The world over, image recognition are essential players in promoting quality object recognition especially in emergency and search-rescue operation. In this paper precise image recognition system using Matlab Simulink Blockset to detect selected object from crowd is presented. The process involves extracting object
features and then recognizes it considering illumination, direction and pose. A Simulink model has been developed to eliminate the tiny elements from the image, then creating segments for precise object recognition. Furthermore, the simulation explores image recognition from the coloured and gray-scale images through image processing techniques in Simulink environment. The tool employed for computation
and simulation is the Matlab image processing blockset. The process comprises morphological operation method which is effective for captured images and video. The results of extensive simulations indicate that this method is suitable for application identifying a person from a crow. The model can be used in emergency and search-rescue operation as well as in medicine, information security, access control, law enforcement, surveillance system, microscopy etc.
Performance analysis on color image mosaicing techniques on FPGAIJECEIAES
Today, the surveillance systems and other monitoring systems are considering the capturing of image sequences in a single frame. The captured images can be combined to get the mosaiced image or combined image sequence. But the captured image may have quality issues like brightness issue, alignment issue (correlation issue), resolution issue, manual image registration issue etc. The existing technique like cross correlation can offer better image mosaicing but faces brightness issue in mosaicing. Thus, this paper introduces two different methods for mosaicing i.e., (a) Sliding Window Module (SWM) based Color Image Mosaicing (CIM) and (b) Discrete Cosine Transform (DCT) based CIM on Field Programmable Gate Array (FPGA). The SWM based CIM adopted for corner detection of two images and perform the automatic image registration while DCT based CIM aligns both the local as well as global alignment of images by using phase correlation approach. Finally, these two methods performances are analyzed by comparing with parameters like PSNR, MSE, device utilization and execution time. From the analysis it is concluded that the DCT based CIM can offers significant results than SWM based CIM.
We presents a technique for moving objects extraction. There are several different approaches for moving object extraction, clustering is one of object extraction method with a stronger teorical foundation used in many applications. And need high performance in many extraction process of moving object. We compare K-Means and Self-Organizing Map method for extraction moving objects, for performance measurement of moving object extraction by applying MSE and PSNR. According to experimental result that the MSE value of K-Means is smaller than Self-Organizing Map. It is also that PSNR of K-Means is higher than Self-Organizing Map algorithm. The result proves that K-Means is a promising method to cluster pixels in moving objects extraction.
Face Recognition Based Intelligent Door Control Systemijtsrd
This paper presents the intelligent door control system based on face detection and recognition. This system can avoid the need to control by persons with the use of keys, security cards, password or pattern to open the door. The main objective is to develop a simple and fast recognition system for personal identification and face recognition to provide the security system. Face is a complex multidimensional structure and needs good computing techniques for recognition. The system is composed of two main parts face recognition and automatic door access control. It needs to detect the face before recognizing the face of the person. In face detection step, Viola Jones face detection algorithm is applied to detect the human face. Face recognition is implemented by using the Principal Component Analysis PCA and Neural Network. Image processing toolbox which is in MATLAB 2013a is used for the recognition process in this research. The PIC microcontroller is used to automatic door access control system by programming MikroC language. The door is opened automatically for the known person according to the result of verification in the MATLAB. On the other hand, the door remains closed for the unknown person. San San Naing | Thiri Oo Kywe | Ni Ni San Hlaing ""Face Recognition Based Intelligent Door Control System"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23893.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/23893/face-recognition-based-intelligent-door-control-system/san-san-naing
Tracking Chessboard Corners Using Projective Transformation for Augmented Rea...CSCJournals
Augmented reality has been a topic of intense research for several years for many applications. It consists of inserting a virtual object into a real scene. The virtual object must be accurately positioned in a desired place. Some measurements (calibration) are thus required and a set of correspondences between points on the calibration target and the camera images must be found. In this paper, we present a tracking technique based on both detection of Chessboard corners and a least squares method; the objective is to estimate the perspective transformation matrix for the current view of the camera. This technique does not require any information or computation of the camera parameters; it can used in real time without any initialization and the user can change the camera focal without any fear of losing alignment between real and virtual object.
Real Time Object Identification for Intelligent Video Surveillance ApplicationsEditor IJCATR
Intelligent video surveillance system has emerged as a very important research topic in the computer vision field in the
recent years. It is well suited for a broad range of applications such as to monitor activities at traffic intersections for detecting
congestions and predict the traffic flow. Object classification in the field of video surveillance is a key component of smart
surveillance software. Two robust methodology and algorithms adopted for people and object classification for automated surveillance
systems is proposed in this paper. First method uses background subtraction model for detecting the object motion. The background
subtraction and image segmentation based on morphological transformation for tracking and object classification on highways is
proposed. This algorithm uses erosion followed by dilation on various frames. Proposed algorithm in first method, segments the image
by preserving important edges which improves the adaptive background mixture model and makes the system learn faster and more
accurately. The system used in second method adopts the object detection method without background subtraction because of the static
object detection. Segmentation is done by the bounding box registration technique. Then the classification is done with the multiclass
SVM using the edge histogram as features. The edge histograms are calculated for various bin values in different environment. The
result obtained demonstrates the effectiveness of the proposed approach.
Performance analysis on color image mosaicing techniques on FPGAIJECEIAES
Today, the surveillance systems and other monitoring systems are considering the capturing of image sequences in a single frame. The captured images can be combined to get the mosaiced image or combined image sequence. But the captured image may have quality issues like brightness issue, alignment issue (correlation issue), resolution issue, manual image registration issue etc. The existing technique like cross correlation can offer better image mosaicing but faces brightness issue in mosaicing. Thus, this paper introduces two different methods for mosaicing i.e., (a) Sliding Window Module (SWM) based Color Image Mosaicing (CIM) and (b) Discrete Cosine Transform (DCT) based CIM on Field Programmable Gate Array (FPGA). The SWM based CIM adopted for corner detection of two images and perform the automatic image registration while DCT based CIM aligns both the local as well as global alignment of images by using phase correlation approach. Finally, these two methods performances are analyzed by comparing with parameters like PSNR, MSE, device utilization and execution time. From the analysis it is concluded that the DCT based CIM can offers significant results than SWM based CIM.
We presents a technique for moving objects extraction. There are several different approaches for moving object extraction, clustering is one of object extraction method with a stronger teorical foundation used in many applications. And need high performance in many extraction process of moving object. We compare K-Means and Self-Organizing Map method for extraction moving objects, for performance measurement of moving object extraction by applying MSE and PSNR. According to experimental result that the MSE value of K-Means is smaller than Self-Organizing Map. It is also that PSNR of K-Means is higher than Self-Organizing Map algorithm. The result proves that K-Means is a promising method to cluster pixels in moving objects extraction.
Face Recognition Based Intelligent Door Control Systemijtsrd
This paper presents the intelligent door control system based on face detection and recognition. This system can avoid the need to control by persons with the use of keys, security cards, password or pattern to open the door. The main objective is to develop a simple and fast recognition system for personal identification and face recognition to provide the security system. Face is a complex multidimensional structure and needs good computing techniques for recognition. The system is composed of two main parts face recognition and automatic door access control. It needs to detect the face before recognizing the face of the person. In face detection step, Viola Jones face detection algorithm is applied to detect the human face. Face recognition is implemented by using the Principal Component Analysis PCA and Neural Network. Image processing toolbox which is in MATLAB 2013a is used for the recognition process in this research. The PIC microcontroller is used to automatic door access control system by programming MikroC language. The door is opened automatically for the known person according to the result of verification in the MATLAB. On the other hand, the door remains closed for the unknown person. San San Naing | Thiri Oo Kywe | Ni Ni San Hlaing ""Face Recognition Based Intelligent Door Control System"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23893.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/23893/face-recognition-based-intelligent-door-control-system/san-san-naing
Tracking Chessboard Corners Using Projective Transformation for Augmented Rea...CSCJournals
Augmented reality has been a topic of intense research for several years for many applications. It consists of inserting a virtual object into a real scene. The virtual object must be accurately positioned in a desired place. Some measurements (calibration) are thus required and a set of correspondences between points on the calibration target and the camera images must be found. In this paper, we present a tracking technique based on both detection of Chessboard corners and a least squares method; the objective is to estimate the perspective transformation matrix for the current view of the camera. This technique does not require any information or computation of the camera parameters; it can used in real time without any initialization and the user can change the camera focal without any fear of losing alignment between real and virtual object.
Real Time Object Identification for Intelligent Video Surveillance ApplicationsEditor IJCATR
Intelligent video surveillance system has emerged as a very important research topic in the computer vision field in the
recent years. It is well suited for a broad range of applications such as to monitor activities at traffic intersections for detecting
congestions and predict the traffic flow. Object classification in the field of video surveillance is a key component of smart
surveillance software. Two robust methodology and algorithms adopted for people and object classification for automated surveillance
systems is proposed in this paper. First method uses background subtraction model for detecting the object motion. The background
subtraction and image segmentation based on morphological transformation for tracking and object classification on highways is
proposed. This algorithm uses erosion followed by dilation on various frames. Proposed algorithm in first method, segments the image
by preserving important edges which improves the adaptive background mixture model and makes the system learn faster and more
accurately. The system used in second method adopts the object detection method without background subtraction because of the static
object detection. Segmentation is done by the bounding box registration technique. Then the classification is done with the multiclass
SVM using the edge histogram as features. The edge histograms are calculated for various bin values in different environment. The
result obtained demonstrates the effectiveness of the proposed approach.
Text and Object Recognition using Deep Learning for Visually Impaired Peopleijtsrd
the main aim of this paper is to aid the visually impaired people with object detection and text detection using deep learning. Object detection is done using a convolution neural network and text recognition is done by optical character recognition. The detected output is converted into speech using text to the speech synthesizer. Object detection comprises of two methods. One is object localization and the other is image classification. Image classification refers to the prediction of classes of different objects within an image. Object localization infers the location of objects using bounding boxes. R. Soniya | B. Mounica | A. Joshpin Shyamala | Mr. D. Balakumaran "Text and Object Recognition using Deep Learning for Visually Impaired People" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31508.pdf Paper Url :https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/31508/text-and-object-recognition-using-deep-learning-for-visually-impaired-people/r-soniya
Implementation of Object Tracking for Real Time VideoIDES Editor
Real-time tracking of object boundaries is an
important task in many vision applications. Here we propose
an approach to implement the level set method. This approach
does not need to solve any partial differential equations (PDFs),
thus reducing the computation dramatically compared with
optimized narrow band techniques proposed before. With our
approach, real-time level-set based video tracking can be
achieved.
Classification and Detection of Vehicles using Deep Learningijtsrd
The vehicle classification and detecting its license plate are important tasks in intelligent security and transportation systems. The traditional methods of vehicle classification and detection are highly complex which provides coarse grained results due to suffering from limited viewpoints. Because of the latest achievements of Deep Learning, it was successfully applied to image classification and detection of objects. This paper presents a method based on a convolutional neural network, which consists of two steps vehicle classification and vehicle license plate recognition. Several typical neural network modules have been applied in training and testing the vehicle Classification and detection of license plate model, such as CNN convolutional neural networks , TensorFlow, Tesseract OCR. The proposed method can identify the vehicle type, number plate and other information accurately. This model provides security and log details regarding vehicles by using AI Surveillance. It guides the surveillance operators and assists human resources. With the help of the original training dataset and enriched testing dataset, the algorithm can obtain results with an average accuracy of about 97.32 in the classification and detection of vehicles. By increasing the amount of the data, the mean error and misclassification rate gradually decreases. So, this algorithm which is based on Deep Learning has good superiority and adaptability. When compared to the leading methods in the challenging Image datasets, our deep learning approach obtains highly competitive results. Finally, this paper proposes modern methods for the improvement of the algorithm and prospects the development direction of deep learning in the field of machine learning and artificial intelligence. Madde Pavan Kumar | Dr. K. Manivel | N. Jayanthi "Classification & Detection of Vehicles using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30353.pdf Paper Url :https://www.ijtsrd.com/engineering/software-engineering/30353/classification-and-detection-of-vehicles-using-deep-learning/madde-pavan-kumar
VEHICLE CLASSIFICATION USING THE CONVOLUTION NEURAL NETWORK APPROACHJANAK TRIVEDI
We present vehicle detection classification using the Convolution
Neural Network (CNN) of the deep learning approach. The automatic vehicle
classification for traffic surveillance video systems is challenging for the Intelligent
Transportation System (ITS) to build a smart city. In this article, three different
vehicles: bike, car and truck classification are considered for around 3,000 bikes,
6,000 cars, and 2,000 images of trucks. CNN can automatically absorb and extract
different vehicle dataset’s different features without a manual selection of features.
The accuracy of CNN is measured in terms of the confidence values of the detected
object. The highest confidence value is about 0.99 in the case of the bike category
vehicle classification. The automatic vehicle classification supports building an
electronic toll collection system and identifying emergency vehicles in the traffic
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.
PARALLEL GENERATION OF IMAGE LAYERS CONSTRUCTED BY EDGE DETECTION USING MESSA...ijcsit
Edge detection is one of the most fundamental algorithms in digital image processing. Many algorithms have been implemented to construct image layers extracted from the original image based on selecting threshold parameters. Changing theses parameters to get a high quality layer is time consuming. In this paper, we propose two parallel technique, NASHT1 and NASHT2, to generate multiple layers of an input
image automatically to enable the image tester to select the highest quality detected edges. In addition, the
effect of intensive I/O operations and the number of parallel running processes on the performance of the proposed techniques have also been studied.
A UTILIZATION OF CONVOLUTIONAL MATRIX METHODS ON SLICED HIPPOCAMPAL NEURON RE...ijscai
Present methodologies for cell segmentation on hippocampal neuron regions contain excess information leading to the creation of unwanted noise. To distinctly draw boundaries around the cells in each of the channels like DAPI, Cy5, TRITC, FITC, it is pertinent to start off by denoising the present data and cropping the relevant ROI for analysis to remove excess background information. Present edge detection methodologies like Canny Edge Detection create black and white outputs. It is difficult to accurately do edge detection with color throughout an entire image. As such, we utilized a more involved approach that uses pixel level comparisons to determine the existence of an edge points. By extrapolating all the available edge points, our algorithms are able to detect general edges throughout an imagine. To streamline the process, it has been accompanied with a GUI interface which allows for freehand crops. This information is stored in a downloadable txt file, which provides the necessary input for the thresholding and final cropping. Together, the interface works to create clean data which is ready for further analysis with algorithms likes FRCNN and YOLOv3.
Development of Human Tracking System For Video Surveillancecscpconf
Visual surveillance in dynamic scenes, especially for human and some objects is one of the
most active research areas. An attempt has been made to this issue in this work. It has wide
spectrum of promising application including human identification to detect the suspicious
behavior, crowd flux statistics, and congestion analysis using multiple cameras.
In this paper deals with the problem of detecting and tracking multiple moving people in a static
background. Detection of foreground object is done by background subtraction. Detected
objects are identified and analyzed through different blobs. Then tracking is performed by
matching corresponding features of blob. An algorithm has been developed in this perspective
using Angular Deviation of Center of Gravity (ADCG), which gives a satisfying result for segmentation of human object.
PC-based Vision System for Operating Parameter Identification on a CNC MachineIDES Editor
Identification of suitable or optimum operating
parameters on a CNC machine is a non-trivial task. Especially
when the material of the component changes, operating
parameters need to be suitably varied. In this paper, a PCbased
vision system is presented for the automatic identification
of component material and appropriate selection of operating
parameters. The objective of this work is to develop a support
system to aid the operator in quick identification of machining
parameters
Neural Network based Vehicle Classification for Intelligent Traffic Controlijseajournal
Nowadays, number of vehicles has been increased and traditional systems of traffic controlling couldn’t be
able to meet the needs that cause to emergence of Intelligent Traffic Controlling Systems. They improve
controlling and urban management and increase confidence index in roads and highways. The goal of this
article is vehicles classification base on neural networks. In this research, it has been used a immovable
camera which is located in nearly close height of the road surface to detect and classify the vehicles. The
algorithm that used is included two general phases; at first, we are obtaining mobile vehicles in the traffic
situations by using some techniques included image processing and remove background of the images and
performing edge detection and morphology operations. In the second phase, vehicles near the camera are
selected and the specific features are processed and extracted. These features apply to the neural networks
as a vector so the outputs determine type of vehicle. This presented model is able to classify the vehicles in
three classes; heavy vehicles, light vehicles and motorcycles. Results demonstrate accuracy of the
algorithm and its highly functional level.
Color based image processing , tracking and automation using matlabKamal Pradhan
Image processing is a form of signal processing in which the input is an image, such as a photograph or video frame. The output of image processing may be either an image or, a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it. This project aims at processing the real time images captured by a Webcam for motion detection and Color Recognition and system automation using MATLAB programming.
In color based image processing we work with colors instead of object. Color provides powerful information for object recognition. A simple and effective recognition scheme is to represent and match images on the basis of color histograms.
Tracking refers to detection of the path of the color once the color based processing is done the color becomes the object to be tracked this can be very helpful in security purposes.
Automation refers to an automated system is any system that does not require human intervention. In this project I’ve automated the mouse that work with our gesture and do the desired tasks.
Extraction of Buildings from Satellite ImagesAkanksha Prasad
Buildings are termed as important components for various applications. Building extraction is defined as a sub-problem of Object Recognition. Though, numerous building extraction techniques have been proposed in the literature. But still they often exhibit limited success in the real scenarios. The main purpose of this research is to develop an algorithm which is able to detect and extract buildings from satellite images. In the proposed approach feature-based extraction process is used to extract buildings from satellite images. The overall system is tested and high performance detection is achieved which shows the effectiveness of proposed approach.
HARDWARE SOFTWARE CO-SIMULATION FOR TRAFFIC LOAD COMPUTATION USING MATLAB SIM...ijcsity
Due to increase in number of vehicles, Traffic is a major problem faced in urban areas throughout the
world. This document presents a newly developed Matlab Simulink model to compute traffic load for real
time traffic signal control. Signal processing, video and image processing and Xilinx Blockset have been
extensively used for traffic load computation. The approach used is Edge detection operation, wherein,
Edges are extracted to identify the number of vehicles. The developed model computes the results with
greater degrees of accuracy and is capable of being used to set the green signal duration so as to release
the traffic dynamically on traffic junctions.
Xilinx System Generator (XSG) provides Simulink Blockset for several hardware operations that could be
implemented on various Xilinx Field programmable gate arrays (FPGAs). The method described in this
paper involves object feature identification and detection. Xilinx System Generator provides some blocks to
transform data provided from the software side of the simulation environment to the hardware side. In our
case it is MATLAB Simulink to System Generator blocks. This is an important concept to understand in the
design process using Xilinx System Generator. The Xilinx System Generator, embedded in MATLAB
Simulink is used to program the model and then test on the FPGA board using the properties of hardware
co-simulation tools.
Text and Object Recognition using Deep Learning for Visually Impaired Peopleijtsrd
the main aim of this paper is to aid the visually impaired people with object detection and text detection using deep learning. Object detection is done using a convolution neural network and text recognition is done by optical character recognition. The detected output is converted into speech using text to the speech synthesizer. Object detection comprises of two methods. One is object localization and the other is image classification. Image classification refers to the prediction of classes of different objects within an image. Object localization infers the location of objects using bounding boxes. R. Soniya | B. Mounica | A. Joshpin Shyamala | Mr. D. Balakumaran "Text and Object Recognition using Deep Learning for Visually Impaired People" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31508.pdf Paper Url :https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/31508/text-and-object-recognition-using-deep-learning-for-visually-impaired-people/r-soniya
Implementation of Object Tracking for Real Time VideoIDES Editor
Real-time tracking of object boundaries is an
important task in many vision applications. Here we propose
an approach to implement the level set method. This approach
does not need to solve any partial differential equations (PDFs),
thus reducing the computation dramatically compared with
optimized narrow band techniques proposed before. With our
approach, real-time level-set based video tracking can be
achieved.
Classification and Detection of Vehicles using Deep Learningijtsrd
The vehicle classification and detecting its license plate are important tasks in intelligent security and transportation systems. The traditional methods of vehicle classification and detection are highly complex which provides coarse grained results due to suffering from limited viewpoints. Because of the latest achievements of Deep Learning, it was successfully applied to image classification and detection of objects. This paper presents a method based on a convolutional neural network, which consists of two steps vehicle classification and vehicle license plate recognition. Several typical neural network modules have been applied in training and testing the vehicle Classification and detection of license plate model, such as CNN convolutional neural networks , TensorFlow, Tesseract OCR. The proposed method can identify the vehicle type, number plate and other information accurately. This model provides security and log details regarding vehicles by using AI Surveillance. It guides the surveillance operators and assists human resources. With the help of the original training dataset and enriched testing dataset, the algorithm can obtain results with an average accuracy of about 97.32 in the classification and detection of vehicles. By increasing the amount of the data, the mean error and misclassification rate gradually decreases. So, this algorithm which is based on Deep Learning has good superiority and adaptability. When compared to the leading methods in the challenging Image datasets, our deep learning approach obtains highly competitive results. Finally, this paper proposes modern methods for the improvement of the algorithm and prospects the development direction of deep learning in the field of machine learning and artificial intelligence. Madde Pavan Kumar | Dr. K. Manivel | N. Jayanthi "Classification & Detection of Vehicles using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30353.pdf Paper Url :https://www.ijtsrd.com/engineering/software-engineering/30353/classification-and-detection-of-vehicles-using-deep-learning/madde-pavan-kumar
VEHICLE CLASSIFICATION USING THE CONVOLUTION NEURAL NETWORK APPROACHJANAK TRIVEDI
We present vehicle detection classification using the Convolution
Neural Network (CNN) of the deep learning approach. The automatic vehicle
classification for traffic surveillance video systems is challenging for the Intelligent
Transportation System (ITS) to build a smart city. In this article, three different
vehicles: bike, car and truck classification are considered for around 3,000 bikes,
6,000 cars, and 2,000 images of trucks. CNN can automatically absorb and extract
different vehicle dataset’s different features without a manual selection of features.
The accuracy of CNN is measured in terms of the confidence values of the detected
object. The highest confidence value is about 0.99 in the case of the bike category
vehicle classification. The automatic vehicle classification supports building an
electronic toll collection system and identifying emergency vehicles in the traffic
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.
PARALLEL GENERATION OF IMAGE LAYERS CONSTRUCTED BY EDGE DETECTION USING MESSA...ijcsit
Edge detection is one of the most fundamental algorithms in digital image processing. Many algorithms have been implemented to construct image layers extracted from the original image based on selecting threshold parameters. Changing theses parameters to get a high quality layer is time consuming. In this paper, we propose two parallel technique, NASHT1 and NASHT2, to generate multiple layers of an input
image automatically to enable the image tester to select the highest quality detected edges. In addition, the
effect of intensive I/O operations and the number of parallel running processes on the performance of the proposed techniques have also been studied.
A UTILIZATION OF CONVOLUTIONAL MATRIX METHODS ON SLICED HIPPOCAMPAL NEURON RE...ijscai
Present methodologies for cell segmentation on hippocampal neuron regions contain excess information leading to the creation of unwanted noise. To distinctly draw boundaries around the cells in each of the channels like DAPI, Cy5, TRITC, FITC, it is pertinent to start off by denoising the present data and cropping the relevant ROI for analysis to remove excess background information. Present edge detection methodologies like Canny Edge Detection create black and white outputs. It is difficult to accurately do edge detection with color throughout an entire image. As such, we utilized a more involved approach that uses pixel level comparisons to determine the existence of an edge points. By extrapolating all the available edge points, our algorithms are able to detect general edges throughout an imagine. To streamline the process, it has been accompanied with a GUI interface which allows for freehand crops. This information is stored in a downloadable txt file, which provides the necessary input for the thresholding and final cropping. Together, the interface works to create clean data which is ready for further analysis with algorithms likes FRCNN and YOLOv3.
Development of Human Tracking System For Video Surveillancecscpconf
Visual surveillance in dynamic scenes, especially for human and some objects is one of the
most active research areas. An attempt has been made to this issue in this work. It has wide
spectrum of promising application including human identification to detect the suspicious
behavior, crowd flux statistics, and congestion analysis using multiple cameras.
In this paper deals with the problem of detecting and tracking multiple moving people in a static
background. Detection of foreground object is done by background subtraction. Detected
objects are identified and analyzed through different blobs. Then tracking is performed by
matching corresponding features of blob. An algorithm has been developed in this perspective
using Angular Deviation of Center of Gravity (ADCG), which gives a satisfying result for segmentation of human object.
PC-based Vision System for Operating Parameter Identification on a CNC MachineIDES Editor
Identification of suitable or optimum operating
parameters on a CNC machine is a non-trivial task. Especially
when the material of the component changes, operating
parameters need to be suitably varied. In this paper, a PCbased
vision system is presented for the automatic identification
of component material and appropriate selection of operating
parameters. The objective of this work is to develop a support
system to aid the operator in quick identification of machining
parameters
Neural Network based Vehicle Classification for Intelligent Traffic Controlijseajournal
Nowadays, number of vehicles has been increased and traditional systems of traffic controlling couldn’t be
able to meet the needs that cause to emergence of Intelligent Traffic Controlling Systems. They improve
controlling and urban management and increase confidence index in roads and highways. The goal of this
article is vehicles classification base on neural networks. In this research, it has been used a immovable
camera which is located in nearly close height of the road surface to detect and classify the vehicles. The
algorithm that used is included two general phases; at first, we are obtaining mobile vehicles in the traffic
situations by using some techniques included image processing and remove background of the images and
performing edge detection and morphology operations. In the second phase, vehicles near the camera are
selected and the specific features are processed and extracted. These features apply to the neural networks
as a vector so the outputs determine type of vehicle. This presented model is able to classify the vehicles in
three classes; heavy vehicles, light vehicles and motorcycles. Results demonstrate accuracy of the
algorithm and its highly functional level.
Color based image processing , tracking and automation using matlabKamal Pradhan
Image processing is a form of signal processing in which the input is an image, such as a photograph or video frame. The output of image processing may be either an image or, a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it. This project aims at processing the real time images captured by a Webcam for motion detection and Color Recognition and system automation using MATLAB programming.
In color based image processing we work with colors instead of object. Color provides powerful information for object recognition. A simple and effective recognition scheme is to represent and match images on the basis of color histograms.
Tracking refers to detection of the path of the color once the color based processing is done the color becomes the object to be tracked this can be very helpful in security purposes.
Automation refers to an automated system is any system that does not require human intervention. In this project I’ve automated the mouse that work with our gesture and do the desired tasks.
Extraction of Buildings from Satellite ImagesAkanksha Prasad
Buildings are termed as important components for various applications. Building extraction is defined as a sub-problem of Object Recognition. Though, numerous building extraction techniques have been proposed in the literature. But still they often exhibit limited success in the real scenarios. The main purpose of this research is to develop an algorithm which is able to detect and extract buildings from satellite images. In the proposed approach feature-based extraction process is used to extract buildings from satellite images. The overall system is tested and high performance detection is achieved which shows the effectiveness of proposed approach.
HARDWARE SOFTWARE CO-SIMULATION FOR TRAFFIC LOAD COMPUTATION USING MATLAB SIM...ijcsity
Due to increase in number of vehicles, Traffic is a major problem faced in urban areas throughout the
world. This document presents a newly developed Matlab Simulink model to compute traffic load for real
time traffic signal control. Signal processing, video and image processing and Xilinx Blockset have been
extensively used for traffic load computation. The approach used is Edge detection operation, wherein,
Edges are extracted to identify the number of vehicles. The developed model computes the results with
greater degrees of accuracy and is capable of being used to set the green signal duration so as to release
the traffic dynamically on traffic junctions.
Xilinx System Generator (XSG) provides Simulink Blockset for several hardware operations that could be
implemented on various Xilinx Field programmable gate arrays (FPGAs). The method described in this
paper involves object feature identification and detection. Xilinx System Generator provides some blocks to
transform data provided from the software side of the simulation environment to the hardware side. In our
case it is MATLAB Simulink to System Generator blocks. This is an important concept to understand in the
design process using Xilinx System Generator. The Xilinx System Generator, embedded in MATLAB
Simulink is used to program the model and then test on the FPGA board using the properties of hardware
co-simulation tools.
COMPUTER VISION PERFORMANCE AND IMAGE QUALITY METRICS: A RECIPROCAL RELATION csandit
Computer vision algorithms are essential components of many systems in operation today. Predicting the robustness of such algorithms for different visual distortions is a task which can
be approached with known image quality measures. We evaluate the impact of several image distortions on object segmentation, tracking and detection, and analyze the predictability of this impact given by image statistics, error parameters and image quality metrics. We observe that
existing image quality metrics have shortcomings when predicting the visual quality of virtual or augmented reality scenarios. These shortcomings can be overcome by integrating computer vision approaches into image quality metrics. We thus show that image quality metrics can be
used to predict the success of computer vision approaches, and computer vision can be employed to enhance the prediction capability of image quality metrics – a reciprocal relation.
Computer Vision Performance and Image Quality Metrics : A Reciprocal Relation cscpconf
Computer vision algorithms are essential components of many systems in operation today.
Predicting the robustness of such algorithms for different visual distortions is a task which can
be approached with known image quality measures. We evaluate the impact of several image
distortions on object segmentation, tracking and detection, and analyze the predictability of this
impact given by image statistics, error parameters and image quality metrics. We observe that
existing image quality metrics have shortcomings when predicting the visual quality of virtual
or augmented reality scenarios. These shortcomings can be overcome by integrating computer
vision approaches into image quality metrics. We thus show that image quality metrics can be
used to predict the success of computer vision approaches, and computer vision can be
employed to enhance the prediction capability of image quality metrics – a reciprocal relation.
Design and implementation of video tracking system based on camera field of viewsipij
The basic idea of this paper is to design and implement of video tracking system based on Camera Field of
View (CFOV), Otsu’s method was used to detect targets such as vehicles and people. Whereas most
algorithms were spent a lot of time to execute the process, an algorithm was developed to achieve it in a
little time. The histogram projection was used in both directional to detect target from search region,
which is robust to various light conditions in Charge Couple Device (CCD) camera images and saves
computation time.
Our algorithm based on background subtraction, and normalize cross correlation operation from a series
of sequential sub images can estimate the motion vector. Camera field of view (CFOV) was determined and
calibrated to find the relation between real distance and image distance. The system was tested by
measuring the real position of object in the laboratory and compares it with the result of computed one. So
these results are promising to develop the system in future.
Semi-Supervised Method of Multiple Object Segmentation with a Region Labeling...sipij
Efficient and efficient multiple object segmentation is an important task in computer vision and object recognition. In this work; we address a method to effectively discover a user’s concept when multiple objects of interest are involved in content based image retrieval. The proposed method incorporate a framework for multiple object retrieval using semi-supervised method of similar region merging and flood fill which models the spatial and appearance relations among image pixels. To improve the effectiveness of similarity based region merging we propose a new similarity based object retrieval. The users only need to roughly indicate the after which steps desired objects contour is obtained during the automatic merging of similar regions. A novel similarity based region merging mechanism is proposed to guide the merging process with the help of mean shift technique and objects detection using region labeling and flood fill. A region R is merged with its adjacent regions Q if Q has highest similarity with Q (using Bhattacharyya descriptor) among all Q’s adjacent regions. The proposed method automatically merges the regions that are initially segmented through mean shift technique, and then effectively extracts the object contour by merging all similar regions. Extensive experiments are performed on 12 object classes (224 images total) show promising results.
An Analysis of Various Deep Learning Algorithms for Image Processingvivatechijri
Various applications of image processing has given it a wider scope when it comes to data analysis.
Various Machine Learning Algorithms provide a powerful environment for training modules effectively to
identify various entities of images and segment the same accordingly. Rather one can observe that though the
image classifiers like the Support Vector Machines (SVM) or Random Forest Algorithms do justice to the task,
deep learning algorithms like the Artificial Neural Networks (ANN) and its subordinates, the very well-known
and extremely powerful Algorithm Convolution Neural Networks (CNN) can provide a new dimension to the
image processing domain. It has way higher accuracy and computational power for classifying images further
and segregating their various entities as individual components of the image working region. Major focus will
be on the Region Convolution Neural Networks (R-CNN) algorithm and how well it provides the pixel-level
segmentation further using its better successors like the Fast-Faster and Mask R-CNN versions.
Statistical Feature based Blind Classifier for JPEG Image Splice Detectionrahulmonikasharma
Digital imaging, image forgery and its forensics have become an established field of research now days. Digital imaging is used to enhance and restore images to make them more meaningful while image forgery is done to produce fake facts by tampering images. Digital forensics is then required to examine the questioned images and classify them as authentic or tampered. This paper aims to design and implement a blind classifier to classify original and spliced Joint Photographic Experts Group (JPEG) images. Classifier is based on statistical features obtained by exploiting image compression artifacts which are extracted as Blocking Artifact Characteristics Matrix. The experimental results have shown that the proposed classifier outperforms the existing one. It gives improved performance in terms of accuracy and area under curve while classifying images. It supports .bmp and .tiff file formats and is fairly robust to noise.
OBJECT DETECTION, EXTRACTION AND CLASSIFICATION USING IMAGE PROCESSING TECHNIQUEJournal For Research
Domestic refrigerators are widely used household appliances and a large extent of energy is consumed by this system. A phase change material is a substances that can store or release significant amount of heat energy by changing the phase liquid to vapour or vice versa. So, reduction of temperature fluctuation and improvement of system performances is that main reason of using PCM enhances the heat transfer rate thus improves the COP of refrigeration as well as the quality frozen food. The release and storage rate of a refrigerator is depends upon the characteristics of refrigerators and its properties using phase change material for a certain thermal load it is found that COP of conventional refrigerator is increased . The phase change material used in chamber built manually and which surrounds the evaporator chamber of a conventional refrigerator the whole heat transfer for load given to refrigerator cabin (to evaporator) evaporator to phase change material by conduction. This system hence improves the performances of household refrigerator by increasing its compressor cut-off time and thereby minimizing electrical energy usage. The main objective is to improve the performance, cooling time period, storage capacity and to maintain the constant cooling effect for more time during power cut off hours using phase change material.
Foreground algorithms for detection and extraction of an object in multimedia...IJECEIAES
Background Subtraction of a foreground object in multimedia is one of the major preprocessing steps involved in many vision-based applications. The main logic for detecting moving objects from the video is difference of the current frame and a reference frame which is called “background image” and this method is known as frame differencing method. Background Subtraction is widely used for real-time motion gesture recognition to be used in gesture enabled items like vehicles or automated gadgets. It is also used in content-based video coding, traffic monitoring, object tracking, digital forensics and human-computer interaction. Now-a-days due to advent in technology it is noticed that most of the conferences, meetings and interviews are done on video calls. It’s quite obvious that a conference room like atmosphere is not always readily available at any point of time. To eradicate this issue, an efficient algorithm for foreground extraction in a multimedia on video calls is very much needed. This paper is not to just build Background Subtraction application for Mobile Platform but to optimize the existing OpenCV algorithm to work on limited resources on mobile platform without reducing the performance. In this paper, comparison of various foreground detection, extraction and feature detection algorithms are done on mobile platform using OpenCV. The set of experiments were conducted to appraise the efficiency of each algorithm over the other. The overall performances of these algorithms were compared on the basis of execution time, resolution and resources required.
Automated Traffic sign board classification system is one of the key technologies of Intelligent
Transportation Systems (ITS). Traffic Surveillance System is being more and important with improving
urban scale and increasing number of vehicles. This Paper presents an intelligent sign board
classification method based on blob analysis in traffic surveillance. Processing is done by three main
steps: moving object segmentation, blob analysis, and classifying. A Sign board is modelled as a
rectangular patch and classified via blob analysis. By processing the blob of sign boards, the meaningful
features are extracted. Tracking moving targets is achieved by comparing the extracted features with
training data. After classifying the sign boards the system will intimate to user in the form of alarms,
sound waves. The experimental results show that the proposed system can provide real-time and useful
information for traffic surveillance.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Adversarial Attention Modeling for Multi-dimensional Emotion Regression.pdf
IMAGE RECOGNITION USING MATLAB SIMULINK BLOCKSET
1. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol. 7, No. 2, April 2017
1
IMAGE RECOGNITION USING MATLAB SIMULINK
BLOCKSET
Sagir Lawan and CL Wamdeo
Department of Electrical/Electronic Engineering, Nigerian Defence Academy, Kaduna
ABSTRACT
The world over, image recognition are essential players in promoting quality object recognition especially
in emergency and search-rescue operation. In this paper precise image recognition system using Matlab
Simulink Blockset to detect selected object from crowd is presented. The process involves extracting object
features and then recognizes it considering illumination, direction and pose. A Simulink model has been
developed to eliminate the tiny elements from the image, then creating segments for precise object
recognition. Furthermore, the simulation explores image recognition from the coloured and gray-scale
images through image processing techniques in Simulink environment. The tool employed for computation
and simulation is the Matlab image processing blockset. The process comprises morphological operation
method which is effective for captured images and video. The results of extensive simulations indicate that
this method is suitable for application identifying a person from a crow. The model can be used in
emergency and search-rescue operation as well as in medicine, information security, access control, law
enforcement, surveillance system, microscopy etc.
KEYWORDS
Matlab, Morphological Operations, identification, Target Recognition, Simulink, Video and Image
Processing
1. INTRODUCTION
Presently, there exists a good number of software for digital image processing in object
recognition. Image recognition has varied applications in medicine, information security, access
control, law enforcement, surveillance system, microscopy etc. Image processing techniques is
used to modify pictures to improve them for recognition. In some cases image processing is used
to change images structure by optical, photographic and electronic means. Image processing
using digital computers is the most common because digital methods are fast, flexible and
precise. Hence, computer algorithms are continuously being developed to achieve better results.
The search for more reliable, cheaper and less complex computation led to use of Matlab
Simulink Model Blockset for image recognition.
In making operational decisions especially in the military operations in urban terrain, it may be
possible to take into consideration image processing to aid in object recognition. This paper aims
at identifying important object in images captured from scene using Blockset in Simulink
environment of MATLAB. The Video and Image Processing Blockset contains blocks that
perform morphological operations such as erosion, dilation, opening and closing. A Simulink
model has been developed to eliminate the tiny elements from the image, then creating segments
for precise object recognition [1]. The remainder of the paper is organized as follows. Section 2
briefly presents the related research. Section 3 briefly describes on various types of object
recognition techniques. Section 4 presents experimental environment. Section 5 covers the
results and analysis. Finally, Section 5 draws the conclusion.
2. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol. 7, No. 2, April 2017
2
2. RELATED RESEARCH
The idea of target identification has taken various forms prior to image processing. A
considerable amount of literature has also been published on digital image processing and
intelligent target recognition. In [2], an approach on image/video segmentation was taken in
which existing video object segmentation algorithm and morphological operation was used to
analyse core operations. Hence, morphology image processing element array and stream
processing was used. The author, further stated that control and interconnection between
processing element can also be configured. This view is supported by [3], the authors experiment
with FPGA using Xlinx system generator to implement MRI image filtering and tour
characterization. They focused on processing an image pixel by pixel and in modification of pixel
neighbourhoods and the transformation that can be applied to the whole image or partial region.
This work was complemented by [4] study of pixel to pixel processing of an image and the
modification of pixel transformation to the whole image or only a partial region. However, a big
challenge to this approach is feature extraction.
More recent attention has focused on the problem of recognizing of vehicles on a crowed road as
contained in [1]. The author found that vehicle counting on roads can reliably be performed using
traffic load computation with Matlab Simulink Model Blockset. They stated that Simulink model
can be used to detect a particular vehicle in traffic on the road. Their algorithm proves to be an
efficient solution for computing traffic load. A broader perspective has been adopted by [5] who
argues that segmentation algorithms based on one of two basic properties partition an image
based on abrupt change in intensity and regions that are similar according to predefined criteria.
Although this is the most comprehensive Simulink image processing, the methods lacks clear
details for identification of target. In the same vein, [6] noted the detection and tracking of human
as well as vehicles. The paper hitherto have attempted to draw fine distinctions between detection
and tracking in three ways (i) automatic target detection and recognition tracking technique, (ii)
tracking object across a network and (iii) development of real time communication and message
networks.
[7 ] introduced a novel optical encryption and decryption method of gray image based on the
computer-generated hologram (CGH). However, in the encryption process, the hologram, which
is gotten by Fresnel diffraction from the gray image, is modulated with the chaotic sequence, and
the output is encryption image. According to the author in [8] image recognition was achieved
through noise reconstruction of holographic images in the form of a system of fringes parallel to
the hologram frame boundaries. The author assert that mathematical and physical interpretation is
proposed together with an algorithm for reduction of image effect using bicubic splines. Trofimov
el at, [9] proposed an improved and a real-time algorithm of computer processing for
image recognition using passive THz image with the aim of concealed object detection.
They stated that the algorithm used correlation function between the THz image and a
standard image. The result detect the presence of forbidden objects on the human body..
3. OBJECT RECOGNITION METHODS
In the 1970's, object recognition was treated as a 2D pattern recognition problem [2]. The
distances between important points where used to recognize known object, however, with
technological drive object 2D and 3D objects recognition methods are fully automated. Object
recognition methods include holistic matching methods, feature-based (structural) methods and
hybrid methods are widely used.
3. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol. 7, No. 2, April 2017
3
In both 2D and 3D holistic approach, the complete object region is taken into account as input
data into catching system. One of the best examples of holistic methods is Eigenfaces [8] (most
widely used method for face recognition). This approach covers face recognition as a two-
dimensional recognition problem. The process involves insertion of a set of images into a
database to create the eigenfaces. Eigenfaces of humans are made by extracting characteristic
features from the faces. The input images are normalized to line up the eyes and mouths. When
the eigenfaces have been created, each image will be represented as a vector of weights. The
weight of the incoming unknown image is found and then compared the weights of those already
in the system. If the input image's weight is over a given threshold it is considered to be
unidentified. [2].
Another method is described as feature-based (structural) methods: In this methods local features
such as eyes, nose and mouth are first of all extracted and their locations and local statistics
(geometric and/or appearance) are fed into a structural classifier. A draw back to this method is
feature extraction and image restoration [5]. In compliance with need for accurate image
recognition, a hybrid method was developed. This method recognized face in both 2D and 3D
pattern using a combination of both holistic and feature extraction methods.
4. EXPERIMENTAL ENVIRONMENT
To date various models for image processing have been developed and introduced. In this paper
hybrid method model based design is configured to identify and recognize important object from
a crowd using Matlab Simulink Model. The process involves use of Matlab numerical computing
environment and matrix manipulations. Simulink model is used to add graphical multi domain
simulation and embedded systems. Signal processing is achieved by adapting to the existing
algorithms contained in MATLAB programs. Figure 1 shows block diagram of simulation
process.
Figure 1: Simulink Model
The basic model design and the fundamental work to read and write images through Simulink is
shown in Figures 2 – 9. The intensity of the image under experiment is imported From Simulink
library browser to create a new Simulink model as indicated in Figure 2.
4. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol. 7, No. 2, April 2017
4
Figure 2: Simulink Library
Figure 3: New Model
The values of the block parameters are modified to complain with the specification of the targeted
object as demonstrated in Figures 4, 5 and 6. The constant value parameter is set to 200, this
defined the threshold. The Rational operator is set to” < ” and the configuration parameters is set
to solver pane stop time to zero, solver tyre to fixed-step and solver to discrete (no continuous
state). The simulated image workspace is illustrated in Figures 7, 10, 13, 16 and19.
5. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol. 7, No. 2, April 2017
5
Fig 4: Threshold
Figure 5: Opening Block
6. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol. 7, No. 2, April 2017
6
Figure 6: Label Block
5. RESULTS
This section describes the results of the simulations conducted; the overall result is presented in
terms of: A. coloured and gray-scale image, B. identification and C. boundary performance.
A. Coloured and Gray-Scale Images. The conversion of colour to gray process is done by
making all pixels above a certain threshold level white while, others black. The suitable threshold
constant taken is 200. Figures 8, 11, 14, and 17 shows results of the image inputs been converted
to gray.
Figure 7: Input Image “Classroom”
7. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol. 7, No. 2, April 2017
7
Figure 8: Classroom “Gray”
Figure 9: Classroom Object Identified
B. Identification. Identification here is simply masking operation and the main function in the
opening block create a circular STREL object with radius of 5 pixels that separate the important
objects from the image and from each other at the image. The results of the objects identified in
gray are depicted in Figures 9, 12, 15 and 18.
8. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol. 7, No. 2, April 2017
8
Figure 10: Input image “River side”
Figure 11: River side Gray
Figure 12: River side Object Identified
C. Boundary. For boundary identification, the image is filtered and masked to ensure better noise
suppression and smoothing. The STREL function provided this as illustrated in Figures 9, 12, 15
and 18.
9. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol. 7, No. 2, April 2017
9
Figure 13: Input image “Youth”
Figure 14: Youth Gray
10. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol. 7, No. 2, April 2017
10
Figure 15: Youth Object Identified
Figure 16: Input image “Lab”
Figure 17: Lab Gray
Figure 18: Lab Object Identified
11. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol. 7, No. 2, April 2017
11
6. CONCLUSION
In this paper, we presented an image recognition using Matlab Smulink blockset that facilitates
flexible identification and recognition of objet in a crowd. Our goal is to separate identified object
from many based on configuration and content management of Matlab Smulink blockset. When
employing hybrid object identification method for 2D and 3D patterns, the algorithm is easily
updated at any time. The proposed method allows reliable and dynamic object identification that
will lead to making critical decisions during emergency and search-rescue operations. Feature
steps include research on facial feature extraction of human face. Another aspect is integration of
the proposed model towards object projection of face images into a linear subspace by reducing
dimension.
REFERENCES
[1] Pratishtha G, Purohit G.N, Adhyana Gupta, “Traffic Load Computer using Matlab Simulink Model
Blockset” international Journal of Advanced Research in Computer and Communication Engineering
Vol. 2, Issue6, June 2013.
[2] Chien S.Y. ,Chen L.G., “Reconfigurable Morphological Image Processing Accelerator for Video
Object Segmentation”, springer, J Sign Process Syst (2011) 62:77–96, DOI 10.1007/s11265-008-
0311-6
[3] Christe S.A., Vignesh M., Kandaswamy A., “An efficient FPGA implementation of MRI image
filtering and tumour characterization using Xilinx system generator”, International Journal of VLSI
design & Communication Systems (VLSICS), Vol.2 ,No.4, December 2011
[4] Suthar A. C., Vayada M., Patel C. B., Kulkarni G.R., Hardware Software co-simulation for Image
Processing Applications”, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 2, No
2, March 2012
[5] Al-amri S.S., Kalyankar N.V., Khamitkar S.D, “Image Segmentation by Using Threshold
Techniques”, journal of computing, volume 2, issue 5, may 2010, ISSN 2151-9617 [6]Shinde B.,
Mhaske D., Dani A.R., “Study of Image Processing, Enhancement and Restoration”, IJCSI
International Journal of Computer Science Issues, Vol. 8, Issue 6, No 3, November 2011
[7] Mahalanobis A., Cannon J., Stanfill S.R., Muise R., Martin L., “Network Video Image Processing for
Security, Surveillance, and Situational Awareness”, Proc. SPIE 5440, Digital Wireless
Communications VI,1 (August 10, 2004); doi:10.1117/12.548981
[8] Ren, Hui, Jun Wang, and Qiong-hua Wang. "Optical Encryption of Gray Image Based on the
Computer Generated Hologram and Logical Modulation." Proc. of SPIE Vol. Vol. 10250. 2017.
[9] Dyomin, V., and A. Olshukov. "Improvement of the Quality of Reconstructed Holographic Images by
Extrapolation of Digital Holograms." Russian Physics Journal 58.10 (2016).
[10] Trofimov, Vyacheslav A., et al. "Concealed object detection using the passive THz image without its
viewing." SPIE Defense+ Security. International Society for Optics and Photonics, 2016.