- Professor, CSE, SJBIT, Bangalore, India
- Computer vision, image processing, Background Subtraction, Background modeling, Foreground Detection, Background Maintenance, Object Tracking (Computer Vision), and 13 moreObject Detection and Tracking, Moving Object Recognition, Image Enhancement, Object Extraction Using Dominant Eigen Map, Image Recognition (Computer Vision), Motion Object Tracking, Image Features Extraction, Background Reconstruction, Image segmentation, Video Processing, Shape (Computer Science), Robotics, and Parallel Computingedit
- Dr. M. T. Gopalakrishna is a Professor, Department of Computer Scienceedit
The demand for automatic action recognition systems have increased due to the rapid increase in the number of video surveillance cameras installed in cities and towns. Automatic action recognition system can be effectively used to... more
The demand for automatic action recognition systems have increased due to the rapid increase in the number of video surveillance cameras installed in cities and towns. Automatic action recognition system can be effectively used to generate on-line alarm in case of abnormal activities to assist human operators and for offline inspection. Although action recognition problem has become a hot topic within computer vision, detection of violent scenes receives considerable attention in surveillance system which is justified by the need of providing people with safer public spaces. This survey discusses the current state of the art methods and techniques that are being applied for the task of automated violence detection.This survey emphasizes on motivation and challenges of this very recent research area by presenting approaches for violence recognition in surveillance video. This paper aims at being a driving force for researchers who wish to approach the study of violent activity recognition and gather insights on the main challenges to solve in this emerging field.
Research Interests:
Moving object Detection in video sequences is one among the foremost indispensable challenges in Image and video processing. Its conjoint research areas are activity monitoring and video surveillance application. However, still beneath... more
Moving object Detection in video sequences is one among the foremost indispensable challenges in Image and video processing. Its conjoint research areas are activity monitoring and video surveillance application. However, still beneath the biological process stage needs robust approaches once applied in an unconstrained environment. Several detection algorithms have higher performance under the static background, however decline results under background with fake motions. Detecting and Tracking of multiple moving objects in presence of Litter background like leaves movement of trees, water waves, fountain, window curtain movement and change of illumination in video sequences is a challenging problem. Because of these little movements within the background, it affects the performance of the automated tracking system. To overcome the above said problem, an approach consisting of Intensity Slicing and Spatial Resolution is considered to attenuate the results caused by the Litter Background. A modified 3-frame difference technique is employed to detect a moving object. Then, Adaptive Thresholding is used to segment the object from the background and to track the object. Results are compared with the existing well known traditional techniques. The proposed technique is tested on standard PETS datasets and our own collected video datasets. The experimental results prove the feasibility and usefulness of the proposed technique.
In recent years, automatic moving object detection and tracking is a challenging task for many computer vision applications such as video surveillance, traffic monitoring and activity analysis. In this regard, many methods have been... more
In recent years, automatic moving object detection and tracking is a challenging task for many computer vision applications such as video surveillance, traffic monitoring and activity analysis. In this regard, many methods have been proposed based on different approaches. Despite of its importance, moving object detection and tracking in complex environments is still far from being completely solved for low resolution videos, foggy videos, and also Infrared video sequences. A novel scheme for Moving Object detection based on Tensor Locality Preserving Projections (Ten-LoPP) approach is proposed. Consequently, a Moving Object is tracked based on the centroid and area of a detected object. Numbers of experiments are conducted for indoor and outdoor video sequences of standard PETS, OTCBVS, Videoweb Activities datasets and also our own collected video sequences comprising partial night vision video sequences. Results obtained are satisfactory and competent. Comparative study is performed with existing well known traditional subspace learning methods.
Object recognition in the video sequence or images is one of the subfield of computer vision. Moving object recognition from a video sequence is an appealing topic with applications in various areas such as airport safety, intrusion... more
Object recognition in the video sequence or images is one of the subfield of computer vision. Moving object recognition from a video sequence is an appealing topic with applications in various areas such as airport safety, intrusion surveillance, video monitoring, intelligent highway, etc. Moving object recognition is the most challenging task in intelligent video surveillance system. In this regard, many techniques have been proposed based on different methods. Despite of its importance, moving object recognition in complex environments is still far from being completely solved for low resolution videos, foggy videos, and also dim video sequences. All in all, these make it necessary to develop exceedingly robust techniques. This paper introduces multiple moving object recognition in the video sequence based on LoG Gabor-PCA approach and Angle based distance Similarity measures techniques used to recognize the object as a human, vehicle etc. Number of experiments are conducted for indoor and outdoor video sequences of standard datasets and also our own collection of video sequences comprising of partial night vision video sequences. Experimental results show that our proposed approach achieves an excellent recognition rate. Results obtained are satisfactory and competent.
Research Interests:
The cancer is an intimidating illness. Extra care is necessary while making a diagnosis. To aid the identification process, medical imaging plays a crucial role by producing images of the internal organs of the body for better diagnosis... more
The cancer is an intimidating illness. Extra care is necessary while making a diagnosis. To aid the identification process, medical imaging plays a crucial role by producing images of the internal organs of the body for better diagnosis of cancer. Medical images are typically utilized by radiologists, engineers, and clinicians to spot the inner constitution of either individual patients or group of individuals. Most doctors prefer computed tomography (CT) images for initial screening of cancer — mainly lung cancer. To achieve deeper understanding and categorization of lung cancer, diverse machine learning techniques are employed in image classification. Many research works have been done on the classification of CT images with different algorithms, but they failed to reach 100% accuracy. By applying methods like Support Vector Machine, deep learning system like artificial neural network (ANN) and proposed convolution neural network (CNN), a computerized system can be built for truth...
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In this paper, a novel Discriminatively Trained Multi-Source CNN Model (DTM-CNN) is developed for multi-camera based vehicle tracking purpose. DTM-CNN performs pretraining of a gigantically large set of traffic videos to track ground... more
In this paper, a novel Discriminatively Trained Multi-Source CNN Model (DTM-CNN) is developed for multi-camera based vehicle tracking purpose. DTM-CNN performs pretraining of a gigantically large set of traffic videos to track ground truths for retaining region of interest (ROI) representation. Being a multi-source tracking method DTM-CNN embodies shared layers and multiple branches of source-specific layers to perform feature extraction and training. Here, source signifies each camera input with distinct training sequences, where each branch exhibits binary classification for ROI identification and tracking in each source. DTM-CNN trains each source input iteratively to achieve generic ROI representations in the shared layers. When performing tracking in a new sequence, DTM-CNN forms a new network by combining the shared layers with a new binary classification layer, which is updated online. It assists online tracking by retrieving the ROI windows arbitrarily sampled near the previous ROI state that enables DTM-CNN to exhibit continuous vehicle tracking even under short and long term occlusion.
Research Interests:
Research Interests:
The demand for automatic action recognition systems have increased due to the rapid increase in the number of video surveillance cameras installed in cities and towns. Automatic action recognition system can be effectively used to... more
The demand for automatic action recognition systems have increased due to the rapid increase in the number of video surveillance cameras installed in cities and towns. Automatic action recognition system can be effectively used to generate on-line alarm in case of abnormal activities to assist human operators and for offline inspection. Although action recognition problem has become a hot topic within computer vision, detection of violent scenes receives considerable attention in surveillance system which is justified by the need of providing people with safer public spaces. This survey discusses the current state of the art methods and techniques that are being applied for the task of automated violence detection.This survey emphasizes on motivation and challenges of this very recent research area by presenting approaches for violence recognition in surveillance video. This paper aims at being a driving force for researchers who wish to approach the study of violent activity recogn...
Research Interests:
Vision-based traffic surveillance has been one of the most promising fields for improvement and research. Still, many challenging problems remain unsolved, such as addressing vehicle occlusions and reducing false detection. In this work,... more
Vision-based traffic surveillance has been one of the most promising fields for improvement and research. Still, many challenging problems remain unsolved, such as addressing vehicle occlusions and reducing false detection. In this work, a method for vehicle detection and tracking is proposed. The proposed model considers background subtraction concept for moving vehicle detection but unlike conventional approaches, here numerous algorithmic optimization approaches have been applied such as multi-directional filtering and fusion based background subtraction, thresholding, directional filtering and morphological operations for moving vehicle detection. In addition, blob analysis and adaptive bounding box is used for Detection and Tracking. The Performance of Proposed work is measured on Standard Dataset and results are encouraging. __________________________________________________________________________
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Visual Surveillance systems have greatly increased in past few years. Several methods have been proposed in order to improve the efficiency of Face Detection but still remains a challenging task due to various illumination, poses and... more
Visual Surveillance systems have greatly increased in past few years. Several methods have been proposed in order to improve the efficiency of Face Detection but still remains a challenging task due to various illumination, poses and occlusion conditions. In this paper, we propose a novel method for Face Detection where a decision boundary is defined for skin classifier based on training dataset. Log-Gabor filter is used for feature extraction which is superior to Gabor filter as they can represent better frequency properties of the objects present in the video and SVM classifier is used for classifying it as face or non-face. The proposed method is tested on standard and our own collected video sequences, which shows good tolerance and is better than those of existing related algorithms.
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Real-time video surveillance, medical imaging, industrial automation and oceanography applications use image enhancement as a preprocessing technique for the analysis of images. Contrast enhancement is one of a method to enhance low... more
Real-time video surveillance, medical imaging, industrial automation and oceanography applications use image enhancement as a preprocessing technique for the analysis of images. Contrast enhancement is one of a method to enhance low contrast images obtained under poor lighting and fog conditions. In this paper, various variants of histogram equalisation, Homomorphic filtering and dark channel prior techniques used for image enhancement are reviewed and presented. Real-time processing of images is implemented on Field Programmable Gate Array (FPGA) to increase the computing speed. Further this paper focus on the review of contrast enhancement techniques implemented on FPGA in terms of device utilization and processing time.
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ABSTRACT Face recognition system have been widely developed. The machine vision system becomes an interest of many researchers in various fields of science. It provides the most important characteristic of natural interaction that is... more
ABSTRACT Face recognition system have been widely developed. The machine vision system becomes an interest of many researchers in various fields of science. It provides the most important characteristic of natural interaction that is personalization. Automatic face recognition is a challenging problem, since human faces have a complex pattern. This paper presents a method for recognition of frontal human faces on gray scale images. The system is developed so that user can access the room just stand in front of the webcam. The webcam will send the image captured to the computer for recognition. In the proposed method, Discrete Cosine Transform (DCT) is used to extract the facial feature of an image the distance between the of the test image and train I and Euclidean Classifier is used to for the selection of best match between test image and trained image that has already been stored in database. When match occurred, computer will send signal to the microcontroller to open the lock through UART, else computer will send the unrecognized image to the owner’s mobile. When owner wants to open the door for the visitor then, using his mobile owner will send signal to the microcontroller to unlock the door. This paper will develop an intelligent door based on smart phone that can be implemented in real-life applications.
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Video enhancement becomes a very challenging problem under low lighting conditions. Numerous techniques for enhancing visual quality of videos/images captured under different environmental situations are proposed by number of researchers... more
Video enhancement becomes a very challenging problem under low lighting conditions. Numerous techniques for enhancing visual quality of videos/images captured under different environmental situations are proposed by number of researchers especially in dark or night time, foggy situations, rainy and so on. This paper discusses brief review of existing algorithms related to video enhancement techniques under various lighting condition such as De-hazing based enhancement algorithm, a novel integrated algorithm, gradient based fusion algorithm and dark channel prior and in addition it also presents advantages and disadvantages of these algorithms.
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Preservation of details in an image while denoising is a significant part in the preprocessing stage of digital image processing. Since the past two decades, various filters have been proposed by numerous researchers to address the common... more
Preservation of details in an image while denoising is a significant part in the preprocessing stage of digital image processing. Since the past two decades, various filters have been proposed by numerous researchers to address the common problem of preserving the edges during denoising. Most of the edge preserving denoising filters are suitable only for low contrast images and it's applicability has progressed significantly for variety of images. This survey provides a compact summary of the different nonlinear filters for details preservation in the spatial domain. Some of the filters decline from intrinsic theoretical limitations that triggers dramatic instability in the presence of impulsive noise and shadowed images. The comparisons of visual results of the filters demonstrates similar outputs, but often deteriorate with artifacts and halos. As is evident from the review of related work, the Guided image filter is agile and extensively used edge preserving denoising filter. They are beneficial in edge preserving denoising, high dynamic range compression, image feathering, haze removal, joint upsampling and many such applications as their computational complexity is independent of the filtering kernel size.
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"In this Paper, Segmentation of deformable objects using our proposed method is discussed. Segmentation of deformable object is very much useful specially in case of medical image processing applications. This paper represent methods... more
"In this Paper, Segmentation of deformable objects using our proposed method is discussed. Segmentation of deformable object is very much useful specially in case of medical image processing applications. This paper represent methods based on Radial Active Ray method, Active contour model with Gradient vector flow field. Segmentation of deformable objects is very difficult for medical images due to poor resolution and weak contrast, but still these techniques gives correct result and the result of these techniques is useful in further processing like deformable object tracking in Laproscopic surgery. "
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ABSTRACT In medical image processing, retinal image enhancement is the challenging issue to reveal the unseen details of an retinal image, thus, in many applications image enhancement issued to solve the challenges such as, noise... more
ABSTRACT In medical image processing, retinal image enhancement is the challenging issue to reveal the unseen details of an retinal image, thus, in many applications image enhancement issued to solve the challenges such as, noise reduction, blurring, degradation, etc. To improve the visual grade of retinal images we have many alternative image enhancement techniques that are suitable for specific application. This paper presents an overview of various retinal image enhancement techniques that will process the original Retinal image to obtain enhanced image suitable for a specific application. The method used in this paper has been evaluated with help of PSNR image Quality measure which is applied over several retinal images which is obtained from the datasets such as DRIVE, STARE and few other’s provided by local medical experts. The comparative experimental results indicate that our proposed enhanced method has better outcome.
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The intention of this paper is to review various face detection and recognition methods, sort them into different categories and distinguish innovative trends. In this connection the face detection and recognition in video streams is the... more
The intention of this paper is to review various face detection and recognition methods, sort them into different categories and distinguish innovative trends. In this connection the face detection and recognition in video streams is the foremost significant step of information drawing out in many computer vision and image processing applications. Detecting and recognition of face in video stream in generally being a challenging problem, provides an enormous attention for recognition, classification, and activity analysis, making these later steps more efficient. Naturally, assumptions are made to constrain the detection and recognition problem in the perspective of a particular application. In this survey, investigation of many existing schemes in the literature of recent developments and general strategies of all these stages are done and limitations of the various methods and outline promising directions of research are discussed.
Research Interests:
The increase in availability of high performance, low-priced, portable digital imaging devices has created an opportunity for supplementing traditional scanning for document image acquisition. Cameras attached to cellular phones, wearable... more
The increase in availability of high performance, low-priced, portable digital imaging devices has created an opportunity for supplementing traditional scanning for document image acquisition. Cameras attached to cellular phones, wearable computers, and standalone image or video devices are highly mobile and easy to use; they can capture images making them much more versatile than desktop scanners. Should gain solutions to the analysis of documents captured with such devices become available, there will clearly be a demand in many domains. Images captured from images can suffer from low resolution, perspective distortion, and blur, as well as a complex layout and interaction of the content and background. In this paper, we present a survey of application domains and technical challenges for the analysis of documents captured by digital cameras. Each method is discussed in brief and then compared against other approaches.
Research Interests:
A new design and novel architecture suitable for FPGA/ASIC implementation of a 2D Gaussian surround function for image processing application is presented in this paper. The proposed scheme results in enormous savings of memory normally... more
A new design and novel architecture suitable for FPGA/ASIC implementation of a 2D Gaussian surround function for image processing application is presented in this paper. The proposed scheme results in enormous savings of memory normally required for 2D Gaussian function implementation. In the present work, the Gaussian symmetric characteristics which quickly falls off toward plus/minus infinity has been used in order to save the memory. The 2D Gaussian function implementation is presented for use in applications such as image enhancement, smoothing, edge detection and filtering etc. The FPGA implementation of the proposed 2D Gaussian function is capable of processing (blurring, smoothing, and convolution) high resolution color pictures of size up to $1600\times1200$ pixels at the real time video rate of 30 frames/sec. The Gaussian design exploited here has been used in the core part of retinex based color image enhancement. Therefore, the design presented produces Gaussian output wi...
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Research Interests:
ABSTRACT Automatic moving object detection and tracking is very important task in video surveillance applications. In the present work the well known background subtraction model and use of Gaussian Mixture Models (GMM) have been used to... more
ABSTRACT Automatic moving object detection and tracking is very important task in video surveillance applications. In the present work the well known background subtraction model and use of Gaussian Mixture Models (GMM) have been used to implement a robust automated single object tracking system. In this implementation, background subtraction on subtracting consecutive frame-by-frame basis for moving object detection is done. Once the object has been detected it is tracked by employing an efficient GMM technique. After successful completion of tracking, moving object recognition of those objects using well known Principal Component Analysis (PCA), which is used for extracting features and Manhattan based distance metric is used for subsequent classification purpose. The system is capable of handling entry and exit of an object. Such a tracking system is cost effective and can be used as an automated video conferencing system and also has applications like human tracking, vehicles monitoring, and event recognition for video surveillance. The proposed algorithm was tested on standard database on complex environments and the results were satisfactory.
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ABSTRACT In recent years, automatic moving object detection and tracking is a challenging task for many computer vision applications such as video surveillance, traffic monitoring and activity analysis. In this regard, many methods have... more
ABSTRACT In recent years, automatic moving object detection and tracking is a challenging task for many computer vision applications such as video surveillance, traffic monitoring and activity analysis. In this regard, many methods have been proposed based on different approaches. Despite of its importance, moving object detection and tracking in complex environments is still far from being completely solved for low resolution videos, foggy videos, and also Infrared video sequences. A novel scheme for Moving Object detection based on Tensor Locality Preserving Projections (Ten-LoPP) approach is proposed. Consequently, a Moving Object is tracked based on the centroid and area of a detected object. Numbers of experiments are conducted for indoor and outdoor video sequences of standard PETS, OTCBVS, Videoweb Activities datasets and also our own collected video sequences comprising partial night vision video sequences. Results obtained are satisfactory and competent. Comparative study is performed with existing well known traditional subspace learning methods.
Research Interests:
Research Interests:
ABSTRACT Moving object Detection in video sequences is one among the foremost indispensable challenges in Image and video processing. Its conjoint research areas are activity monitoring and video surveillance application. However, still... more
ABSTRACT Moving object Detection in video sequences is one among the foremost indispensable challenges in Image and video processing. Its conjoint research areas are activity monitoring and video surveillance application. However, still beneath the biological process stage needs robust approaches once applied in an unconstrained environment. Several detection algorithms have higher performance under the static background, however decline results under background with fake motions. Detecting and Tracking of multiple moving objects in presence of Litter background like leaves movement of trees, water waves, fountain, window curtain movement and change of illumination in video sequences is a challenging problem. Because of these little movements within the background, it affects the performance of the automated tracking system. To overcome the above said problem, an approach consisting of Intensity Slicing and Spatial Resolution is considered to attenuate the results caused by the Litter Background. A modified 3-frame difference technique is employed to detect a moving object. Then, Adaptive Thresholding is used to segment the object from the background and to track the object. Results are compared with the existing well known traditional techniques. The proposed technique is tested on standard PETS datasets and our own collected video datasets. The experimental results prove the feasibility and usefulness of the proposed technique.
Object recognition in the video sequence or images is one of the sub-field of computer vision. Moving object recognition from a video sequence is an appealing topic with applications in various areas such as airport safety, intrusion... more
Object recognition in the video sequence or images is one of the sub-field of computer vision. Moving object recognition from a video sequence is an appealing topic with applications in various areas such as airport safety, intrusion surveillance, video monitoring, intelligent highway, etc. Moving object recogni-tion is the most challenging task in intelligent video surveillance system. In this regard, many techniques have been proposed based on different methods. Despite of its importance, moving object recognition in complex environments is still far from being completely solved for low resolution videos, foggy videos, and also dim video sequences. All in all, these make it necessary to develop exceedingly ro-bust techniques. This paper introduces multiple moving object recognition in the video sequence based on LoG Gabor-PCA approach and Angle based distance Sim-ilarity measures techniques used to recognize the object as a human, vehicle etc. Number of experiments are conducted f...
Research Interests:
In this paper, a novel Discriminatively Trained Multi-Source CNN Model (DTM-CNN) is developed for multi-camera based vehicle tracking purpose. DTM-CNN performs pretraining of a gigantically large set of traffic videos to track ground... more
In this paper, a novel Discriminatively Trained Multi-Source CNN Model (DTM-CNN) is developed for multi-camera based vehicle tracking purpose. DTM-CNN performs pretraining of a gigantically large set of traffic videos to track ground truths for retaining region of interest (ROI) representation. Being a multi-source tracking method DTM-CNN embodies shared layers and multiple branches of source-specific layers to perform feature extraction and training. Here, source signifies each camera input with distinct training sequences, where each branch exhibits binary classification for ROI identification and tracking in each source. DTM-CNN trains each source input iteratively to achieve generic ROI representations in the shared layers. When performing tracking in a new sequence, DTM-CNN forms a new network by combining the shared layers with a new binary classification layer, which is updated online. It assists online tracking by retrieving the ROI windows arbitrarily sampled near the previ...
Research Interests:
A new design and novel architecture suitable for FPGA/ASIC implementation of a 2D Gaussian surround function for image processing application is presented in this paper. The proposed scheme results in enormous savings of memory normally... more
A new design and novel architecture suitable for FPGA/ASIC implementation of a 2D Gaussian surround function for image processing application is presented in this paper. The proposed scheme results in enormous savings of memory normally required for 2D Gaussian function implementation. In the present work, the Gaussian symmetric characteristics which quickly falls off toward plus/minus infinity has been used in order to save the memory. The 2D Gaussian function implementation is presented for use in applications such as image enhancement, smoothing, edge detection and filtering etc. The FPGA implementation of the proposed 2D Gaussian function is capable of processing (blurring, smoothing, and convolution) high resolution color pictures of size up to 1600 × 1200 pixels at the real time video rate of 30 frames/sec. The Gaussian design exploited here has been used in the core part of retinex based color image enhancement. Therefore, the design presented produces Gaussian output with th...
Research Interests:
The field of remote sensing and image processing are constantly evolving in the last decade. At present there are many enhancement techniques which are used for remote sensing image processing. The contrast of remote sensing images is... more
The field of remote sensing and image processing are constantly evolving in the last decade. At present there are many enhancement techniques which are used for remote sensing image processing. The contrast of remote sensing images is low, which may include various types of noises. In order to make full use of remote sensing image information, the original image has to be enhanced. This paper presents different technologies which are recently proposed for remote sensing image processing. These techniques overcome the limitations of conventional methods. This represent enhancement methods based on Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT), Multi- Scale Retinex, Singular Value, Adaptive Intensity Transformations, and Dual-Tree Complex Wavelet Transform (DT-CWT). These methods overcome the limitations of the existing methods. Different application area has different requirement for image enhancement. These techniques perform image enhancement in different are...
Computer vision and intelligent video surveillance system are more interesting topics in the field of moving object recognition. The security systems which are effective will have intelligent video surveillance as an integral part of it.... more
Computer vision and intelligent video surveillance system are more interesting topics in the field of moving object recognition. The security systems which are effective will have intelligent video surveillance as an integral part of it. The major worldwide concern is security since any criminal activities occurred across the world. Monitoring such events currently rely on man power and technology; however, in order to avoid human errors by using advanced automatic monitoring technology that can be affected by different reasons. To overcome these shortfalls, the intelligent surveillance system is developed for monitoring multiple moving object recognitions. Object recognition remains challenging due to illumination shadows, changes, and occlusion. All these make it necessary to develop robust approaches. Gabor–PCA approach and distance similarity technique are proposed for multiple moving object recognitions such as a human, vehicle, etc. The proposed approach achieves good recognit...
Breast cancer is ranked second among the leading causes of death affecting females. Statistics have shown that one out of eight (12 %) women are affected by breast cancer in their lifetime. Mammography is the most effective strategy for... more
Breast cancer is ranked second among the leading causes of death affecting females. Statistics have shown that one out of eight (12 %) women are affected by breast cancer in their lifetime. Mammography is the most effective strategy for breast cancer screening and can be used for the early detection of masses or abnormalities. Small clusters of micro calcifications appearing as a collection of white spots on mammograms show an early sign of breast cancer. In digital mammography, electronic image of the breast is taken and is stored directly in a computer. However, early detection of breast cancer is dependent on both the radiologist’s ability to read mammograms and the quality of mammogram images. The aim of this paper is to conduct a review of existing mammogram enhancement techniques. Each method will be discussed in brief and compared against other approaches.
Research Interests:
The field of image processing is constantly evolving in the last decades and it is being used in many applications. At present there are many enhancement techniques are available for contrast enhancement of the image. This paper proposes... more
The field of image processing is constantly evolving in the last decades and it is being used in many applications. At present there are many enhancement techniques are available for contrast enhancement of the image. This paper proposes an effective approach to improve the quality of the image based on the image fusion using the discrete wavelet transform (DWT). Contrast enhancement approach is proposed for images which has low contrast. Contrast enhancement involves transforming the intensity of pixels from the original value to new value. The goal of contrast enhancement is to improve visibility of image details without introducing unwanted visual appearances and/or unwanted artefacts. This paper presents the contract enhancement technique to visualize all available information in an image. Proposed method uses the retinex algorithm and gamma correction technique. It uses the DWT for decomposition of the image and decompose the image into a set of band-limited components, called HH, HL, LH, and LL. LL subband will be fused to get the better result of the image. Finally, inverse DWT will be performed on fused image to achieve the final enhanced image.
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In video surveillance, identification is a very significant element for target tracking, activity recognition, traffic monitoring, military etc. The identification process classifies the pixels into either foreground or background and a... more
In video surveillance, identification is a very significant element for target tracking, activity recognition, traffic monitoring, military etc. The identification process classifies the pixels into either foreground or background and a common approach used to achieve such a classification is background removal. A Novel method is proposed for the moving object detection based on Modular Wavelet approach, where two consecutive image from image sequences are divided into four parts and then, the Wavelet Energy (WE) is applied to each sub image. The sub image in turn has two energy values of WE, namely, the percentage of energy corresponding to the approximation and the detail. Comparing the energy values corresponding to the detail, the moving object is recognized. Since the discrete wavelet transform has a pleasant property that it can divide an image into four different frequency bands without loss of the spatial information and most of the fake motions in the background can be deco...
Breast cancer is the leading cause of deaths among female cancer patients. Breast cancer can be diagnosed in several ways such as imaging or mammography, clinical breast exam, breast self examination and surgery. In breast cancer... more
Breast cancer is the leading cause of deaths among female cancer patients. Breast cancer can be diagnosed in several ways such as imaging or mammography, clinical breast exam, breast self examination and surgery. In breast cancer diagnosis, the radiologist mainly uses their eyes to discern cancer when they screen the mammograms. Mammography is the most effective technique for breast cancer screening and detection of abnormalities. Calcification and masses are most common abnormality found in breast cancer. The aim of this paper is to conduct a comprehensive review of mammogram image enhancement methods to early detection of breast cancer. Keywords—Mammogram enhancement, Image calcification, detection,Breast mass detecion, Dyadic wavelet transform, image enhancement and denoising, Microcalcification detection.
The need for automatic activity detection systems has been elevated since the number of surveillance cameras installed in the surroundings is increased. Automatic activity detection systems can be productively used to cooperate with human... more
The need for automatic activity detection systems has been elevated since the number of surveillance cameras installed in the surroundings is increased. Automatic activity detection systems can be productively used to cooperate with human operators and for offline inspection to generate an on-line alarm in case of abnormal activities. Although the activity detection problem is a trending field belonging to computer vision, automatically characterizing violent scenes has been considerably less studied in the surveillance system which is vindicated by the demand of providing safer surroundings for the public. Thus in the proffered work, a deep neural network model based on an ensemble of the Mask Region-based Convolutional Neural network (Mask RCNN), Key-point detection, and Long Short Term Memory (LSTM) has been put forward to identify single person, violent activities such as Punching, kicking. Former to extract human key-points and mask and later to capture temporal information of the data. The upshot of experiments manifests that the ensemble model can outperform individual models. The proposed approach has managed to accomplish a good accuracy rate of 73.1%, 93.4%, and 86.5% on Weizmann, KTH, and own Dataset respectively. The proposed work is more relevant to the industry, which in turn is helpful in serving society as it deals with security.
Research Interests:
Research Interests:
The increase in availability of high performance, low-priced, portable digital imaging devices has created an opportunity for supplementing traditional scanning for document image acquisition. Cameras attached to cellular phones,... more
The increase in availability of high performance, low-priced, portable digital imaging devices has created an opportunity for supplementing traditional scanning for document image acquisition. Cameras attached to cellular phones, wearable computers, and standalone image or video devices are highly mobile and easy to use; they can capture images making them much more versatile than desktop scanners. Should gain solutions to the analysis of documents captured with such devices become available, there will clearly be a demand in many domains. Images captured from images can suffer from low resolution, perspective distortion, and blur, as well as a complex layout and interaction of the content and background. In this paper, we present a survey of application domains and technical challenges for the analysis of documents captured by digital cameras. Each method is discussed in brief and then compared against other approaches.
Research Interests:
Breast cancer is ranked second among the leading causes of death affecting females. Statistics have shown that one out of eight (12 %) women are affected by breast cancer in their lifetime. Mammography is the most effective strategy for... more
Breast cancer is ranked second among the leading causes of death affecting females. Statistics have shown that one out of eight (12 %) women are affected by breast cancer in their lifetime. Mammography is the most effective strategy for breast cancer screening and can be used for the early detection of masses or abnormalities. Small clusters of micro calcifications appearing as a collection of white spots on mammograms show an early sign of breast cancer. In digital mammography, electronic image of the breast is taken and is stored directly in a computer. However, early detection of breast cancer is dependent on both the radiologist’s ability to read mammograms and the quality of mammogram images. The aim of this paper is to conduct a review of existing mammogram enhancement techniques. Each method will be discussed in brief and compared against other approaches.
Research Interests:
In recent years, the numbers of Visual Surveillance systems have greatly increased, and these systems have developed into intellectual systems that automatically detect, track, and recognize objects in video. Automatic moving object... more
In recent years, the numbers of Visual Surveillance systems have greatly increased, and these systems have developed into intellectual systems that automatically detect, track, and recognize objects in video. Automatic moving object detection and tracking is a very challenging task in video surveillance applications. In this regard, many methods have been proposed for Moving Object Detection and Tracking based on edge, color, texture information. Due to unpredictable characteristics of objects in foggy videos, the task of object detection remains a challenging problem. In this paper, we propose a novel scheme for moving object detection based on Log Gabor filter (LGF) and Dominant Eigen Map (DEM) approaches. Location of the moving object is obtained by performing connected component analysis. In turn, a Moving Object is Tracked based on the centroid manipulation. Number of experiments is performed using indoor and outdoor video sequences. The proposed method is tested on standard PETS datasets and many real time video sequences. Results obtained are satisfactory and are compared with existing well known traditional methods.
Research Interests:
Research Interests:
The intention of this paper is to review various face detection and recognition methods, sort them into different categories and distinguish innovative trends. In this connection the face detection and recognition in video streams... more
The intention of this paper is to review various
face detection and recognition methods, sort them into
different categories and distinguish innovative trends. In
this connection the face detection and recognition in video
streams is the foremost significant step of information
drawing out in many computer vision and image
processing applications. Detecting and recognition of face
in video stream in generally being a challenging problem,
provides an enormous attention for recognition,
classification, and activity analysis, making these later
steps more efficient. Naturally, assumptions are made to
constrain the detection and recognition problem in the
perspective of a particular application. In this survey,
investigation of many existing schemes in the literature of
recent developments and general strategies of all these
stages are done and limitations of the various methods and
outline promising directions of research are discussed.
face detection and recognition methods, sort them into
different categories and distinguish innovative trends. In
this connection the face detection and recognition in video
streams is the foremost significant step of information
drawing out in many computer vision and image
processing applications. Detecting and recognition of face
in video stream in generally being a challenging problem,
provides an enormous attention for recognition,
classification, and activity analysis, making these later
steps more efficient. Naturally, assumptions are made to
constrain the detection and recognition problem in the
perspective of a particular application. In this survey,
investigation of many existing schemes in the literature of
recent developments and general strategies of all these
stages are done and limitations of the various methods and
outline promising directions of research are discussed.
Research Interests:
The field of remote sensing and image processing are constantly evolving in the last decade. At present there are many enhancement techniques which are used for remote sensing image processing. The contrast of remote sensing images is... more
The field of remote sensing and image processing are constantly evolving in the last decade. At present there are many enhancement techniques which are used for remote sensing image processing. The contrast of remote sensing images is low, which may include various types of noises. In order to make full use of remote sensing image information, the original image has to be enhanced. This paper presents different technologies which are recently proposed for remote sensing image processing. These techniques overcome the limitations of conventional methods. This represent enhancement methods based on Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT), Multi-Scale Retinex, Singular Value, Adaptive Intensity Transformations, and Dual-Tree Complex Wavelet Transform (DT-CWT). These methods overcome the limitations of the existing methods. Different application area has different requirement for image enhancement. These techniques perform image enhancement in different areas and have unique advantages in different image processing fields.
In this Paper, Segmentation of deformable objects using our proposed method is discussed. Segmentation of deformable object is very much useful specially in case of medical image processing applications. This paper represent methods... more
In this Paper, Segmentation of deformable objects using our proposed method is discussed. Segmentation of
deformable object is very much useful specially in case of medical image processing applications. This paper represent
methods based on Radial Active Ray method, Active contour model with Gradient vector flow field. Segmentation of
deformable objects is very difficult for medical images due to poor resolution and weak contrast, but still these techniques
gives correct result and the result of these techniques is useful in further processing like deformable object tracking in
Laproscopic surgery.
deformable object is very much useful specially in case of medical image processing applications. This paper represent
methods based on Radial Active Ray method, Active contour model with Gradient vector flow field. Segmentation of
deformable objects is very difficult for medical images due to poor resolution and weak contrast, but still these techniques
gives correct result and the result of these techniques is useful in further processing like deformable object tracking in
Laproscopic surgery.
Research Interests:
Automatic moving object detection and tracking is very important task in video surveillance applications. In the present work the well known background subtraction model and use of Gaussian Mixture Models (GMM) have been used to implement... more
Automatic moving object detection and tracking is very important task in video surveillance applications. In the present work the well known background subtraction model and use of Gaussian Mixture Models (GMM) have been used to implement a robust automated single object tracking system. In this implementation, background subtraction on subtracting consecutive frame-by-frame basis for moving object detection is done. Once the object has been detected it is tracked by employing an efficient GMM technique. After successful completion of tracking, moving object recognition of those objects using well known Principal Component Analysis (PCA), which is used for extracting features and Manhattan based distance metric is used for subsequent classification purpose. The system is capable of handling entry and exit of an object. Such a tracking system is cost effective and can be used as an automated video conferencing system and also has applications like human tracking, vehicles monitoring, and event recognition for video surveillance. The proposed algorithm was tested on standard database on complex environments and the results were satisfactory.
Automatic moving object detection and tracking is very important task in video surveillance applications. In this paper, we propose a novel scheme for moving object detection based on Locality Preserving Projections (LPP). It is also... more
Automatic moving object detection and tracking is very important task in video surveillance applications. In this paper,
we propose a novel scheme for moving object detection based on Locality Preserving Projections (LPP). It is also known
as Laplacian eigenmaps, which optimally preserves the neighborhood structure of the data set [1]. The proposed method
wastestedonstandardPETS dataset and many real time video sequence and the results was satisfactory.
we propose a novel scheme for moving object detection based on Locality Preserving Projections (LPP). It is also known
as Laplacian eigenmaps, which optimally preserves the neighborhood structure of the data set [1]. The proposed method
wastestedonstandardPETS dataset and many real time video sequence and the results was satisfactory.
Research Interests:
Autonomous video surveillance and monitoring has a rich history. A new method for detecting multiple moving objects based on improved background subtraction model and for tracking is based on feature based approach has proposed. Then... more
Autonomous video surveillance and monitoring has a rich history. A new method for detecting multiple moving objects based on improved background subtraction model and for tracking is based on feature based approach has proposed. Then identified moving objects are also counted, by indexing individually. The proposed algorithm is automatic and efficient in intelligent surveillance applications like vehicles monitoring, event recognition, and crime prevention, etc. The proposed model has proved to be robust in various environments (including indoor and outdoor scenes) and different types of background scenes. Experiments on real scenes show that the algorithm is effective for object detection and tracking.
A new design and novel architecture suitable for FPGA/ASIC implementation of a 2D Gaussian surround function for image processing application is presented in this paper. The proposed scheme results in enor- mous savings of memory normally... more
A new design and novel architecture suitable for FPGA/ASIC implementation of a 2D Gaussian surround function for image processing application is presented in this paper. The proposed scheme results in enor- mous savings of memory normally required for 2D Gaussian function implementation. In the present work, the Gaussian symmetric characteristics which quickly falls off toward plus/minus infinity has been used in order to save the memory. The 2D Gaussian function implementation is presented for use in applications such as image enhancement, smoothing, edge detection and filtering etc. The FPGA implementation of the proposed 2D Gaussian function is capable of processing (blurring, smoothing, and convolution) high resolution color pictures of size upto 1600 × 1200 pixels at the real time video rate of 30 frames/sec. The Gaussian design exploited here has been used in the core part of retinex based color image enhancement. Therefore, the design presented produces Gaussian output with three different scales, namely, 16, 64 and 128. The design was coded in Verilog, a popular hardware design language used in industries, conforming to RTL coding guidelines and fits onto a single chip with a gate count utilization of 89,213 gates. Experimental results presented confirms that the proposed method offers a new approach for development of large sized Gaussian pyramid while reducing the on-chip memory utilization.