Bench Marking Higuchi Fractal for CBIR
A. Suhas Rautmare, B. Anjali Bhalchandra
A. Tata Consultancy Services, Mumbai B. Govt. College of Engineering, Aurangabad
Feature Fusion and Classifier Ensemble Technique for Robust Face RecognitionCSCJournals
Face recognition is an important part of the broader biometric security systems research. In the past, researchers have explored either the Feature Space or the Classifier Space at a time to achieve efficient face recognition. In this work, both the Feature Space optimization as well as the Classifier Space optimization have been used to achieve improved results. The efficient technique of Feature Fusion in the Feature Space and Classifier Ensemble technique in the Classifier Space have been used to achieve robust and efficient face recognition. In the Feature Space, the Discrete Wavelet Transform (DWT) and the Histogram of Oriented Gradient (HOG) features have been extracted from face images and these have been used for classification purposes after Feature Fusion using the Principal Component Analysis (PCA). In the Classifier Space, a Classifier Ensemble has been used, utilizing the bagging technique for ensemble training, instead of a single classifier for efficient classification. Proper selections of various parameters of the DWT, HOG features and the Classification Ensemble have been considered to achieve optimum performance. The proposed classification technique has been applied to the AT&T (ORL) and Yale benchmark face recognition databases, and we have achieved excellent results of 99.78% and 97.72% classification accuracy respectively. The proposed Feature Fusion and Classifier Ensemble technique has been subjected to sensitivity analysis and it has been found to be robust under reduced spatial resolution conditions.
This document describes a proposed method to improve image classification accuracy and speed using the bag-of-features model with spatial pooling. The proposed method has two phases: a training phase to create an image feature database, and an evaluation phase to classify new images. In the evaluation phase, spatial pooling is applied to input image features before classification with KNN. Variance-based feature selection is also used to reduce features before KNN classification. Experimental results show the proposed method improves classification accuracy up to 5% and reduces classification time by up to 50% compared to the standard bag-of-features model.
Learning in content based image retrieval a revieweSAT Journals
Abstract
Relevance feedback in Content Based Image Retrieval is an interactive process where the user provides feedback on the systemretrieved
images to bridge the gap between user semantics at high level and machine extracted low level features of images.
RF exploits Machine Learning and Pattern Recognition techniques for Short Term Learning and Long Term Learning to provide
improved performance in retrieval. Intra query and across query learning have received enormous attention over the past
decade. This paper first categorizes the various learning techniques and discusses the intuition behind each of these techniques.
State-of-art learning techniques ranging from Feature Relevance learning to manifold learning in STL and Latent Semantic
Analysis used in text processing to recent kernel semantic learning in LTL are discussed.
Keywords: Relevance Feedback, Short Term Learning, Long Term Learning, Sematic Gap, High Level Features
IRJET- An Improvised Multi Focus Image Fusion Algorithm through QuadtreeIRJET Journal
The document proposes a new quadtree-based algorithm for multi-focus image fusion. The algorithm divides the input images into 4 equal blocks using a quadtree structure. It then further divides each block into smaller blocks and detects the focused regions in each block using a focus measure and weighted values. The small blocks are then fused using a modified Laplacian mechanism. The fused image is evaluated using SSIM and ESSIM values, which indicate the proposed algorithm performs better fusion than previous methods.
This document summarizes an article from the International Journal of Computer Engineering and Technology (IJCET) that proposes different indexing models for multiple queries called Indexing for Multiple Queries (IMQ). It first proposes using k-Nearest Neighbor (kNN) indexing for queries. It then describes an online method that creates an indexing model for each query based on similar labeled queries. It also describes two offline methods that pre-create indexing models to improve efficiency. Experimental results showed the online and offline kNN methods performed better than a baseline single indexing model method.
Towards Semantic Clustering – A Brief OverviewCSCJournals
Image clustering is an important technology which helps users to get hold of the large amount of online visual information, especially after the rapid growth of the Web. This paper focuses on image clustering methods and their application in image collection or online image repository. Current progress of image clustering related to image retrieval and image annotation are summarized and some open problems are discussed. Related works are summarized based on the problems addressed, which are image segmentation, compact representation of image set, search space reduction, and semantic gap. Issues are also identified in current progress and semantic clustering is conjectured to be the potential trend. Our framework of semantic clustering as well as the main abstraction levels involved is briefly discussed.
This document discusses guided image filtering. It introduces the guided filter, which performs edge-preserving smoothing while maintaining the gradient of a guidance image. The guided filter works by assuming a local linear model between the guidance image and filtering output within a window, and solving a cost function to determine the filter coefficients. It can perform edge-preserving smoothing and gradient-preserving filtering in linear time complexity.
Data Mining Based Skin Pixel Detection Applied On Human Images: A Study PaperIJERA Editor
Skin segmentation is the process of the identifying the skin pixels in a image in a particular color model and dividing the images into skin and non-skin pixels. It is the process of find the particular skin of the image or video in a color model. Finding the regions of the images in human images to say these pixel regions are part of the image or videos is typically a preprocessing step in skin detection in computer vision, face detection or multi-view face detection. Skin pixel detection model converts the images into appropriate format in a color space and then classification process is being used for labeling of the skin and non-skin pixels. A skin classifier identifies the boundary of the skin image in a skin color model based on the training dataset. Here in this paper, we present the survey of the skin pixel segmentation using the learning algorithms.
Feature Fusion and Classifier Ensemble Technique for Robust Face RecognitionCSCJournals
Face recognition is an important part of the broader biometric security systems research. In the past, researchers have explored either the Feature Space or the Classifier Space at a time to achieve efficient face recognition. In this work, both the Feature Space optimization as well as the Classifier Space optimization have been used to achieve improved results. The efficient technique of Feature Fusion in the Feature Space and Classifier Ensemble technique in the Classifier Space have been used to achieve robust and efficient face recognition. In the Feature Space, the Discrete Wavelet Transform (DWT) and the Histogram of Oriented Gradient (HOG) features have been extracted from face images and these have been used for classification purposes after Feature Fusion using the Principal Component Analysis (PCA). In the Classifier Space, a Classifier Ensemble has been used, utilizing the bagging technique for ensemble training, instead of a single classifier for efficient classification. Proper selections of various parameters of the DWT, HOG features and the Classification Ensemble have been considered to achieve optimum performance. The proposed classification technique has been applied to the AT&T (ORL) and Yale benchmark face recognition databases, and we have achieved excellent results of 99.78% and 97.72% classification accuracy respectively. The proposed Feature Fusion and Classifier Ensemble technique has been subjected to sensitivity analysis and it has been found to be robust under reduced spatial resolution conditions.
This document describes a proposed method to improve image classification accuracy and speed using the bag-of-features model with spatial pooling. The proposed method has two phases: a training phase to create an image feature database, and an evaluation phase to classify new images. In the evaluation phase, spatial pooling is applied to input image features before classification with KNN. Variance-based feature selection is also used to reduce features before KNN classification. Experimental results show the proposed method improves classification accuracy up to 5% and reduces classification time by up to 50% compared to the standard bag-of-features model.
Learning in content based image retrieval a revieweSAT Journals
Abstract
Relevance feedback in Content Based Image Retrieval is an interactive process where the user provides feedback on the systemretrieved
images to bridge the gap between user semantics at high level and machine extracted low level features of images.
RF exploits Machine Learning and Pattern Recognition techniques for Short Term Learning and Long Term Learning to provide
improved performance in retrieval. Intra query and across query learning have received enormous attention over the past
decade. This paper first categorizes the various learning techniques and discusses the intuition behind each of these techniques.
State-of-art learning techniques ranging from Feature Relevance learning to manifold learning in STL and Latent Semantic
Analysis used in text processing to recent kernel semantic learning in LTL are discussed.
Keywords: Relevance Feedback, Short Term Learning, Long Term Learning, Sematic Gap, High Level Features
IRJET- An Improvised Multi Focus Image Fusion Algorithm through QuadtreeIRJET Journal
The document proposes a new quadtree-based algorithm for multi-focus image fusion. The algorithm divides the input images into 4 equal blocks using a quadtree structure. It then further divides each block into smaller blocks and detects the focused regions in each block using a focus measure and weighted values. The small blocks are then fused using a modified Laplacian mechanism. The fused image is evaluated using SSIM and ESSIM values, which indicate the proposed algorithm performs better fusion than previous methods.
This document summarizes an article from the International Journal of Computer Engineering and Technology (IJCET) that proposes different indexing models for multiple queries called Indexing for Multiple Queries (IMQ). It first proposes using k-Nearest Neighbor (kNN) indexing for queries. It then describes an online method that creates an indexing model for each query based on similar labeled queries. It also describes two offline methods that pre-create indexing models to improve efficiency. Experimental results showed the online and offline kNN methods performed better than a baseline single indexing model method.
Towards Semantic Clustering – A Brief OverviewCSCJournals
Image clustering is an important technology which helps users to get hold of the large amount of online visual information, especially after the rapid growth of the Web. This paper focuses on image clustering methods and their application in image collection or online image repository. Current progress of image clustering related to image retrieval and image annotation are summarized and some open problems are discussed. Related works are summarized based on the problems addressed, which are image segmentation, compact representation of image set, search space reduction, and semantic gap. Issues are also identified in current progress and semantic clustering is conjectured to be the potential trend. Our framework of semantic clustering as well as the main abstraction levels involved is briefly discussed.
This document discusses guided image filtering. It introduces the guided filter, which performs edge-preserving smoothing while maintaining the gradient of a guidance image. The guided filter works by assuming a local linear model between the guidance image and filtering output within a window, and solving a cost function to determine the filter coefficients. It can perform edge-preserving smoothing and gradient-preserving filtering in linear time complexity.
Data Mining Based Skin Pixel Detection Applied On Human Images: A Study PaperIJERA Editor
Skin segmentation is the process of the identifying the skin pixels in a image in a particular color model and dividing the images into skin and non-skin pixels. It is the process of find the particular skin of the image or video in a color model. Finding the regions of the images in human images to say these pixel regions are part of the image or videos is typically a preprocessing step in skin detection in computer vision, face detection or multi-view face detection. Skin pixel detection model converts the images into appropriate format in a color space and then classification process is being used for labeling of the skin and non-skin pixels. A skin classifier identifies the boundary of the skin image in a skin color model based on the training dataset. Here in this paper, we present the survey of the skin pixel segmentation using the learning algorithms.
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONgerogepatton
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the
convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
Multi Label Spatial Semi Supervised Classification using Spatial Associative ...cscpconf
Multi-label spatial classification based on association rules with multi objective genetic
algorithms (MOGA) enriched by semi supervised learning is proposed in this paper. It is to deal
with multiple class labels problem. In this paper we adapt problem transformation for the multi
label classification. We use hybrid evolutionary algorithm for the optimization in the generation
of spatial association rules, which addresses single label. MOGA is used to combine the single
labels into multi labels with the conflicting objectives predictive accuracy and
comprehensibility. Semi supervised learning is done through the process of rule cover
clustering. Finally associative classifier is built with a sorting mechanism. The algorithm is
simulated and the results are compared with MOGA based associative classifier, which out
performs the existing
A Novel Approach of Fuzzy Based Semi-Automatic Annotation for Similar Facial ...ijsrd.com
This paper proposes a semi-automatic approach for annotating similar facial images that are often weakly labeled with duplicate, noisy, or incomplete names. It uses an unsupervised label refinement (ULR) algorithm with fuzzy clustering to improve the labels. The ULR algorithm refines the labels through multiple iterations using machine learning techniques. It also uses a parallel computation framework to solve very large problems efficiently. Evaluation on a dataset with introduced noise shows the proposed optimized fuzzy ULR approach outperforms other ULR algorithms in refining the labels.
Collaborative semantic annotation of images ontology based modelsipij
The document presents a collaborative semantic annotation approach for images based on ontology. It proposes using ontologies to extract multiple meanings from images as different annotators may interpret images differently. Annotators would propose keywords from an ontology and dictionary to describe image contents. The system would then determine the most representative meaning by measuring similarities between meanings based on keyword frequencies and rankings. This emergent semantic approach aims to capture different interpretations of images for more accurate semantic annotation compared to single-annotator methods.
Q-Learnıng Based Real Tıme Path Plannıng for Mobıle Robotsijtsrd
Decision making and movement control are used for mobile robots to perform the given tasks. This study presents a real time application in which the robotic system estimates the shortest way from robot's current location to target point via Q learning algorithm and makes decision to go the target point on the estimated path by using movement control. Q Learning algorithm is known as a Reinforcement Learning RL algorithm. In this study, it is used as a core algorithm for estimation of the path that is optimum way for mobile robot in an environment. The environment is viewed by a camera. This study includes three phases. Firstly, the map and the locations of all objects including a mobile robot, obstacles and target point in the environment are determined by using image processing. Secondly, Q Learning algorithm is applied for the problem of the estimation of the shortest way from the current location of the robot to target point. Finally, a mobile robot with three omni wheels was developed. Experiments were carried out using this robot. Two different experiments are performed in experimental environment. The results obtained are shared at the end of the paper. Halil Cetin | Akif Durdu | M. Fatih Aslan | M. Mustafa Kelek "Q-Learnıng Based Real Tıme Path Plannıng for Mobıle Robots" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29625.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/29625/q-learn%C4%B1ng-based-real-t%C4%B1me-path-plann%C4%B1ng-for-mob%C4%B1le-robots/halil-cetin
Data entry is a time consuming and erroneous procedure in its nature. In addition, validity
check of submitted information is not easier than retyping it. In a mega-corporation like Kanoon
Farhangi Amoozesh, there are almost no way to control the authenticity of students' educational
background. By the virtue of fast computer architectures, optical character recognition, a.k.a.
OCR, systems have become viable. Unfortunately, general-purpose OCR systems like Google's
Tesseract are not handful because they don't have any a-priori information about what they are
reading. In this paper the authors have taken a in-depth look on what has done in the field of
OCR in the last 60 years. Then, a custom-made system adapted to the problem is presented
which is way more accurate than general purpose OCRs. The developed system reads more than
60 digits per second. As shown in the Results section, the accuracy of the devised method is
reasonable enough to be exposed in public use.
IRJET- The Machine Learning: The method of Artificial IntelligenceIRJET Journal
This document discusses machine learning and its role in artificial intelligence. It begins with an abstract that explains machine learning is widely used in artificial intelligence to enable systems to learn and make decisions without being explicitly programmed. It then provides an introduction to machine learning, explaining it allows software to learn from data and improve predictions without being explicitly programmed. The document also discusses related work from other researchers on topics like supervised learning, unsupervised learning, and evaluating different machine learning methods. It describes problems that can occur during the learning process like bias, noise, and pattern recognition. Finally, it provides algorithms for hierarchical clustering and k-means clustering as examples of unsupervised learning methods.
Facial image retrieval on semantic features using adaptive mean genetic algor...TELKOMNIKA JOURNAL
The emergence of larger databases has made image retrieval techniques an essential component and has led to the development of more efficient image retrieval systems. Retrieval can either be content or text-based. In this paper, the focus is on the content-based image retrieval from the FGNET database. Input query images are subjected to several processing techniques in the database before computing the squared Euclidean distance (SED) between them. The images with the shortest Euclidean distance are considered as a match and are retrieved. The processing techniques involve the application of the median modified Weiner filter (MMWF), extraction of the low-level features using histogram-oriented gradients (HOG), discrete wavelet transform (DWT), GIST, and Local tetra pattern (LTrP). Finally, the features are selected using Adaptive Mean Genetic Algorithm (AMGA). In this study, the average PSNR value obtained after applying the Wiener filter was 45.29. The performance of the AMGA was evaluated based on its precision, F-measure, and recall, and the obtained average values were respectively 0.75, 0.692, and 0.66. The performance matrix of the AMGA was compared to those of particle swarm optimization algorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its efficiency.
Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Predictionijtsrd
Data mining techniques play an important role in data analysis. For the construction of a classification model which could predict performance of students, particularly for engineering branches, a decision tree algorithm associated with the data mining techniques have been used in the research. A number of factors may affect the performance of students. Data mining technology which can related to this student grade well and we also used classification algorithms prediction. In this paper, we used educational data mining to predict students final grade based on their performance. We proposed student data classification using ID3 Iterative Dichotomiser 3 Decision Tree Algorithm Khin Khin Lay | San San Nwe "Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26545.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26545/using-id3-decision-tree-algorithm-to-the-student-grade-analysis-and-prediction/khin-khin-lay
Mansoor K is seeking a position that allows him to utilize his knowledge and skills in process control and instrumentation engineering. He has an M.Tech in this field from Hindustan University with a CGPA of 8.71. His areas of interest include circuit theory, digital logic circuits, and power electronics. He has work experience with active noise cancellation, programmable logic controllers, and line-following robots. Mansoor also participated in workshops, in-plant trainings, and engineering events and competitions. He is proficient with Matlab, Proteus, embedded C, and basic C++.
A Fast and Accurate Palmprint Identification System based on Consistency Orie...IJTET Journal
Abstract — A palmprint identification system is a relatively most promising physiological biometric approach to identify the person. The numbers of palmprint recognition based biometric system have been successfully applied for real world access to control applications. A typical palmprint identification system identifies a query palmprint and matching it with the template stored in the database and comparing the similarity score with a pre-defined threshold. The Consistency Orientation Pattern (COP) hashing method is implemented in this work to enforce the fast search and to obtain the accurate result. Orientation pattern (OP) is defined as a collection of orientation features at arbitrary positions. The principal palm line is a kind of evident and stable features in palmprint images, and the orientation features in this region are expected to be more consistent than others. Using the orientation and response features extracted by steerable filter and gives an analysis on the consistency of orientation features, and then introduces a method to construct COP using the consistent features. Those features can be used as the indexes to the target template. Because the COP is very stable across the samples of the same subject, the COP hashing method can find the target template quickly. This method can lead to early termination of the searching process.
Google Site Simulations and DCP activitiesjghopwood
This Google Site contains four physics simulations from the University of Colorado that students can use for data collection and processing activities. Each simulation has a learning task where students collect data to analyze relationships between variables. Students are responsible for independently collecting raw data, processing and organizing it in spreadsheets, and presenting their results. The activities aim to address the IB criteria for the Data Collection and Processing internal assessment. Examples of simulations covered force and motion, pendulums, blackbody radiation, and bending light.
IRJET- Class Attendance using Face Detection and Recognition with OPENCVIRJET Journal
This document describes a system to automate class attendance using face detection and recognition with OpenCV. The system uses the Viola-Jones algorithm for face detection and linear binary pattern histograms for face recognition. Detected faces are converted to grayscale images for better accuracy. The system trains on positive images of faces and negative images without faces to build a classifier. It then detects faces in class and recognizes students by matching features to a stored database, updating attendance and notifying administrators. The proposed system aims to reduce time spent on manual attendance and increase accuracy by automating the process through computer vision techniques.
Design of an Effective Method for Image RetrievalAM Publications
This document summarizes a research paper that proposes an effective method for image retrieval using relevance feedback with self-organizing maps (SOMs). The key points are:
1) Relevance feedback is used to improve image retrieval performance by iteratively refining queries based on user feedback on retrieved images. With SOMs, relevance feedback scores are spread to neighboring image clusters.
2) The method represents images with features and maps them to units in a SOM grid. Positive and negative feedback is scored on the grid. Scores are spread to neighboring units based on their distance in feature space.
3) Images are retrieved by selecting from high-scoring areas of the SOM grid, using either a depth-
Monu Agarwal is a data analyst based in Chennai, India with experience in marketing analytics and data visualization. He has a B.Tech in Electrical and Electronics Engineering from Birla Institute of Technology Mesra. His work experience includes projects for Choice Hotels International involving data extraction, exception control views, and device-platform performance analysis. For another project, he performed customer segmentation, analyzed key performance indicators for events, and conducted cross-device tactic purchase analysis. He is proficient in technologies like HP Vertica, Tableau, Python, and R. He has strong problem-solving, leadership, and communication skills as shown through his technical and extracurricular achievements and positions of responsibility in college.
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.
Color Image Segmentation Technique Using “Natural Grouping” of PixelsCSCJournals
This paper focuses on the problem Image Segmentation which aims at sub dividing a given image into its constituent objects. Here an unsupervised method for color image segmentation is proposed where we first perform a Minimum Spanning Tree (MST) based “natural grouping” of the image pixels to find out the clusters of the pixels having RGB values within a certain range present in the image. Then the pixels nearest to the centers of those clusters are found out and marked as the seeds. They are then used for region growing based image segmentation purpose. After that a region merging based segmentation method having a suitable threshold is performed to eliminate the effect of over segmentation that may still persist after the region growing method. This proposed method is unsupervised as it does not require any prior information about the number of regions present in a given image. The experimental results show that the proposed method can find homogeneous regions present in a given image efficiently.
This document summarizes a research paper that presents a hybrid approach for detecting and localizing color text in natural scene images. The approach uses both region-based and connected component-based methods. In the preprocessing stage, a text region detector is used to detect text regions and generate candidate text components. A conditional random field model combines unary component properties and binary contextual relationships to filter non-text components. Finally, neighboring text components are grouped into text lines or words using a learning-based energy minimization method. The paper is evaluated on a natural scene image dataset and shows improvements over existing methods.
Measuring Sub Pixel Erratic Shift in Egyptsat-1 Aliased Images: proposed method
1M.A. Fkirin, 1S.M. Badway, 2A.K. Helmy, 2S.A. Mohamed
1Department of Industrial Electronic Engineering and Control, Faculty of Electronic Engineering,
Menoufia University, Menoufia, Egypt.
2Division of Data Reception Analysis and Receiving Station Affairs, National Authority for Remote Sensing and Space Sciences, Cairo, Egypt.
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONgerogepatton
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the
convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
Multi Label Spatial Semi Supervised Classification using Spatial Associative ...cscpconf
Multi-label spatial classification based on association rules with multi objective genetic
algorithms (MOGA) enriched by semi supervised learning is proposed in this paper. It is to deal
with multiple class labels problem. In this paper we adapt problem transformation for the multi
label classification. We use hybrid evolutionary algorithm for the optimization in the generation
of spatial association rules, which addresses single label. MOGA is used to combine the single
labels into multi labels with the conflicting objectives predictive accuracy and
comprehensibility. Semi supervised learning is done through the process of rule cover
clustering. Finally associative classifier is built with a sorting mechanism. The algorithm is
simulated and the results are compared with MOGA based associative classifier, which out
performs the existing
A Novel Approach of Fuzzy Based Semi-Automatic Annotation for Similar Facial ...ijsrd.com
This paper proposes a semi-automatic approach for annotating similar facial images that are often weakly labeled with duplicate, noisy, or incomplete names. It uses an unsupervised label refinement (ULR) algorithm with fuzzy clustering to improve the labels. The ULR algorithm refines the labels through multiple iterations using machine learning techniques. It also uses a parallel computation framework to solve very large problems efficiently. Evaluation on a dataset with introduced noise shows the proposed optimized fuzzy ULR approach outperforms other ULR algorithms in refining the labels.
Collaborative semantic annotation of images ontology based modelsipij
The document presents a collaborative semantic annotation approach for images based on ontology. It proposes using ontologies to extract multiple meanings from images as different annotators may interpret images differently. Annotators would propose keywords from an ontology and dictionary to describe image contents. The system would then determine the most representative meaning by measuring similarities between meanings based on keyword frequencies and rankings. This emergent semantic approach aims to capture different interpretations of images for more accurate semantic annotation compared to single-annotator methods.
Q-Learnıng Based Real Tıme Path Plannıng for Mobıle Robotsijtsrd
Decision making and movement control are used for mobile robots to perform the given tasks. This study presents a real time application in which the robotic system estimates the shortest way from robot's current location to target point via Q learning algorithm and makes decision to go the target point on the estimated path by using movement control. Q Learning algorithm is known as a Reinforcement Learning RL algorithm. In this study, it is used as a core algorithm for estimation of the path that is optimum way for mobile robot in an environment. The environment is viewed by a camera. This study includes three phases. Firstly, the map and the locations of all objects including a mobile robot, obstacles and target point in the environment are determined by using image processing. Secondly, Q Learning algorithm is applied for the problem of the estimation of the shortest way from the current location of the robot to target point. Finally, a mobile robot with three omni wheels was developed. Experiments were carried out using this robot. Two different experiments are performed in experimental environment. The results obtained are shared at the end of the paper. Halil Cetin | Akif Durdu | M. Fatih Aslan | M. Mustafa Kelek "Q-Learnıng Based Real Tıme Path Plannıng for Mobıle Robots" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29625.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/29625/q-learn%C4%B1ng-based-real-t%C4%B1me-path-plann%C4%B1ng-for-mob%C4%B1le-robots/halil-cetin
Data entry is a time consuming and erroneous procedure in its nature. In addition, validity
check of submitted information is not easier than retyping it. In a mega-corporation like Kanoon
Farhangi Amoozesh, there are almost no way to control the authenticity of students' educational
background. By the virtue of fast computer architectures, optical character recognition, a.k.a.
OCR, systems have become viable. Unfortunately, general-purpose OCR systems like Google's
Tesseract are not handful because they don't have any a-priori information about what they are
reading. In this paper the authors have taken a in-depth look on what has done in the field of
OCR in the last 60 years. Then, a custom-made system adapted to the problem is presented
which is way more accurate than general purpose OCRs. The developed system reads more than
60 digits per second. As shown in the Results section, the accuracy of the devised method is
reasonable enough to be exposed in public use.
IRJET- The Machine Learning: The method of Artificial IntelligenceIRJET Journal
This document discusses machine learning and its role in artificial intelligence. It begins with an abstract that explains machine learning is widely used in artificial intelligence to enable systems to learn and make decisions without being explicitly programmed. It then provides an introduction to machine learning, explaining it allows software to learn from data and improve predictions without being explicitly programmed. The document also discusses related work from other researchers on topics like supervised learning, unsupervised learning, and evaluating different machine learning methods. It describes problems that can occur during the learning process like bias, noise, and pattern recognition. Finally, it provides algorithms for hierarchical clustering and k-means clustering as examples of unsupervised learning methods.
Facial image retrieval on semantic features using adaptive mean genetic algor...TELKOMNIKA JOURNAL
The emergence of larger databases has made image retrieval techniques an essential component and has led to the development of more efficient image retrieval systems. Retrieval can either be content or text-based. In this paper, the focus is on the content-based image retrieval from the FGNET database. Input query images are subjected to several processing techniques in the database before computing the squared Euclidean distance (SED) between them. The images with the shortest Euclidean distance are considered as a match and are retrieved. The processing techniques involve the application of the median modified Weiner filter (MMWF), extraction of the low-level features using histogram-oriented gradients (HOG), discrete wavelet transform (DWT), GIST, and Local tetra pattern (LTrP). Finally, the features are selected using Adaptive Mean Genetic Algorithm (AMGA). In this study, the average PSNR value obtained after applying the Wiener filter was 45.29. The performance of the AMGA was evaluated based on its precision, F-measure, and recall, and the obtained average values were respectively 0.75, 0.692, and 0.66. The performance matrix of the AMGA was compared to those of particle swarm optimization algorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its efficiency.
Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Predictionijtsrd
Data mining techniques play an important role in data analysis. For the construction of a classification model which could predict performance of students, particularly for engineering branches, a decision tree algorithm associated with the data mining techniques have been used in the research. A number of factors may affect the performance of students. Data mining technology which can related to this student grade well and we also used classification algorithms prediction. In this paper, we used educational data mining to predict students final grade based on their performance. We proposed student data classification using ID3 Iterative Dichotomiser 3 Decision Tree Algorithm Khin Khin Lay | San San Nwe "Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26545.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26545/using-id3-decision-tree-algorithm-to-the-student-grade-analysis-and-prediction/khin-khin-lay
Mansoor K is seeking a position that allows him to utilize his knowledge and skills in process control and instrumentation engineering. He has an M.Tech in this field from Hindustan University with a CGPA of 8.71. His areas of interest include circuit theory, digital logic circuits, and power electronics. He has work experience with active noise cancellation, programmable logic controllers, and line-following robots. Mansoor also participated in workshops, in-plant trainings, and engineering events and competitions. He is proficient with Matlab, Proteus, embedded C, and basic C++.
A Fast and Accurate Palmprint Identification System based on Consistency Orie...IJTET Journal
Abstract — A palmprint identification system is a relatively most promising physiological biometric approach to identify the person. The numbers of palmprint recognition based biometric system have been successfully applied for real world access to control applications. A typical palmprint identification system identifies a query palmprint and matching it with the template stored in the database and comparing the similarity score with a pre-defined threshold. The Consistency Orientation Pattern (COP) hashing method is implemented in this work to enforce the fast search and to obtain the accurate result. Orientation pattern (OP) is defined as a collection of orientation features at arbitrary positions. The principal palm line is a kind of evident and stable features in palmprint images, and the orientation features in this region are expected to be more consistent than others. Using the orientation and response features extracted by steerable filter and gives an analysis on the consistency of orientation features, and then introduces a method to construct COP using the consistent features. Those features can be used as the indexes to the target template. Because the COP is very stable across the samples of the same subject, the COP hashing method can find the target template quickly. This method can lead to early termination of the searching process.
Google Site Simulations and DCP activitiesjghopwood
This Google Site contains four physics simulations from the University of Colorado that students can use for data collection and processing activities. Each simulation has a learning task where students collect data to analyze relationships between variables. Students are responsible for independently collecting raw data, processing and organizing it in spreadsheets, and presenting their results. The activities aim to address the IB criteria for the Data Collection and Processing internal assessment. Examples of simulations covered force and motion, pendulums, blackbody radiation, and bending light.
IRJET- Class Attendance using Face Detection and Recognition with OPENCVIRJET Journal
This document describes a system to automate class attendance using face detection and recognition with OpenCV. The system uses the Viola-Jones algorithm for face detection and linear binary pattern histograms for face recognition. Detected faces are converted to grayscale images for better accuracy. The system trains on positive images of faces and negative images without faces to build a classifier. It then detects faces in class and recognizes students by matching features to a stored database, updating attendance and notifying administrators. The proposed system aims to reduce time spent on manual attendance and increase accuracy by automating the process through computer vision techniques.
Design of an Effective Method for Image RetrievalAM Publications
This document summarizes a research paper that proposes an effective method for image retrieval using relevance feedback with self-organizing maps (SOMs). The key points are:
1) Relevance feedback is used to improve image retrieval performance by iteratively refining queries based on user feedback on retrieved images. With SOMs, relevance feedback scores are spread to neighboring image clusters.
2) The method represents images with features and maps them to units in a SOM grid. Positive and negative feedback is scored on the grid. Scores are spread to neighboring units based on their distance in feature space.
3) Images are retrieved by selecting from high-scoring areas of the SOM grid, using either a depth-
Monu Agarwal is a data analyst based in Chennai, India with experience in marketing analytics and data visualization. He has a B.Tech in Electrical and Electronics Engineering from Birla Institute of Technology Mesra. His work experience includes projects for Choice Hotels International involving data extraction, exception control views, and device-platform performance analysis. For another project, he performed customer segmentation, analyzed key performance indicators for events, and conducted cross-device tactic purchase analysis. He is proficient in technologies like HP Vertica, Tableau, Python, and R. He has strong problem-solving, leadership, and communication skills as shown through his technical and extracurricular achievements and positions of responsibility in college.
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.
Color Image Segmentation Technique Using “Natural Grouping” of PixelsCSCJournals
This paper focuses on the problem Image Segmentation which aims at sub dividing a given image into its constituent objects. Here an unsupervised method for color image segmentation is proposed where we first perform a Minimum Spanning Tree (MST) based “natural grouping” of the image pixels to find out the clusters of the pixels having RGB values within a certain range present in the image. Then the pixels nearest to the centers of those clusters are found out and marked as the seeds. They are then used for region growing based image segmentation purpose. After that a region merging based segmentation method having a suitable threshold is performed to eliminate the effect of over segmentation that may still persist after the region growing method. This proposed method is unsupervised as it does not require any prior information about the number of regions present in a given image. The experimental results show that the proposed method can find homogeneous regions present in a given image efficiently.
This document summarizes a research paper that presents a hybrid approach for detecting and localizing color text in natural scene images. The approach uses both region-based and connected component-based methods. In the preprocessing stage, a text region detector is used to detect text regions and generate candidate text components. A conditional random field model combines unary component properties and binary contextual relationships to filter non-text components. Finally, neighboring text components are grouped into text lines or words using a learning-based energy minimization method. The paper is evaluated on a natural scene image dataset and shows improvements over existing methods.
Measuring Sub Pixel Erratic Shift in Egyptsat-1 Aliased Images: proposed method
1M.A. Fkirin, 1S.M. Badway, 2A.K. Helmy, 2S.A. Mohamed
1Department of Industrial Electronic Engineering and Control, Faculty of Electronic Engineering,
Menoufia University, Menoufia, Egypt.
2Division of Data Reception Analysis and Receiving Station Affairs, National Authority for Remote Sensing and Space Sciences, Cairo, Egypt.
Improved Iris Verification System
Basma M.Almezgagi, M. A. Wahby Shalaby, Hesham N. Elmahdy Faculty of Computers and Information, Cairo University, Egypt.
The document reviews concepts related to random variables and random processes. It defines key terms such as:
- Discrete and continuous random variables and their probability distribution and density functions.
- Joint, marginal, and conditional density functions which describe the relationships between multiple random variables.
- Independent and orthogonal random variables, and concepts like inner products, that are used to analyze relationships between random variables.
- Various types of convergence for sequences of random variables such as almost sure, mean square, and in probability which are important for analyzing random processes over time.
The review covers critical foundational concepts for understanding and working with random variables and stochastic processes.
This document summarizes the photographer's experience taking photos to learn the different types of photography, including closed form, open form, scale, linear perspective, and atmospheric perspective. While some types were difficult to distinguish like scale and linear perspective photos, practicing taking these photos helped the photographer better understand digital photography and tell the differences between the various techniques.
1) The document outlines the schedule and program for an inaugural function and free complimentary dinner for delegates from outside Lahore on December 25, 2010.
2) The day-long program includes sessions on family medicine and psychiatry, medicine and medical specialties, surgery and allied topics, and miscellaneous topics like diabetes management.
3) It also provides information on hotel accommodations and rates for attendees at hotels like Pearl Continental, Crown Plaza, Heaven, Symons Tower, and Executives Inn.
The document discusses wind energy modeling at the Zafarana Wind Farm in Egypt. It provides background on the wind farm and outlines the methodology used. Wind speed and energy production data was collected hourly and monthly from the wind farm over one year. Statistical modeling using the Weibull distribution was then applied to the wind speed and energy data to characterize the wind resource at the site.
This document provides an overview of an ECG rhythm interpretation module, including its objectives to recognize normal sinus rhythm and common arrhythmias, and identify myocardial infarctions on ECGs. It describes the conduction system of the heart and how it relates to ECG readings, defining key components of the PQRST waveform and normal pacemakers. It also outlines how ECG paper is organized with measurements of time and voltage.
Custom, in depth 5 day PHP course I put together in 2014. I'm available to deliver this training in person at your offices - contact me at rich@quicloud.com for rate quotes.
The document summarizes research on implementing an adaptive backstepping control scheme for an uncertain brushless DC motor. The goals are to design a speed controller that achieves asymptotic tracking without full knowledge of motor parameters, and to estimate unknown parameters online. An adaptive backstepping controller is designed using Lyapunov stability analysis. Simulation results show the controller provides robustness and stability in the presence of parameter uncertainties.
The document provides an overview of IndexedDB, a database API for storing structured data locally in the browser. It discusses how to initialize a database and object stores, add, retrieve, and delete data, use indexes to query data more efficiently, and iterate over data using cursors. It also covers key concepts like key paths, key generators, key ranges, and compatibility across browsers. The document aims to explain the basic functionality and usage of IndexedDB.
1) The narrator describes watching the moon landing with their family on July 20, 1969. They had spread blankets outside to watch the astronauts' first steps on the moon.
2) When astronaut Neil Armstrong said his famous words, "That's one small step for man, one giant leap for mankind," the narrator felt a sense of shared wonder with the hundreds of millions of people around the world who were also watching.
3) The narrator's grandfather, who was initially skeptical of the space program, tells them "Great days. An astronaut in the family. Who'd have thought?" encouraging the child to keep dreaming big.
Virtualization allows a single computer to run multiple virtual machines simultaneously. This allows developers to easily create and restore test environments. It also enables demonstrators to maintain separate demo environments. Virtual machine snapshots can be easily saved and shared between computers, benefiting developers, demonstrators, and home users. However, virtualization performance declines as more virtual machines are run simultaneously on a single computer.
This document provides an overview of market-based cash balance plans, including:
- A brief history of interest credit options for cash balance plans under different regulations and notices.
- Examples of how cash balance accounts accumulate pay credits and interest credits over time.
- Seven key issues that can arise for market-based cash balance plans, such as investment risk, nondiscrimination testing, lump sum restrictions, and administrative challenges. Suggested approaches are provided to help address these issues.
- Contact information for the actuarial consulting firm that authored the document.
American Bureau of Shipping engaged Partners Consulting to deploy a single sign-on solution for its customer-facing applications. Partners implemented Oracle Access Manager for centralized authentication, authorizing users through WebLogic Security Provider Interface. They migrated user data to Oracle Internet Directory and configured Oracle Access Manager policies and WebGates to provide single sign-on for over 20 portal applications while integrating with the existing WebLogic security framework.
Samsung Gulf Electronics held a press conference to launch their 2011 Samsung SMART TV lineup in Dubai Mall. 62 media members attended the event, which included an overview presentation and 8 one-on-one interviews. The subsequent TV launch event at Dubai Mall from April 6-9 allowed consumers to interact with the new smart TV products. Press coverage of the launch announcement generated 32 articles with over 21 million impressions, valued at over $76,000 in advertising equivalency. The success was shared internally through Samsung's global communication tools.
Visual Perception Oriented CBIR envisaged through Fractals and Presence Score
Suhas Rautmare, Anjali Bhalchandra
A. Tata Consultancy Services, Mumbai B. Govt. College of Engineering, Aurangabad
Image Features Matching and Classification Using Machine LearningIRJET Journal
This document presents a research paper that proposes a new methodology for image feature matching and classification using machine learning. The paper aims to improve accuracy and robustness in feature extraction and matching between digital images. The proposed methodology extracts features from images using machine learning, matches common features between images, and classifies objects. It is evaluated based on precision, recall, and F1-score, and shows improved performance over traditional Scale Invariant Feature Transform (SIFT) techniques on tested datasets with different objects. The proposed approach extracts fewer features and takes less computation time than traditional methods.
CBIR Processing Approach on Colored and Texture Images using KNN Classifier a...IRJET Journal
This document presents a content-based image retrieval system that uses color and texture features. It uses a K-nearest neighbor classifier to classify images based on color features and extract texture features using log-Gabor filters. Images are then ranked based on their similarity to the query image using Spearman's rank correlation coefficient. The system is tested on a dataset of flag images to retrieve the most similar flags to a given query image based on color and texture features. Experimental results show that the combined approach of using classification, similarity measures and log-Gabor filtering for color and texture features provides better retrieval performance than methods using only wavelets or Gabor filters.
A Review on Geometrical Analysis in Character Recognitioniosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document provides a review of existing methods for handwritten character recognition based on geometrical properties. It begins by classifying character recognition as either printed or handwritten, and describes the different phases a character recognition system typically includes: image acquisition, preprocessing, segmentation, feature extraction, and classification. Preprocessing steps like binarization, noise removal, normalization and morphological operations are discussed. Feature extraction methods focused on include statistical, global and structural features. Geometrical features involving lines, loops, strokes and their directions are highlighted. Classification algorithms mentioned are neural networks, SVM, k-nearest neighbor, and genetic algorithms. The literature review provides examples of character recognition research using geometrical features like horizontal/vertical line analysis and directional feature
The document proposes a new model called the Simple Connected Pattern Array Grammar (SCPAG) that is capable of generating and recognizing complex connected patterns in an image neighborhood. SCPAG aims to address the difficulty of representing all possible connected patterns on even a small 3x3 neighborhood using existing methods. The paper introduces SCPAG as a way to efficiently generate and recognize patterns by properly describing the pattern set with a uniquely parsable array grammar.
Texture Classification Based on Binary Cross Diagonal Shape Descriptor Texture Matrix (BCDSDTM)
1P.Kiran Kumar Reddy, 2Vakulabharanam Vijaya Kumar, 3B.Eswar Reddy
1RGMCET, Nandyal, AP, India, 2Anurag Group of Institutions, Hyderabad, AP, India
3JNTUA College of Engineering, India.
IRJET- Shape based Image Classification using Geometric –PropertiesIRJET Journal
This document discusses shape-based image classification using geometric properties. It proposes classifying shapes based on extracting geometric properties like area, perimeter, circularity, and eccentricity. The Discrete Wavelet Transform is used to remove noise and compress images. Then a K-Nearest Neighbor classifier is used to classify objects like squares, circles, ellipses and rectangles. The method is evaluated on the MPEG-7 dataset and achieves a maximum accuracy. Geometric properties provide powerful representations for shape recognition in content-based image retrieval applications.
A simplified and novel technique to retrieve color images from hand-drawn sk...IJECEIAES
This document presents a novel technique for retrieving color images from hand-drawn sketches. The proposed method uses K-means clustering and bag-of-attributes approaches to extract key information from sketches. It introduces a unique indexing scheme to make the retrieval process faster and more accurate. The study implemented the technique in MATLAB and found it offered better accuracy and faster processing times than existing feature extraction methods.
Face Recognition Smart Attendance System: (InClass System)IRJET Journal
- The document describes a face recognition system called "InClass" to automate student attendance tracking. It aims to address issues with traditional manual attendance systems like being inaccurate, time-consuming, and difficult to maintain.
- The InClass system uses a CNN face detector to detect and identify students' faces from images captured with a camera. It can handle variations in lighting, angles, and occlusions. Matching faces to a database allows for automated attendance marking.
- The system aims to simplify the attendance process, reduce time and errors compared to existing biometric systems, and make attendance records easily accessible and storable digitally rather than on paper.
Overwriting Grammar Model to Represent 2D Image Patterns
1Vishnu Murthy. G, 2Vakulabharanam Vijaya Kumar
1,2Anurag Group of Institutions, Hyderabad, AP,India.
IRJET- Brain Tumour Detection and ART Classification Technique in MR Brai...IRJET Journal
This document describes a proposed method for detecting and classifying brain tumors in MR brain images using robust principal component analysis (RPCA) and quad tree (QT) decomposition for image fusion. The method involves fusing T1 and T2 MRI images using RPCA and QT decomposition. The fused image is then segmented using level set segmentation. Features are extracted from the segmented image using complete local binary pattern (CLBP) and pyramid histogram of oriented gradients (PHOG) approaches. The features are then classified using an adaptive resonance theory (ART) classifier to classify the brain tumor as malignant or benign. The proposed method aims to efficiently fuse multi-modal MRI images for improved brain tumor detection and classification.
This document provides a survey of content-based image retrieval (CBIR) techniques using relevance feedback, interactive genetic algorithms, and neuro-fuzzy logic. It discusses how relevance feedback can help reduce the semantic gap between low-level image features and high-level concepts to improve retrieval accuracy. Interactive genetic algorithms make the retrieval process more interactive by evolving image content based on user feedback. Neuro-fuzzy systems combine fuzzy logic and neural networks to establish decoupled subsystems that perform classification and retrieval. The paper analyzes various CBIR systems that use these relevance feedback techniques and their performance based on precision, recall, and convergence ratio. It also covers applications of CBIR in areas like crime prevention, security, medical diagnosis, and design.
This document provides a survey of content-based image retrieval (CBIR) techniques using relevance feedback, interactive genetic algorithms, and neuro-fuzzy logic. It discusses how relevance feedback can help reduce the semantic gap between low-level image features and high-level concepts to improve retrieval accuracy. Interactive genetic algorithms make the retrieval process more interactive by evolving image content based on user feedback. Neuro-fuzzy systems combine fuzzy logic and neural networks to establish decoupled subsystems that perform classification and retrieval. The paper analyzes various CBIR systems that use these relevance feedback techniques and their performance based on precision, recall, and convergence ratio. It also covers applications of CBIR in areas like crime prevention, security, medical diagnosis, and design.
This document provides a survey of content-based image retrieval (CBIR) techniques using relevance feedback, interactive genetic algorithms, and neuro-fuzzy logic. It discusses how relevance feedback can help reduce the semantic gap between low-level image features and high-level concepts to improve retrieval accuracy. Interactive genetic algorithms make the retrieval process more interactive by evolving image content based on user feedback. Neuro-fuzzy systems combine fuzzy logic and neural networks to establish decoupled subsystems that perform classification and retrieval. The paper analyzes various CBIR systems that use these relevance feedback techniques and their performance based on precision, recall, and convergence ratio. It also outlines applications of CBIR in areas like crime prevention, security, medical diagnosis, and design.
A modified fractal texture image analysis based on grayscale morphology for m...nooriasukmaningtyas
This document presents a modified fractal texture analysis approach using grayscale morphology for analyzing multi-model views in MR brain images. The approach uses hierarchical transformations and morphological operations in pre-processing to enhance images. It then applies fractal texture analysis for segmentation and feature extraction, extracting features like mean, area, fractal dimension and shape. These features are classified using KNN and ensemble bagged tree classifiers to classify images as normal or abnormal and extract tumor regions. The morphological operations and reduced thresholds improve upon previous segmentation based fractal texture analysis methods.
Face Recognition Technique using ICA and LBPHIRJET Journal
The document describes a face recognition technique that uses Independent Component Analysis (ICA) and Local Binary Pattern Histograms (LBPH). It begins with an introduction to face recognition and the challenges involved. Then it describes LBPH, which extracts local features from images by comparing pixel intensities to the center pixel. ICA is also introduced as a method for extracting independent components from images to better represent faces across changes. The proposed technique applies both ICA and LBPH to extract features, then uses those features for face recognition by comparing an unknown image to training images. It claims this approach provides good recognition performance across variations in poses, illumination and sizes.
IRJET - Simulation of Colour Image Processing Techniques on VHDLIRJET Journal
This document summarizes research on simulating color image processing techniques using VHDL. It discusses using VHDL to implement thresholding, brightness, and inversion operations on images. The goal is to perform these operations faster than software by taking advantage of the reconfigurability and parallelism of hardware. The paper reviews related work on image processing using FPGAs and proposes simulating the image processing system using a link between MATLAB and VHDL for testing and verification.
A Review of Feature Extraction Techniques for CBIR based on SVMIJEEE
As with the advancement of multimedia technologies, users are not gratified with the conventional retrieval system techniques. So a application “Content Based Image Retrieval System” is introduced. CBIR is the application to retrieve the images or to search the digital images from the large database .The term “content” deals with the colour, shape, texture and all the information which is extracted from the image itself. This paper reviews the CBIR system which uses SVM classifier based algorithms for feature extraction phase.
International Journal of Image Processing (IJIP) Volume (3) Issue (6)CSCJournals
This paper proposes a method for face hallucination using eigen transformation in transform domains. Face hallucination aims to enhance the resolution of facial images using super resolution techniques. The proposed method performs eigen transformation on low resolution face images that have been transformed using wavelet transform or discrete cosine transform. This avoids iterative optimization techniques, making the method faster than other learning-based super resolution approaches. The results show that eigen transformation can be effectively applied in transform domains for face hallucination. This suggests it may be suitable for super resolving compressed images with minor modifications. The method provides an efficient way to enhance facial images for applications like face recognition and detection.
Secure Image Encryption using Two Dimensional Logistic Map
* Gangadhar Tiwari1, Debashis Nandi2, Abhishek Kumar3, Madhusudhan Mishra4 1, 2Department of Information Technology, NIT Durgapur (W.B.), India 3Department of Electronics and Electrical Engineering, NITAP, (A.P.), India 4Department of Electronics and Communication Engineering, NERIST, (A.P.), India
Non-Invertible Wavelet Domain Watermarking using Hash Function
*Gangadhar Tiwari1, Debashis Nandi 2, Madhusudhan Mishra3
1,2 IT Department, NIT, Durgapur-713209, West Bengal, India,
3ECE Department, NERIST, Nirjuli-791109, Arunachal Pradesh, India,
Converting UML class diagram with anti-pattern problems to verified code based on Event-B
Eman K. Elsayed
Mathematical and computer science Dep., Faculty of Science,
Al-Azhar University, Cairo, Egypt
Approach to Seismic Signal Discrimination based on Takagi-Sugeno Fuzzy Inference System
E. H. Ait Laasri, E. Akhouayri, D. Agliz, A. Atmani Electronic, Signal processing and Physical Modelling Laboratory, Physics’ Department, Faculty of Sciences, Ibn Zohr University, B.P. 8106, Agadir, Morocco
Unit Commitment Using a Hybrid Differential Evolution with Triangular Distribution Factor for Adaptive Crossover
N. Malla Reddy* K. Ramesh Reddy** and N. V. Ramana***
Intelligent e-assessment: ontological model for personalizing assessment activities
Rafaela Blanca Silva-López1, Iris Iddaly Méndez-Gurrola1, Victor Germán Sánchez Arias2
1 Universidad Autónoma Metropolitana, Unidad Azcapotzalco.
Av. San Pablo 180, Col. Reynosa Tamaulipas, Del. Azcapotzalco, México, D.F.
2 Universidad Nacional Autónoma de México
Circuito Escolar Ciudad Universitaria, 04510 México, D.F.
The State of the Art of Video Summarization for Mobile Devices:
Review Article
Hesham Farouk *, Kamal ElDahshan**, Amr Abozeid **
* Computers and Systems Dept., Electronics Research Institute, Cairo, Egypt.
** Dept. of Mathematics, Computer Science Division,
Faculty of Science, Al-Azhar University, Cairo, Egypt.
The document discusses the performance of Flexible AC Transmission System (FACTS) devices on voltage stability in a deregulated power system. It focuses on enhancing voltage stability using FACTS controllers like Static Var Compensator (SVC) and Thyristor-controlled series capacitor (TCSC). The optimal location of FACTS devices is determined using sensitivity methods. The effectiveness of the proposed method is demonstrated on a modified IEEE-9 bus system using PowerWorld simulator 8.0.
This document summarizes a numerical study of automatically controlling and stabilizing the pulse repetition rate of passively Q-switched laser systems. The researchers propose a technique called automatic pulsed pumping that detects generated Q-switched pulses and alters the pumping power accordingly. By simulating a diode-pumped Yb:YAG laser with Cr4+:YAG as a saturable absorber using rate equations, the technique showed good control over pulse repetition rate with high stability, allowing for both high rates like pulsed pumping and stability like continuous pumping.
The document analyzes and compares the movement of metallic particles in SF6/N2, SF6/CO2, and SF6/Air gas mixtures used in gas insulated substations. It finds that SF6 has a nonlinear breakdown strength affected by metallic particles, and is also a greenhouse gas. Alternative gas mixtures are needed. The paper presents results on particle movement in these three gas mixtures as potential SF6 alternatives with good dielectric properties.
This paper discusses suppressing transformer inrush current when connecting to a PWM voltage source converter. It compares this technique to using PI control of DC voltage. The PWM converter acts as a resistor for source current, eliminating inrush. Inrush suppression principles and varying magnetizing current with control gains are examined. Both methods were developed and simulated in MATLAB/SIMULINK. The paper aims to confirm the validity of using a PWM converter to suppress transformer inrush current when connecting to the source.
This document summarizes a paper that investigates the robustness of linear time-invariant state observers when state and measurement variations occur. The paper applies observer theory to an internal combustion engine model and analyzes how state and sensor disturbances affect observer error dynamics and performance robustness. It was authored by S.O. Omekanda from Oakland University, T. Perkins from Oakland University, and M.A. Zohdy from Oakland University.
The document summarizes a two-branch parallel RC circuit Simulink model of a lithium polymer battery. The model parameters depend on temperature and state of charge, which are represented using 2D lookup tables. User-defined functions calculate open circuit voltage and internal resistance. A dedicated block autonomously detects charge/discharge transitions and resets the current integrator. Comparisons to experimental data show maximum errors of 3% for dynamic responses and 5% for static discharging responses.
This paper presents a new hybrid dynamic approach to control the sequential motions of sitting, standing, and transitions between poses in biped robots. Control of each motion is performed using nonlinear backstepping with integral action. Stability and robustness of the biped robot control system are addressed through simulations demonstrating the effectiveness of the control approach.
The document describes a cognitive model that incorporates emotions to model decision making, specifically applied to modeling the behavior of the Red Baron fighter pilot. It develops an emotional model based on theories of emotions and combines this with a cognitive model. Relationships between emotions and behaviors are represented using fuzzy cognitive maps to allow validation of the model using hypothetical combat scenarios. The model shows how specific emotions relate to behavioral actions and outcomes, providing a way to include emotions in decision making processes.
This document analyzes alternatives to traditional alphanumeric passwords including enhancements to traditional passwords and replacements. It discusses various options such as one-time passwords, certificate-based passwords, biometrics, and graphical passwords. It evaluates each option based on ease of use, ease of implementation, security, and versatility. The document concludes that properly chosen traditional alphanumeric passwords currently work better than other available alternatives.
This document describes a system called Visual Simplified Characters' Emotion Emulator (EVE) which allows users to design stories and simulate characters' emotional reactions. EVE implements the OCC cognitive model of emotion. It allows users to define characters, their relationships, possible events, objects, and actions. EVE then maps the characters' emotional states and the likelihood of events/actions based on desirability values assigned by the user. EVE considers happiness, anger, pride and their opposites. It uses matrices to quantitatively determine characters' emotional responses to events, objects and each others' actions based on their prior emotional states and relationships.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
AI for Legal Research with applications, toolsmahaffeycheryld
AI applications in legal research include rapid document analysis, case law review, and statute interpretation. AI-powered tools can sift through vast legal databases to find relevant precedents and citations, enhancing research accuracy and speed. They assist in legal writing by drafting and proofreading documents. Predictive analytics help foresee case outcomes based on historical data, aiding in strategic decision-making. AI also automates routine tasks like contract review and due diligence, freeing up lawyers to focus on complex legal issues. These applications make legal research more efficient, cost-effective, and accessible.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Software Engineering and Project Management - Software Testing + Agile Method...Prakhyath Rai
Software Testing: A Strategic Approach to Software Testing, Strategic Issues, Test Strategies for Conventional Software, Test Strategies for Object -Oriented Software, Validation Testing, System Testing, The Art of Debugging.
Agile Methodology: Before Agile – Waterfall, Agile Development.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 08 Doors and Windows.pdf
P1151404314
1. Bench Marking Higuchi Fractal for CBIR
A. Suhas Rautmare, B. Anjali Bhalchandra
A. Tata Consultancy Services, Mumbai B. Govt. College of Engineering, Aurangabad
1www.icgst.com
http://www.icgst.com/paper.aspx?pid=P1151404314
2. Content Based Image Retrieval (CBIR) has been approached through color, shape, texture and many
other approaches. CBIR can also be approached through fractals. Fractal dimension based approach and
fractal compression based approach are two variants in this domain. The proposed work implements a
fractal dimension based approach supported by morphological operations to enhance the results. The
images under consideration are resized to 128x128. Fractals are patterns that exhibit self similarity. The
paper discusses Higuchi Fractal dimension (HFD) based approach for CBIR. HFD provides direction
dependent and direction independent analysis of the image. To enhance the results, morphological
operations have been performed. The main purpose of adopting morphological operations is to extract
image components that are useful in the representation and description of the content of image.
Morphological operations like fill Holes, clear border object and dilation of the image have been used to
enhance the results. A feature vector of the size 256, 128 for columns and 128 for rows, has been used
to identify the relevant match of the images through Euclidean distance measure. The paper establishes
a bench mark for Higuchi fractal dimension to be used for CBIR. The performance of the proposed
system is good enough in comparison with other fractal based systems to be considered as a bench
mark for using Higuchi fractal in CBIR process. The results and analysis has been presented for the
proposed approach.
2www.icgst.com
http://www.icgst.com/paper.aspx?pid=P1151404314
Bench Marking Higuchi Fractal for CBIR
Abstract
3. 3www.icgst.com
Dr. Anjali Bhalchandra has received B.E. (Electronics & Telecommunica-tion), M.E.
(Electronics), Ph.D. (Electronics)from SGGS college of engineering, Nanded,
Maharashtra, India. She is in the field of Engineering Education for last 29 years.
Currently she is working as Head of E & T/C Dept. at GEC, Aurangabad (An
Autonomous Institute of Govt. of India). Her research interests are in the area of
image and signal processing with special focus on segmentation, CBIR techniques and
Blind signal processing. She has more than 50 publications to her credit published at
various international Journals, conferences and seminars. She has delivered several
lectures on Image processing in Universities of India
Government Engineering College Osmanpura
http://www.geca.ac.in/
4. 4www.icgst.com
Suhas S. Rautmare has received B.E.(Elect and Telecomm) degree from Marathwada
University and M. Tech. (EDT) from CEDT. He is currently working with Tata
Consultancy Services, Mumbai. He is research scholar at Govt. College of Engineering,
Aurangabad. His areas of interest and research are image processing, information
security and automation.
Government Engineering College Osmanpura
http://www.geca.ac.in/