blob tracking and mean shift, as well as filtering and data association approaches like the Kalman filter and particle filter. It provides comparisons between Kalman and particle filters, and between contour tracking and visual feature matching methods. The document concludes that further research could improve computational efficiency and decrease tracking time for diverse video content.
Schematic model for analyzing mobility and detection of multipleIAEME Publication
The document discusses a schematic model for analyzing mobility and detecting multiple objects in traffic scenes. It aims to not only detect and count moving objects, but also understand crowd behavior and reduce issues with objects occluding each other. Previous work on object detection is reviewed, noting that most approaches do not integrate detecting multiple objects simultaneously or address problems of object occlusion. The proposed model uses background subtraction and unscented Kalman filtering to increase detection accuracy and reduce false positives when analyzing image sequences of traffic scenes to detect multiple moving objects. It was tested in MATLAB and results showed highly accurate detection rates.
TRACKING OF PARTIALLY OCCLUDED OBJECTS IN VIDEO SEQUENCESPraveen Pallav
The document describes a proposed algorithm for tracking partially occluded objects in video sequences. The algorithm uses background subtraction and morphological operations to create a binary mask and detect regions of interest. It then applies Lucas Kanade optical flow tracking and maintains a dictionary to track multiple objects across frames. The algorithm was tested on standard and custom databases and was able to track objects when partially occluded by combining color, motion, and feature cues. Potential applications of the algorithm include human-computer interaction, anomaly detection, traffic surveillance, and robot navigation.
Enhanced Optimization of Edge Detection for High Resolution Images Using Veri...ijcisjournal
dge Detection plays a crucial role in Image Processing and Segmentation where a set of algorithms aims
to identify various portions of a digital image at which a sharpened image is observed in the output or
more formally has discontinuities. The contour of Edge Detection also helps in Object Detection and
Recognition. Image edges can be detected by using two attributes such as Gradient and Laplacian. In our
Paper, we proposed a system which utilizes Canny and Sobel Operators for Edge Detection which is a
Gradient First order derivative function for edge detection by using Verilog Hardware Description
Language and in turn compared with the results of the previous paper in Matlab. The process of edge
detection in Verilog significantly reduces the processing time and filters out unneeded information, while
preserving the important structural properties of an image. This edge detection can be used to detect
vehicles in Traffic Jam, Medical imaging system for analysing MRI, x-rays by using Xilinx ISE Design
Suite 14.2.
Gesture Recognition using Principle Component Analysis & Viola-Jones AlgorithmIJMER
Gesture recognition pertains to recognizing meaningful expressions of motion by a human,
involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent
and efficient human–computer interface. The applications of gesture recognition are manifold, ranging
from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on
gesture recognition with particular emphasis on hand gestures and facial expressions. Applications
involving wavelet transform and principal component analysis for face and hand gesture recognition on
digital images
Robust Clustering of Eye Movement Recordings for QuantiGiuseppe Fineschi
Characterizing the location and extent of a viewer’s interest, in terms of eye movement recordings, informs a range of investigations in image and scene viewing. We present an automatic data-driven method for accomplishing this, which clusters visual point-of-regard (POR) measurements into gazes and regions-ofinterest using the mean shift procedure. Clusters produced using this method form a structured representation of viewer interest, and at the same time are replicable and not heavily influenced by noise or outliers. Thus, they are useful in answering fine-grained questions about where and how a viewer examined an image.
A Case Study : Circle Detection Using Circular Hough TransformIJSRED
This paper presents a case study on using the circular Hough transform (CHT) to detect circles in binary images. The CHT works by detecting edges using algorithms like Canny edge detection, and then applying the Hough transform to find parameter triplets (x, y, R) that correspond to circles. An accumulator matrix is used to tally parameter combinations that correspond to edges, with the highest tallies indicating detected circles. The paper applies this method to detect coins in an image, finding circles with 95% accuracy. It concludes the CHT is an effective algorithm for circle detection, though future work could optimize it and improve accuracy.
Algorithmic Analysis to Video Object Tracking and Background Segmentation and...Editor IJCATR
This document discusses algorithms for video object tracking and background segmentation. It analyzes the drawbacks of existing algorithms like partial least squares analysis, hidden Markov models, and CAMSHIFT that are used for object tracking. These include high computational complexity, sensitivity to changes in lighting/scene, and inability to handle occlusion. The document proposes using a Gaussian mixture model (GMM) and level set method for background segmentation, where each pixel is modeled as a mixture of Gaussians. It also proposes using wavelet transforms, specifically the Haar wavelet packet transform, for object tracking. The key advantages of these approaches are their ability to handle multi-modal backgrounds, occlusion, and noise in video signals.
This document summarizes a research paper that proposes an improved deconvolution algorithm to estimate blood flow velocity in nailfold vessels more accurately. The paper describes limitations in existing algorithms related to blurring and proposes using deconvolution and other image enhancement techniques. Results show the new algorithm takes less time (20-21 seconds vs 42-43 seconds) and tracks particle movement more accurately, allowing more precise flow measurements. This helps diagnosis of diseases. Future work could involve additional segmentation and machine learning to further automate and improve reliability.
Wireless Vision based Real time Object Tracking System Using Template MatchingIDES Editor
In the present work the concepts of template
matching through Normalized Cross Correlation (NCC) has
been used to implement a robust real time object tracking
system. In this implementation a wireless surveillance pinhole
camera has been used to grab the video frames from the non
ideal environment. Once the object has been detected it is
tracked by employing an efficient Template Matching
algorithm. The templates used for the matching purposes are
generated dynamically. This ensures that any change in the
pose of the object does not hinder the tracking procedure. To
automate the tracking process the camera is mounted on a
disc coupled with stepper motor, which is synchronized with a
tracking algorithm. As and when the object being tracked
moves out of the viewing range of the camera, the setup is
automatically adjusted to move the camera so as to keep the
object of about 360 degree field of view. The system is capable
of handling entry and exit of an object. The performance of
the proposed Object tracking system has been demonstrated
in real time environment both for indoor and outdoor by
including a variety of disturbances and a means to detect a
loss of track.
2D FEATURES-BASED DETECTOR AND DESCRIPTOR SELECTION SYSTEM FOR HIERARCHICAL R...ijaia
This document proposes a hierarchical recognition system for industrial parts that selects the optimal 2D feature-based detection and description method based on the part characteristics. The system first classifies new parts into groups that behave similarly under different recognition pipelines. It then uses the pipeline that on average performs best for that group, improving overall accuracy compared to using a single pipeline. The proposed approach is evaluated using leave-one-out cross-validation to test different pipelines on labeled image data of the industrial parts.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document provides a summary of feature extraction techniques in both the spatial and transformed domains. In the spatial domain, traditional methods like Canny, Sobel, and Harris detectors are discussed. The paper then focuses on the Scale Invariant Feature Transform (SIFT) technique developed by David Lowe, which extracts local features that are invariant to scale, rotation, and illumination changes. Several variants of SIFT are also examined, including PCA-SIFT, ASIFT, and A2SIFT, which improve robustness, distinctiveness, and performance. In the transformed domain section, the document explores techniques that use Fourier and wavelet transforms for feature extraction.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document provides a summary and analysis of various feature extraction techniques in both the spatial and transformed domains. In the spatial domain, traditional methods like Canny, Sobel, and Harris detectors are discussed. The paper then focuses on the Scale Invariant Feature Transform (SIFT) algorithm developed by David Lowe, which extracts key local image features that are invariant to scale, rotation, and illumination changes. Several variants of SIFT are also examined, including PCA-SIFT, ASIFT, and A2SIFT, which improve upon SIFT's robustness and performance. In the transformed domain section, the document explores feature extraction methods that use transforms like the Fourier and wavelet transforms to extract image features.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document provides a summary of feature extraction techniques in both the spatial and transformed domains. In the spatial domain, traditional methods like Canny, Sobel, and Harris detectors are discussed. The paper finds these methods extract too much irrelevant information or lose some prominent features. The Scale Invariant Feature Transform (SIFT) is presented as addressing these issues by detecting local features in a scale and rotation invariant manner. In the transformed domain, techniques using Fourier and wavelet transforms are explored. The paper provides an overview of various feature extraction methods and their advantages and limitations.
IRJET- Object Detection using Hausdorff DistanceIRJET Journal
This document proposes a new object recognition system using Hausdorff distance. The system aims to improve on existing methods like YOLO that struggle with small objects and can capture garbage data. The document outlines preprocessing steps like noise cancellation, representing shapes as point sets, and extracting features. It then describes using Hausdorff distance and shape context to find the best match between input and reference shapes. Testing on datasets showed encouraging results for recognizing handwritten digits.
IRJET - Object Detection using Hausdorff DistanceIRJET Journal
This document proposes using Hausdorff distance for object detection as it can better handle noise compared to other methods like Euclidean distance. The document discusses preprocessing images using Gaussian filtering for noise cancellation. It then represents shapes as point sets for feature extraction before using Hausdorff distance to match shapes between reference and test images for object recognition. Encouraging results were obtained when testing on MNIST, COIL and private handwritten digit datasets.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Survey On Tracking Moving Objects Using Various AlgorithmsIJMTST Journal
Sparse representation has been applied to the object tracking problem. Mining the self- similarities between particles via multitask learning can improve tracking performance. How-ever, some particles may be different from others when they are sampled from a large region. Imposing all particles share the same structure may degrade the results. To overcome this problem, we propose a tracking algorithm based on robust multitask sparse representation (RMTT) in this letter. When we learn the particle representations, we decompose the sparse coefficient matrix into two parts in our algorithm. Joint sparse regularization is imposed on one coefficient matrix while element-wise sparse regularization is imposed on another matrix. The former regularization exploits self-similarities of particles while the later one considers the differences between them.
Speeded-up and Compact Visual Codebook for Object RecognitionCSCJournals
The well known framework in the object recognition literature uses local information extracted at several patches in images which are then clustered by a suitable clustering technique. A visual codebook maps the patch-based descriptors into a fixed-length vector in histogram space to which standard classifiers can be directly applied. Thus, the construction of a codebook is an important step which is usually done by cluster analysis. However, it is still difficult to construct a compact codebook with reduced computational cost. This paper evaluates the effectiveness and generalisation performance of the Resource-Allocating Codebook (RAC) approach that overcomes the problem of constructing fixed size codebooks that can be used at any time in the learning process and the learning patterns do not have to be repeated. It either allocates a new codeword based on the novelty of a newly seen pattern, or adapts the codebook to fit that observation. Furthermore, we improve RAC to yield codebooks that are more compact. We compare and contrast the recognition performance of RAC evaluated with two distinctive feature descriptors: SIFT and SURF and two clustering techniques: K-means and Fast Reciprocal Nearest Neighbours (fast-RNN) algorithms. SVM is used in classifying the image signatures. The entire visual object recognition pipeline has been tested on three benchmark datasets: PASCAL visual object classes challenge 2007, UIUC texture, and MPEG-7 Part-B silhouette image datasets. Experimental results show that RAC is suitable for constructing codebooks due to its wider span of the feature space. Moreover, RAC takes only one-pass through the entire data that slightly outperforms traditional approaches at drastically reduced computing times. The modified RAC performs slightly better than RAC and gives more compact codebook. Future research should focus on designing more discriminative and compact codebooks such as RAC rather than focusing on methods tuned to achieve high performance in classification.
This summarizes an academic paper that proposes an unsupervised algorithm to detect regions of interest (ROIs) in images using fast feature detectors. It detects keypoints using Speeded-Up Robust Features (SURF) and Features from Accelerated Segment Test (FAST) to maximize interest points. It categorizes keypoints as foreground or background using k-nearest neighbors classification on texture descriptors. ROIs are identified as groups of foreground keypoints. Preliminary experiments showed this approach can efficiently detect ROIs without computationally expensive comparisons between images.
Object tracking with SURF: ARM-Based platform ImplementationEditor IJCATR
This document describes research on implementing the SURF (Speeded Up Robust Features) algorithm for real-time object tracking on a Raspberry Pi mobile platform. The SURF algorithm extracts features from images that can be used for object detection and tracking across multiple frames. The researchers implemented an application on the Raspberry Pi to select an object image and perform SURF feature extraction and matching between the object image and live camera frames to detect and track the object in real-time video. They discuss adapting the SURF algorithm and libraries to optimize performance on the Raspberry Pi's hardware within its computational limitations for real-time tracking. Experimental results demonstrate object detection and tracking on test and live video streams using the Raspberry
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Integrated Hidden Markov Model and Kalman Filter for Online Object Trackingijsrd.com
Visual prior from generic real-world images study to represent that objects in a scene. The existing work presented online tracking algorithm to transfers visual prior learned offline for online object tracking. To learn complete dictionary to represent visual prior with collection of real world images. Prior knowledge of objects is generic and training image set does not contain any observation of target object. Transfer learned visual prior to construct object representation using Sparse coding and Multiscale max pooling. Linear classifier is learned online to distinguish target from background and also to identify target and background appearance variations over time. Tracking is carried out within Bayesian inference framework and learned classifier is used to construct observation model. Particle filter is used to estimate the tracking result sequentially however, unable to work efficiently in noisy scenes. Time sift variance were not appropriated to track target object with observer value to prior information of object structure. Proposal HMM based kalman filter to improve online target tracking in noisy sequential image frames. The covariance vector is measured to identify noisy scenes. Discrete time steps are evaluated for identifying target object with background separation. Experiment conducted on challenging sequences of scene. To evaluate the performance of object tracking algorithm in terms of tracking success rate, Centre location error, Number of scenes, Learning object sizes, and Latency for tracking.
Logo Matching for Document Image Retrieval Using SIFT DescriptorsIJERA Editor
In current trends the logos are playing a vital role in industrial and all commercial applications. Fundamentally the logo is defined as it’s a graphic entity which contains colors textures, shapes and text etc., which is organized in some special visible format. But unfortunately it is very difficult thing to save their brand logos from duplicates. In practical world there are several systems available for logo reorganization and detection with different kinds of requirements. Two dimensional global descriptors are used for logo matching and reorganization. The concept of Shape descriptors based on Shape context and the global descriptors are based on the logo contours. There is an algorithm which is implemented for logo detection is based on partial spatial context and spatial spectral saliency (SSS). The SSS is able to keep away from the confusion effect of background and also speed up the process of logo detection. All such methods are useful only when the logo is visible completely without noise and not subjected to change. We contribute, through this paper, to the design of a novel variation framework able to match and recognize multiple instances of multiple reference logos in image archives. Reference logos and test images are seen as constellations of local features (interest points, regions, etc.) and matched by minimizing an energy function mixing: 1) a fidelity term that measures the quality of feature matching, 2) a neighborhood criterion that captures feature co-occurrence geometry, and 3) a regularization term that controls the smoothness of the matching solution. We also introduce a detection/recognition procedure and study its theoretical consistency
IRJET-Comparison of SIFT & SURF Corner Detector as Features and other Machine...IRJET Journal
This document compares the SIFT and SURF corner detection algorithms for extracting features from leaf images that can then be used in machine learning techniques for leaf identification. It proposes using SIFT or SURF to extract features from leaf training images, clustering the features using the CURE algorithm into k clusters, and building a decision tree for each cluster using C4.5 or CART classification. The same process would be applied to unknown leaf images to extract features, match them to the closest cluster, and traverse the corresponding decision tree to identify the leaf.
HOL, GDCT AND LDCT FOR PEDESTRIAN DETECTIONcsandit
In this paper, we present and analyze different approaches implemented here to resolve pedestrian detection problem. Histograms of Oriented Laplacian (HOL) is a descriptor of
characteristic, it aims to highlight objects in digital images, Discrete Cosine Transform DCT with its two version global (GDCT) and local (LDCT), it changes image's pixel into frequencies coefficients and then we use them as a characteristics in the process. We implemented
independently these methods and tried to combine it and used there outputs in a classifier, the new generated classifier has proved it efficiency in certain cases. The performance of those
methods and their combination is tested on most popular Dataset in pedestrian detection, which
are INRIA and Daimler.
HOL, GDCT AND LDCT FOR PEDESTRIAN DETECTIONcscpconf
The document presents and analyzes different approaches for pedestrian detection, including Histograms of Oriented Laplacian (HOL), Global Discrete Cosine Transform (GDCT), and Local Discrete Cosine Transform (LDCT). HOL is used to highlight objects in images while GDCT and LDCT convert images into frequency coefficients that are then used as characteristics for classification. The performance of these individual methods and their combination is tested on popular pedestrian detection datasets like INRIA and Daimler. Experimental results show the new classifier generated by combining the method outputs proves more efficient in certain cases.
This document provides a review of graph-based image classification and frequent subgraph mining algorithms. It first introduces graph-based image representation and the need for approximate subgraph matching to account for noise and distortions. It then surveys several existing frequent subgraph mining algorithms, including CSMiner, SUBDUE, gdFil, FSG, and PrefixSpan. These algorithms are categorized as Apriori-based approaches or pattern-growth approaches. The document also discusses applications of graph mining in other domains such as chemistry and biology.
This document reviews object detection techniques for mobile robot navigation in dynamic indoor environments. It begins with an abstract that outlines the purpose of object detection for mobile robots and provides an overview of different techniques. It then reviews object detection approaches in two main categories: local feature-based techniques that use features like color, shape and templates, and deep learning-based techniques that use neural networks for object proposals or one-shot detection. Key algorithms discussed include SIFT, SURF, R-CNN, Fast R-CNN, Faster R-CNN, YOLO and SSD. The challenges of object detection and applications for mobile robot navigation are also mentioned.
An efficient technique for color image classification based on lower feature ...Alexander Decker
This document discusses an efficient technique for color image classification using support vector machines with radial basis functions (SVM-RBF). It presents SVM-RBF as an improvement over other classification methods like SVM with ant colony optimization (SVM-ACO) and directed acyclic graph (SVM-DAG). The paper tests the different classifiers on 600 images across 3 classes, finding SVM-RBF achieved the highest precision and recall rates, with precision of 92.3-94% and recall of 84.8-91%. It concludes SVM-RBF more effectively reduces noise and the semantic gap to enhance image classification performance compared to the other methods.
Similar to 2D Features-based Detector and Descriptor Selection System for Hierarchical Recognition of Industrial Parts (20)
PhotoQR: A Novel ID Card with an Encoded Viewgerogepatton
There is an increasing interest in developing techniques to identify and assess data to allow an easy and
continuous access to resources, services or places that require thorough ID control. Usually, in order to
give access to these resources, different kinds of documents are mandatory. In order to avoid forgeries
without the need of extra credentials, a new system –named photoQR, is here proposed. This system is
based on a ID card having two objects: one person’s picture (pre-processed via blur and/or swirl
techniques) and one QR code containing embedded data related to the picture. The idea is that the picture
and the QR code can assess each other by a proper hash value in the QR. The QR without the picture
cannot be assessed and vice versa. An open source prototype of the photoQR system has been implemented
in Python and can be used both in offline and real-time environments, which effectively combines security
concepts and image processing algorithms to obtain data assessment.
12th International Conference of Artificial Intelligence and Fuzzy Logic (AI ...gerogepatton
12th International Conference of Artificial Intelligence and Fuzzy Logic (AI & FL 2024) provides a
forum for researchers who address this issue and to present their work in a peer-reviewed forum. Authors
are solicited to contribute to the conference by submitting articles that illustrate research results, projects,
surveying works and industrial experiences that describe significant advances in the following areas, but
are not limited to these topics only.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
May 2024 - Top 10 Read Articles in Artificial Intelligence and Applications (...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
3rd International Conference on Artificial Intelligence Advances (AIAD 2024)gerogepatton
3rd International Conference on Artificial Intelligence Advances (AIAD 2024) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the area advanced Artificial Intelligence. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the research area. Core areas of AI and advanced multi-disciplinary and its applications will be covered during the conferences.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Information Extraction from Product Labels: A Machine Vision Approachgerogepatton
This research tackles the challenge of manual data extraction from product labels by employing a blend of
computer vision and Natural Language Processing (NLP). We introduce an enhanced model that combines
Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in a Convolutional
Recurrent Neural Network (CRNN) for reliable text recognition. Our model is further refined by
incorporating the Tesseract OCR engine, enhancing its applicability in Optical Character Recognition
(OCR) tasks. The methodology is augmented by NLP techniques and extended through the Open Food
Facts API (Application Programming Interface) for database population and text-only label prediction.
The CRNN model is trained on encoded labels and evaluated for accuracy on a dedicated test set.
Importantly, our approach enables visually impaired individuals to access essential information on
product labels, such as directions and ingredients. Overall, the study highlights the efficacy of deep
learning and OCR in automating label extraction and recognition.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Research on Fuzzy C- Clustering Recursive Genetic Algorithm based on Cloud Co...gerogepatton
Aiming at the problems of poor local search ability and precocious convergence of fuzzy C-cluster
recursive genetic algorithm (FOLD++), a new fuzzy C-cluster recursive genetic algorithm based on
Bayesian function adaptation search (TS) was proposed by incorporating the idea of Bayesian function
adaptation search into fuzzy C-cluster recursive genetic algorithm. The new algorithm combines the
advantages of FOLD++ and TS. In the early stage of optimization, fuzzy C-cluster recursive genetic
algorithm is used to get a good initial value, and the individual extreme value pbest is put into Bayesian
function adaptation table. In the late stage of optimization, when the searching ability of fuzzy C-cluster
recursive genetic is weakened, the short term memory function of Bayesian function adaptation table in
Bayesian function adaptation search algorithm is utilized. Make it jump out of the local optimal solution,
and allow bad solutions to be accepted during the search. The improved algorithm is applied to function
optimization, and the simulation results show that the calculation accuracy and stability of the algorithm
are improved, and the effectiveness of the improved algorithm is verified
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
10th International Conference on Artificial Intelligence and Soft Computing (...gerogepatton
10th International Conference on Artificial Intelligence and Soft Computing (AIS 2024) will
provide an excellent international forum for sharing knowledge and results in theory, methodology, and
applications of Artificial Intelligence, Soft Computing. The Conference looks for significant
contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical
aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from
both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
Employee attrition refers to the decrease in staff numbers within an organization due to various reasons.
As it has a negative impact on long-term growth objectives and workplace productivity, firms have
recognized it as a significant concern. To address this issue, organizations are increasingly turning to
machine-learning approaches to forecast employee attrition rates. This topic has gained significant
attention from researchers, especially in recent times. Several studies have applied various machinelearning methods to predict employee attrition, producing different resultsdepending on the employed
methods, factors, and datasets. However, there has been no comprehensive comparative review of multiple
studies applying machine-learning models to predict employee attrition to date. Therefore, this study aims
to fill this gap by providing an overview of research conducted on applying machine learning to predict
employee attrition from 2019 to February 2024. A literature review of relevant studies was conducted,
summarized, and classified. Most studies agree on conducting comparative experiments with multiple
predictive models to determine the most effective one.From this literature survey, the RF algorithm and
XGB ensemble method are repeatedly the best-performing, outperforming many other algorithms.
Additionally, the application of deep learning to employee attrition prediction issues also shows promise.
While there are discrepancies in the datasets used in previous studies, it is notable that the dataset
provided by IBM is the most widely utilized. This study serves as a concise review for new researchers,
facilitating their understanding of the primary techniques employed in predicting employee attrition and
highlighting recent research trends in this field. Furthermore, it provides organizations with insight into
the prominent factors affecting employee attrition, as identified by studies, enabling them to implement
solutions aimed at reducing attrition rates.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AIFU 2024) is a forum for presenting new advances and research results in the fields of Artificial Intelligence. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world. The scope of the conference covers all theoretical and practical aspects of the Artificial Intelligence.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Project Management Semester Long Project - Acuityjpupo2018
Acuity is an innovative learning app designed to transform the way you engage with knowledge. Powered by AI technology, Acuity takes complex topics and distills them into concise, interactive summaries that are easy to read & understand. Whether you're exploring the depths of quantum mechanics or seeking insight into historical events, Acuity provides the key information you need without the burden of lengthy texts.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
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Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
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2D Features-based Detector and Descriptor Selection System for Hierarchical Recognition of Industrial Parts
1. 2D FEATURES-BASED DETECTOR AND
DESCRIPTOR SELECTION SYSTEM FOR
HIERARCHICAL RECOGNITION OF INDUSTRIAL
PARTS
Ibon Merino1, Jon Azpiazu1, Anthony Remazeilles1, and Basilio Sierra2
1Industry and Transport, Tecnalia Research and Innovation, Donostia-San
Sebastian, Spain
{ibon.merino, jon.azpiazu, anthony.remazeilles}@tecnalia.com
2Computer Science and Artificial Intelligence, University of the Basque Country
UPV/EHU, Donostia-San Sebastian, Spain
b.sierra@ehu.eus
ABSTRACT
Detection and description of keypoints from an image is a well-studied problem in Computer Vision. Some
methods like SIFT, SURF or ORB are computationally really efficient. This paper proposes a solution for
a particular case study on object recognition of industrial parts based on hierarchical classification. Re-
ducing the number of instances leads to better performance, indeed, that is what the use of the hierarchical
classification is looking for. We demonstrate that this method performs better than using just one method
like ORB, SIFT or FREAK, despite being fairly slower.
KEYWORDS
Computer vision, Descriptors, Feature-based object recognition, Expert system
1. INTRODUCTION
Object recognition is an important branch of computer vision. Its main idea is to extract important
data or features from images in order to recognize which object is present on it. Many different
techniques are used in order to achieve this. In recent computer vision literature, it has been a
widely spread tendency to use deep learning due to their benefits throwing out many techniques
of previous literature that, actually, have a good performance in many cases. Our aim is to recover
those techniques in order to boost them and increase their performance or use their benefits that
neural networks may not have.
The classical methods in computer vision are based in pure mathematical operations were images
are used as matrices. These methods look for gradient changes, patterns... and try to find simi-
larities in different images or build a machine learning model to try to predict the objects that are
present in the image.
Our use case is the industrial area were many similar parts are to be recognized. Those parts vary
a lot from one to another (textures, size, color, reflections,...) so an expert is needed for choosing
which method is better for recognizing the objects. We propose a method that simulates the expert
role. This is achieved learning a model that classifies the objects in groups that behave similarly
to different recognition methods. This leads to a hierarchical classification that first classifies the
object to be recognized in one of the previously obtained groups and inside the group the method
that works better in that group is used to recognize the object.
International Journal of Artificial Intelligence & Applications (IJAIA) Vol.10, No.6, November 2019
DOI: 10.5121/ijaia.2019.10601 1
2. The paper is organized as follows. In Section 2 we present a state of art of the most used 2D
feature-based methods, including detectors, descriptors and matchers. The purpose of Section 3
is to present the method that we propose and how we evaluate it. The experiments done and their
results are shown in section 4. Section 5 summarizes the conclusions that can be drawn from our
work.
2. BACKGROUND
There are several methods for object recognition. In our case, we have focused on feature-based
methods. These methods look for points of interest of the images (detectors), try to describe them
(descriptors) and match them (matchers). The combination of different detectors, descriptors and
matchers vary the perfomance of the whole system. This is a fast growing area in image processing
field. The following short and chronologically ordered review presents the gradual improvements
in feature detection (Subsection 2.1), description (Subsection 2.2) and matching (Subsection 2.3).
2.1. 2D features detectors
One of the most used methods was proposed in 1999 by Lowe [14]. This method is called SIFT,
which stands for Scale Invariant Feature Transform. The main idea is to use the Difference-of-
Gaussian function (a close approximation to the Laplacian-of-Gaussian proposed by Lowe) to
search for extrema in the scale space. Even if SIFT was relatively fast, a new method, SURF
(Speeded Up Robust Features) [3], outperforms it in terms of repeatability, distinctiveness and
robustness, although it can be computed and compared much faster.
In addition, FAST (Features from Accelerated Segment Test) [25] proposed by Rosten and Drum-
mond introduce a fast detector. FAST outperforms previous algorithms (like SURF and SIFT) in
both computational performance and repeatability. AGAST [17] is based on the FAST, but it is
more efficient as well as generic. BRISK [12] is a novel method for keypoint detection, descrip-
tion and matching which has a low computational cost (as stated in the corresponding article, an
order of magnitude faster than SURF in some cases). Following the same line of FAST based
mehods, we find ORB Rublee et al. [26], an efficient alternative to SIFT or SURF. This method’s
detector is based on FAST but it adds orientation in order to obtain better results. In fact, this
method performs at two orders of magnitude faster than SIFT, in many situations.
Figure 1 shows some detectors and the relation between them chronologically ordered.
2.2. 2D features descriptors
Lowe also proposed a descriptor called SIFT. As mentioned above, is one of the most popular
feature detector and descriptor. The descriptor is a position-dependent histogram of local image
gradient directions around the interest point and is also scale invariant. It has numerous extensions
such as PCA-SIFT [10], that mixes PCA with SIFT; CSIFT [1], Color invariant SIFT; GLOH
[18]; DAISY [27], a dense descriptor inspired in SIFT and GLOH; and so on. SURF descriptor
[3] relies on integral images for image convolutions in order to obtain its speed.
BRIEF [4] is a highly discriminative feature descriptor that is fast both to build and to match.
BRISK [12] descriptor is composed as a binary string by concatenating the results of simple
brightness comparison tests. ORB descriptor is BRIEF-based and adds rotation invariance and
resistance to noise.
LBP (Local Binary Patterns) [22] is a two-level version of the texture spectrum method [28]. This
methods has been really popular and many derivatives has been proposed. Based on this, the CS-
LBP (Center-Symmetric Local Binary Pattern) [8] combines the strengths of SIFT and LBP. Later
International Journal of Artificial Intelligence & Applications (IJAIA) Vol.10, No.6, November 2019
2
3. Moravec
(1976)
Harris
(1988)
Shi-Tomasi
(1994)
SIFT
(1999)
SURF
(2006)
Hessian
(1998)
Hessian-affine
(2009)
ORB
(2011)
FAST
(2005)
MSER
(2004)
SUSAN
(1997)
BRISK
(2011)
Gabor-wavelet
(1996)
Steerable filters
(1991)
Canny
(1986)
(2001)
Harris-laplace
Hessian-laplace
Harris-affine
(2002)
AGAST
(2010)
CenSurE
(2008)
Figure 1: Recognition pipeline
in 2010, the LTP (Local Ternary Pattern) [13] appeared, a generalization of the LBP that is more
discriminant and less sensitive to noise in uniform regions. Same year, ELTP (Extended local
ternary pattern) [21] improved this by attempting to strike a balance by using a clustering method
to group the patterns in a meaningful way. In 2012, LTrP (Local Tetra Patterns) [20] encoded
the relationship between the referenced pixel and its neighbors, based on the directions that are
calculated using the first-order derivatives in vertical and horizontal directions. In [23] there are
gathered other methods that are based on the LBP.
MTS (Modified texture spectrum) proposed by Xu et al. [29] can be considered as a simplified
version of LBP, where only a subset of the peripheral pixels (up-left, up, up-right and right) is
considered.
The Binary Gradient Contours (BGC) [6] is a binary 8-tuple proposed by Fernndez et al. The
simple loop form (BGC1) makes a closed path around the central pixel computing a set of eight
binary gradients between pairs of pixels.
Other descriptor called FREAK [2] is a keypoint descriptor inspired by the human visual system
and more precisely the retina. It is faster, usess less memory and more robust than SIFT, SURF and
BRISK. They are thus competitive alternatives to existing descriptors in particular for embedded
applications.
Figure 2 shows some detectors and the relation between them chronologically ordered.
2.3. Matchers
The most widely used method for matching is Nearest Neighbor (NN). Many algorithms follow
this method. One of the most used is the kd-tree [24] which works well with low dimensionality.
For dealing with higher dimensionalities many researchers have proposed diverse methods such
as the Approximate Nearest Neighbor (ANN) by Indyk and Motwani [9] or the Fast Approximate
Nearest Neighbors of Muja and Lowe [19] which is implemented in the well known open source
library FLANN (Fast Library for Approximate Nearest Neighbors).
International Journal of Artificial Intelligence & Applications (IJAIA) Vol.10, No.6, November 2019
3
4. (2011)
LBP
(1995)
SIFT
(1999)
Shape context
(2002)
RIFT
(2005)
MOPS
(2005)
HOG
(2005)
SURF
(2006)
LSS
(2007)
CS-LBP
(2009)
DAISY
(2009)
ELTP
(2010)
BRIEF
(2010)
WLD
(2010)
ORB
(2011)
BRISK
(2011)
LIOP
(2011)
Derivated from LBP
(2011)
MROGH
MRRID
LSS, C
(2012)
FLSS, C
(2012)
LTrP
(2012)
FREAK
(2012)
HSOG
(2014)
GLOH
(2005)
PCA-SIFT
(2004)
LTP
(2007)
Figure 2: Recognition pipeline
3. PROPOSED APPROACH
As we have stated before, the issue we are dealing with is the recognition of industrial parts
for pick-and-placing. The main problem is that the accurate recognition of some kind of parts
are highly dependant on the recognition pipeline used. This is because parts’ characteristics
like texture (presence or absence), forms, colors, brightness; make some detectors or descriptors
work differently. We are thus proposing a systematic approach for selecting the best recognition
pipeline for a given object (Subsection 3.2). We also propose in Subsection 3.3 an expert system
that identifies groups of parts that are recognized similarly to improve the overall accuracy. The
recognition pipeline is explained in Subsection 3.1.
We start defining some notations. An industrial part, or object, is named instance. The images
captured of each part are named views. Given the set of views X, the set of instance labels Y
and the set of recognition pipelines Ψ, the function ωΨ
X,Y (y) returns for each y ∈ Y the best
pipeline ψ∗ ∈ Ψ according to a metric F1 that is later discussed. We call ψ∗∗ to the pipeline that
on average performs better according to the evaluation metric, this is, that maximizes the average
of the scores per instance (2).
ωΨ
X,Y (y) = argmax
ψ∈Ψ
F1
ψ
y (X, Y ) = ψ∗
(1)
ψ∗∗
= argmax
ψ∈Ψ
X
y∈Y
F1
ψ
y (X, Y )
|Y |
(2)
3.1. Recognition Pipeline
A recognition pipeline Ψ is composed of 3 steps: detection, description and matching. Detec-
tors, Γ, localize interesting keypoints in the view (gradient changes, changes in illumination,...).
Descriptors, Φ, are used to represent those keypoints in order to locate them in other views.
Matchers, Ω, find the closest features between views. So, a pipeline ψ is composed by a keypoint
detector γ, a feature descriptor φ and a matcher ω. Figure 3 shows the structure of the recognition
pipeline.
The keypoints detection and description are described previously in the background section. In
the matching, are two groups of features: the ones that form the model (train) and the ones that
International Journal of Artificial Intelligence & Applications (IJAIA) Vol.10, No.6, November 2019
4
5. FLANN
Brute force L2
Brute force Hamming
etc
Harris
SIFT
ORB
etc
Images (views) Keypoints detector Keypoints Features descriptor Features
Harris
SIFT
ORB
etc
Harris
SIFT
ORB
etc
Harris
SIFT
ORB
etc
SIFT
ORB
FREAK
etc
SIFT
ORB
FREAK
etc
SIFT
ORB
FREAK
etc
SIFT
ORB
FREAK
etc
Test
Train
Part
0
Part
1
Part
2
Part
¿?
Matching Result
Part y
Figure 3: Recognition pipeline
need to be recognized (test). Different kind of methods could be used to match features, but,
mainly, distance based techniques are used. This techniques make use of different distances (L2,
hamming,...) to find the closest feature to the one that needs to be labeled. Those two features
(the test feature and the closest to this one) are considered a match. In order to discard ambiguous
features, we use the Lowe’s ratio test [15] to define whether two features are a ”good match”.
Assuming ft is the feature to be recognized, and fl1 and fl2 its two closest features from the
model, then (ft, fl1) is a good match if:
d(ft, fl1
)
d(ft, fl2
)
< r (3)
where d(fA, fB) is the distance (Euclidean or L2 distance: Equation 4, Hamming distance: Equa-
tion 5, where δ is the kronecker delta (Equation 6),...) between features A and B, and r is a
threshold that is used to validate if two features are similarly close to the test feature and discard
it. This threshold is set at 0.8. Now a simple voting system is used for labeling the view. For each
view from the model (train) the number of good matches are counted. The good matches of each
instances are summed and the test view is labeled as the instance with more good matches.
dE(P, Q) =
v
u
u
t
n
X
i=1
(pi − qi)2 (4)
dH(P, Q) =
n
X
i=1
δ(pi, qi) (5)
δ(pi, qi) =
0 if xi = yi
1 if xi 6= yi
(6)
International Journal of Artificial Intelligence & Applications (IJAIA) Vol.10, No.6, November 2019
5
6. Table 1: Example of a confusion matrix for 3 instances.
Actual instance
object 1 object 2 object 3
Predicted
instance
object 1 40 10 0 50
object 2 0 30 25 55
object 3 10 10 25 45
50 50 50 150
3.2. Recognition Evaluation
As we have said, we have the input views X, the instance labels Y and the pipelines Ψ. To
evaluate the pipelines we have to separate the views in train and test. The evaluation method
used for it is Leave-One-Out Cross-Validation (LOOCV) [11]. It consists of |X| iterations, that
for each iteration i, the train dataset is (X − xi) and the test sample is xi. With this separation
train-test we can generate the confusion matrix. Table 1 is an example of a confusion matrix for 3
instances.
As mentioned in the introduction of Section 3, we use the metric F1 value [7] for scoring the
performance of the system. The score is calculated for the tests views from the LOOCV. F1
score, or value, is calculated per each instance (7). This metric is an harmonic mean between the
precision and the recall. The mean of all the F1’s, ¯
F1 (8) is used for calculating ψ∗∗.
F1(y) = 2 ·
precisiony ∗ recally
precisiony + recally
(7)
¯
F1 =
P
y∈Y F1(y)
|Y |
(8)
The precision (Equation 9) is the ratio between the correctly predicted views with label y (tpy)
and all predicted views for that given instance (|ψ(X) = y|). The recall (Equation 10), instead,
is the relation between correctly predicted views with label y (tpy) and all views that should have
that label (|label(X) = y|).
precisiony =
tpy
|ψ(X) = y|
(9)
recally =
tpy
|label(X) = y|
(10)
3.3. Expert system
The function ω gives a lot of information about objects but it needs the instance to return the
best pipeline for that instance which is not available a priori. Indeed, this is what we want to
identify. We use the information that would provide ω to build a hierarchical classification based
in a clustering of similar objects.
Since some parts work better with some particular pipelines because of their shape, color or tex-
ture, we try to take advantage of this and make clusters of objects that are classified similarly well
by each pipeline. For example, two parts that have textures may be better recognized by pipelines
that use descriptors like SIFT or SURF rather than non textured parts. We call these clusters
typologies. This clustering is made using the algorithm K-means [16], that aims to partition the
International Journal of Artificial Intelligence & Applications (IJAIA) Vol.10, No.6, November 2019
6
7. test view ψ**+ k-NN
Typology 2
(t=2)
ψ*
t=2
Instance 6
(y=6)
Instance 4
(y=4)
Instance 5
(y=5)
Typology 3
(t=3)
ψ*
t=3
Instance 8
(y=8)
Instance 7
(y=7)
Typology 1
(t=1)
ψ*
t=1
Instance 1
(y=1)
Instance 2
(y=2)
Instance 3
(y=3)
input
Figure 4: Hierarchical classification
objects into K clusters (where K < |Y |) in which each object belongs to the cluster with the near-
est centroids. The input is a matrix with the instances as rows and for each row the F1 value of
each pipeline. The inputs for this algorithm are for each instance an array of the F1 value obtained
with every pipeline. The election of a good K may highly vary the result since if almost all the
clusters are composed by 1 instance the result would be close to just using ψ∗∗. After obtaining
the K typologies, the ψ∗
T ’s (11) are calculated, i.e., the best pipeline for each typology.
ψ∗
T = argmax
ψ∈Ψ
X
y∈T
F1
ψ
y (X, Y )
|T|
(11)
The first step of the hierarchical recognition is to recognize the typology with the ψ∗∗. Given the
typology t as the typology predicted, the ψ∗
t is used to recognize the instance y of the object. We
call the hierarchical recognition Υ. The Figure 4 shows an scheme of the hierarchical recognition
for clarification.
4. EXPERIMENTS AND RESULTS
Our initial hypothesis is that Υ has a better performance than ψ∗∗. In order to demonstrate this
hypothesis we conducted some experiments. Moreover, we want to know in which way does the
number of parts and the number of views per part affect the result.
The pipelines used (detector, descriptor and matcher) are defined in Subsection 4.1. In Subsection
4.2, we explain the dataset we have created to evaluate the proposed method under the use case
that is the industrial area and the results obtained. In order to compare these results with a well-
known dataset in Subsection 4.3 we present the Caltech dataset [5] and the results obtained.
International Journal of Artificial Intelligence & Applications (IJAIA) Vol.10, No.6, November 2019
7
8. Table 2: Pipelines composition.
Pipeline Detector Descriptor Matcher
ψ0 SIFT SIFT FLANN
ψ1 SURF SURF FLANN
ψ2 ORB ORB
Brute force
Hamming
ψ3 —- LBP FLANN
ψ4 SURF BRIEF
Brute force
Hamming
ψ5 BRISK BRISK
Brute force
Hamming
ψ6 AGAST DAISY FLANN
ψ7 AGAST FREAK
Brute force
Hamming
Table 3: F1’s of the ψ∗∗’s and Υ for each subset of our dataset. p stands for number of parts and
t for number of pictures per part.
@
@
@
t 10 20 30 40 50
p
@
@
@
ψ∗∗ Υ ψ∗∗ Υ ψ∗∗ Υ ψ∗∗ Υ ψ∗∗ Υ
3 0.935 0.862 0.967 0.983 0.989 1 0.992 1 0.993 1
4 0.899 0.854 0.924 0.962 0.932 0.966 0.944 0.801 0.91 0.865
5 0.859 0.843 0.868 0.863 0.883 0.818 0.876 0.901 0.87 0.912
6 0.865 0.967 0.873 0.992 0.891 0.87 0.88 0.88 0.856 0.901
7 0.872 0.9 0.886 0.986 0.894 0.891 0.88 0.876 0.845 0.94
4.1. Pipelines
The pipelines we have selected are shown in Table 2. Many combination could be done but it
is not consistent to match binary descriptors with a L2 distance. The combinations chosen are
compatible and may not be the best combination. Global descriptors, such as LBP, does not need
a detector.
4.2. Our dataset
We select 7 random industrial parts and on a white background we make 50 pictures per part from
different angles randomly. That way, we have a dataset with 350 pictures. In Figure 5 are shown
zoomed in examples of the pictures taken to the parts.
We use subsets of the dataset to evaluate if changing the number of views per instance and the
number of instance vary the performance. This subsets have from 3 to 7 parts and from 10 to 50
views (10 views step). In Table 3 are gathered the results for all the subsets using ψ∗∗ and Υ. The
highest score for each subset is in bold. On average the hierarchical recognition performs better.
The more parts or views per part, the better that performs the hierarchical recognition comparing
with the best pipeline.
Now we focus on the whole dataset. In Figure 6 are shown the F1’s of each instance using each
pipeline for this particular case. The horizontal lines mark the ¯
F1 for that pipeline. The score
we obtain with our method (last column) is higher (0.94) than the best pipeline which is ψ2 that
corresponds to the pipeline that uses ORB (0.845).
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10. Figure 7: 6 random examples of images from the Caltech-101 dataset. The classes are: Face,
Leopard, Motorbike, Airplane, Accordion and Anchor.
Table 5: F1’s of the ψ∗∗’s for each test (Caltech-101). p stands for number of parts and t for
number of pictures per part.
@
@
@
t 10 20 30 40 50
p
@
@
@
ψ∗∗ Υ ψ∗∗ Υ ψ∗∗ Υ ψ∗∗ Υ ψ∗∗ Υ
3 0.967 0.967 0.983 0.983 0.989 0.989 0.992 0.992 0.993 0.993
4 0.975 0.975 0.987 0.987 0.975 0.975 0.969 0.969 0.963 0.97
5 0.98 0.98 0.99 0.99 0.967 0.967 0.96 0.96 0.956 0.956
6 0.883 0.883 0.907 0.907 0.883 0.883 0.848 0.848 0.851 0.936
7 0.776 0.776 0.794 0.831 0.78 0.84 0.768 0.849 0.783 0.843
A truthful evaluation of the time performance of the hierarchical classificator is a bit cumbersome
since it directly depends on the clustering phase and on which are the best pipelines for each
cluster. At least, it needs more time than just using a single pipeline. Given t(ψ) the time need by
the pipeline ψ, the time needed by Υ is approximately t(ψ∗∗) + t(ψ∗
T0 ) where T0 is the typology
guessed by the ψ∗∗. In Table 4 is shown the time in seconds that each pipeline and the Υ need to
recognize a view.
4.3. Caltech-101 dataset
Caltech-101 dataset [5] is a known dataset for object recognition than could be similar to our
dataset. This dataset has been tested like our dataset making subsets of the same characteristics.
Some randomly picked images from the dataset are shown in Figure 7.
The results obtained for the subsets of this datasets are shown in Table 5. Same conclusions are
obtained for this dataset.
4.4. Adding more descriptors
Due to the great performance that global descriptors have, we tried other experiments on the previ-
ous 2 datasets adding new descriptors that had been considered from the beginning. These exper-
iments mix global descriptors and local descriptors. Local descriptors make use of the matching
technique to recognise the objects in the images. Global descriptors, instead, are usually used
International Journal of Artificial Intelligence & Applications (IJAIA) Vol.10, No.6, November 2019
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11. Table 6: F1s per instance and mean using added descriptors for our dataset
0 1 2 3 4 5 6 mean
SIFT 0.962 0.785 0.951 0.455 0.598 0.906 0.598 0.751
SURF 0.857 0.5 0.8 0.196 0.667 0.25 0.5 0.539
ORB 0.6 0.53 0.723 0.715 0.8 0.9 0.8 0.845
BRIEF 0.287 0 0.5 0.287 0 0 0 0.153
BRISK 0.931 0.475 0.912 0.422 0.735 0.726 0.301 0.643
DAISY 0.623 0.675 0.871 0.121 0.75 0.637 0.687 0.623
FREAK 0.823 0.911 0.872 0.738 0.617 0.878 0.927 0.824
LBP 0.687 0.767 0.694 0.432 0.647 0.667 0.635 0.647
LBPU 0.106 0.4 0.5 0.333 0.75 0 0.25 0.334
BGC1 0.72 0.4 0.462 0.182 1 0.333 0.5 0.514
GRAD 0.8 0.667 0.5 0.546 0.8 1 0.889 0.743
GABORGB 0.909 1 0.667 0.8 0.889 0.889 0.8 0.851
LTP 0.667 0.572 0.6 0.285 0.8 0.25 0 0.453
MTS 0.261 0 0.445 0 0.75 0.286 0.364 0.301
TPLBP 0.667 0.667 0.889 0.667 0.857 0.8 0.933 0.783
among machine learning techniques to identify the objects. In this case, we have used a Support
Vector Machine to learn the model that recognise the objects using the global descriptors.
The newly added global descriptors are: LBPU (Uniform LBP), BGC1, Gradded histogram, Ga-
bor Filters, LTP, MTS and TPLBP (Three-Patch LBP). In Figure 6, are shown the F1s obtained
using all the descriptors (the first 8 and the newly added global descriptors) for our dataset. The
hierarchical classificator using all the descriptors achieves a F1 of 0.937. In Figure 7, instead are
the results for the Caltech-101 dataset. The hierarchical classificator with this dataset achieves a
F1 of 0.95.
Adding more descriptors without taking out others does not improve the hierarchical classificator.
Even more, in some experiments done, the results are worse than just using the firstly proposed 8
methods. This is due to the fact that the typologies are chosen by clustering the instances. That
does not provide the best combination of instances, but the ones that behave similarly to all the
descriptors. So the more descriptors the more variability in clustering.
5. CONCLUSION
We proposed a hierarchical recognition method based in clustering similar behaviour by the recog-
nition pipelines. It has been demonstrated that on average works better than just recognizing with
classical feature-based methods achieving high F1 Scores (in the biggest case, 0.94 for our dataset
and 0.843 for the Caltech-101). The performance of the hierarchical method is highly dependant
of the feature-based method used to build it. In order to improve its performance new combi-
nations of descriptor may be proposed. Not just adding and increasing the algorithm bag, but
selecting the best combination of algorithms to obtain the best performance.
As we stated, once we recognize a piece we need its pose to tell the robot where to pick it. This
has been let for future work. The use of local features enables the possibility to estimate objects
pose using methods such as Hough voting schema, RANSAC or PnP.
6. ACKNOWLEDGMENT
This paper has been supported by the project SHERLOCK under the European Unions Horizon
2020 Research Innovation programme, grant agreement No. 820689.
International Journal of Artificial Intelligence & Applications (IJAIA) Vol.10, No.6, November 2019
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12. Table 7: F1s per instance and mean using added descriptors for Caltech-101
0 1 2 3 4 5 6 mean
SIFT 0 0 0 0.5 0.667 0.461 0.2 0.261
SURF 0 0.25 0.428 0.706 0 0 0 0.197
ORB 0.693 0.753 0.723 0.715 0.859 0.944 0.8 0.783
BRIEF 0 0.125 0.286 0.8 0 0.333 0.182 0.246
BRISK 0.167 0 0.25 0.889 0.445 0.222 0.182 0.307
DAISY 0 0 0 0.25 0 0 0.222 0.067
FREAK 0 0.182 0.545 0.909 0 0.461 0.5 0.371
LBP 0.909 1 1 0.833 1 0.667 0.889 0.899
LBPU 1 1 0.889 0.8 0.923 0.933 0.857 0.914
BGC1 0.667 0.923 1 0.75 0.889 0.923 0.857 0.858
GRAD 0.75 0.857 1 0.909 1 0.857 0.667 0.862
GABORGB 1 0.72 1 0.744 1 1 0.85 0.902
LTP 0.727 0.445 1 1 0.889 1 0.286 0.763
MTS 0.857 1 0.8 1 1 1 0.5 0.879
TPLBP 1 1 1 0.667 0.857 0.857 0.667 0.864
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