This document summarizes research on optimized fingerprint compression without loss of data. It discusses how fingerprint recognition works by extracting minutiae features from fingerprints. It then describes the proposed fingerprint recognition method using minutiae score matching (FRMSM), which uses block filtering for thinning fingerprints to preserve image quality while extracting minutiae. Experimental results showed the false matching ratio was better than existing algorithms. The document also provides background on biometric systems and fingerprint recognition. It reviews related work on fingerprint enhancement, orientation field estimation, and minutiae extraction. The proposed system describes a line extraction and graph matching approach for fingerprint matching with improved robustness. Modules for the system include authentication, image capturing, fingerprint matching, binarization, and
COMPARATIVE ANALYSIS OF MINUTIAE BASED FINGERPRINT MATCHING ALGORITHMSijcsit
Biometric matching involves finding similarity between fingerprint images.The accuracy and speed of the
matching algorithmdetermines its effectives. This researchaims at comparing two types of matching
algorithms namely(a) matching using global orientation features and (b) matching using minutia triangulation.The comparison is done using accuracy, time and number of similar features. The experiment is conducted on a datasets of 100 candidates using four (4) fingerprints from each candidate. The data is sampled from a mass registration conducted by a reputable organization in Kenya.Theresearch reveals that fingerprint matching based on algorithm (b) performs better in speed with an average of 38.32 milliseconds
as compared to matching based on algorithm (a) with an average of 563.76 milliseconds. On accuracy,algorithm(a) performs better with an average accuracy of 0.142433 as compared to algorithm (b) with an average accuracy score of 0.004202.
Passive Image Forensic Method to Detect Resampling Forgery in Digital Imagesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
An efficient method for recognizing the low quality fingerprint verification ...IJCI JOURNAL
In this paper, we propose an efficient method to provide personal identification using fingerprint to get better accuracy even in noisy condition. The fingerprint matching based on the number of corresponding minutia pairings, has been in use for a long time, which is not very efficient for recognizing the low quality fingerprints. To overcome this problem, correlation technique is used. The correlation-based fingerprint verification system is capable of dealing with low quality images from which no minutiae can be extracted reliably and with fingerprints that suffer from non-uniform shape distortions, also in case of damaged and partial images. Orientation Field Methodology (OFM) has been used as a preprocessing module, and it converts the images into a field pattern based on the direction of the ridges, loops and bifurcations in the image of a fingerprint. The input image is then Cross Correlated (CC) with all the images in the cluster and the highest correlated image is taken as the output. The result gives a good recognition rate, as the proposed scheme uses Cross Correlation of Field Orientation (CCFO = OFM + CC) for fingerprint identification.
Gesture Recognition Review: A Survey of Various Gesture Recognition AlgorithmsIJRES Journal
This document reviews various algorithms for gesture recognition from images and video. It discusses approaches such as pixel-by-pixel comparison, edge detection, orientation histograms, thinning, hidden Markov models, color space segmentation using YUV and tracking using CAMSHIFT, naive Bayes classification, 3D hand modeling, appearance-based modeling using eigenvectors, finite state machines, and particle filtering using condensation. It evaluates these methods and concludes that combining YUV segmentation, CAMSHIFT tracking and hidden Markov modeling provides an effective approach for hand detection and gesture recognition.
A new technique to fingerprint recognition based on partial windowAlexander Decker
1) The document presents a new technique for fingerprint recognition based on analyzing a partial window around the core point of a fingerprint.
2) The technique first locates the core point of a fingerprint, then determines a window around the core point. Features are extracted from this window and input into an artificial neural network (ANN) to recognize fingerprints.
3) The technique aims to reduce computation time for fingerprint recognition by focusing the analysis on a partial window rather than the whole fingerprint image.
This document presents a finger vein pattern recognition security system. It uses near-infrared imaging to capture images of finger vein patterns, which are then analyzed using a self-adaptive illuminance control algorithm and Gabor filters for feature extraction. The extracted features are then used for personal identification via a neural network classifier. The system aims to provide accurate, fast, and secure biometric authentication using unique vein patterns in fingers.
A Dualistic Sub-Image Histogram Equalization Based Enhancement and Segmentati...inventy
This document presents a dualistic sub-image histogram equalization technique for medical image enhancement and segmentation. The technique divides an image histogram into two parts based on mean and median, then equalizes each sub-histogram independently. It enhances images effectively while constraining average luminance shift. For segmentation, canny edge detection and neural networks are used. The technique is tested on medical images and shows improved completeness and correctness over previous methods, with neural networks increasing accuracy to 98.3257%.
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
COMPARATIVE ANALYSIS OF MINUTIAE BASED FINGERPRINT MATCHING ALGORITHMSijcsit
Biometric matching involves finding similarity between fingerprint images.The accuracy and speed of the
matching algorithmdetermines its effectives. This researchaims at comparing two types of matching
algorithms namely(a) matching using global orientation features and (b) matching using minutia triangulation.The comparison is done using accuracy, time and number of similar features. The experiment is conducted on a datasets of 100 candidates using four (4) fingerprints from each candidate. The data is sampled from a mass registration conducted by a reputable organization in Kenya.Theresearch reveals that fingerprint matching based on algorithm (b) performs better in speed with an average of 38.32 milliseconds
as compared to matching based on algorithm (a) with an average of 563.76 milliseconds. On accuracy,algorithm(a) performs better with an average accuracy of 0.142433 as compared to algorithm (b) with an average accuracy score of 0.004202.
Passive Image Forensic Method to Detect Resampling Forgery in Digital Imagesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
An efficient method for recognizing the low quality fingerprint verification ...IJCI JOURNAL
In this paper, we propose an efficient method to provide personal identification using fingerprint to get better accuracy even in noisy condition. The fingerprint matching based on the number of corresponding minutia pairings, has been in use for a long time, which is not very efficient for recognizing the low quality fingerprints. To overcome this problem, correlation technique is used. The correlation-based fingerprint verification system is capable of dealing with low quality images from which no minutiae can be extracted reliably and with fingerprints that suffer from non-uniform shape distortions, also in case of damaged and partial images. Orientation Field Methodology (OFM) has been used as a preprocessing module, and it converts the images into a field pattern based on the direction of the ridges, loops and bifurcations in the image of a fingerprint. The input image is then Cross Correlated (CC) with all the images in the cluster and the highest correlated image is taken as the output. The result gives a good recognition rate, as the proposed scheme uses Cross Correlation of Field Orientation (CCFO = OFM + CC) for fingerprint identification.
Gesture Recognition Review: A Survey of Various Gesture Recognition AlgorithmsIJRES Journal
This document reviews various algorithms for gesture recognition from images and video. It discusses approaches such as pixel-by-pixel comparison, edge detection, orientation histograms, thinning, hidden Markov models, color space segmentation using YUV and tracking using CAMSHIFT, naive Bayes classification, 3D hand modeling, appearance-based modeling using eigenvectors, finite state machines, and particle filtering using condensation. It evaluates these methods and concludes that combining YUV segmentation, CAMSHIFT tracking and hidden Markov modeling provides an effective approach for hand detection and gesture recognition.
A new technique to fingerprint recognition based on partial windowAlexander Decker
1) The document presents a new technique for fingerprint recognition based on analyzing a partial window around the core point of a fingerprint.
2) The technique first locates the core point of a fingerprint, then determines a window around the core point. Features are extracted from this window and input into an artificial neural network (ANN) to recognize fingerprints.
3) The technique aims to reduce computation time for fingerprint recognition by focusing the analysis on a partial window rather than the whole fingerprint image.
This document presents a finger vein pattern recognition security system. It uses near-infrared imaging to capture images of finger vein patterns, which are then analyzed using a self-adaptive illuminance control algorithm and Gabor filters for feature extraction. The extracted features are then used for personal identification via a neural network classifier. The system aims to provide accurate, fast, and secure biometric authentication using unique vein patterns in fingers.
A Dualistic Sub-Image Histogram Equalization Based Enhancement and Segmentati...inventy
This document presents a dualistic sub-image histogram equalization technique for medical image enhancement and segmentation. The technique divides an image histogram into two parts based on mean and median, then equalizes each sub-histogram independently. It enhances images effectively while constraining average luminance shift. For segmentation, canny edge detection and neural networks are used. The technique is tested on medical images and shows improved completeness and correctness over previous methods, with neural networks increasing accuracy to 98.3257%.
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
This document discusses the implementation of a fingerprint matching algorithm. It begins with an introduction to fingerprint recognition and matching. It then discusses the literature on fingerprint matching algorithms. The proposed algorithm involves three main steps: fingerprint pre-processing (including enhancement and binarization), minutiae extraction, and post-processing (including false minutiae removal). Experimental results on the FVC2002 database show that the proposed algorithm has a lower matching time and better accuracy rates compared to an existing method. The algorithm is concluded to be effective for fingerprint image identification.
Facial expression recongnition Techniques, Database and Classifiers Rupinder Saini
This document discusses various techniques for facial expression recognition including eigenface approach, principal component analysis (PCA), Gabor wavelets, PCA with singular value decomposition, independent component analysis with PCA, local Gabor binary patterns, and support vector machines. It describes databases commonly used for facial expression recognition research and classifiers such as Euclidean distance, backpropagation neural networks, PCA, and linear discriminant analysis. The document concludes that combining multiple techniques can achieve more accurate facial expression recognition compared to individual techniques alone by extracting relevant features and evaluating results.
MULTI SCALE ICA BASED IRIS RECOGNITION USING BSIF AND HOG sipij
Iris is a physiological biometric trait, which is unique among all biometric traits to recognize person
effectively. In this paper we propose Multi-scale Independent Component Analysis (ICA) based Iris
Recognition using Binarized Statistical Image Features (BSIF) and Histogram of Gradient orientation
(HOG). The Left and Right portion is extracted from eye images of CASIA V 1.0 database leaving top and
bottom portion of iris. The multi-scale ICA filter sizes of 5X5, 7X7 and 17X17 are used to correlate with
iris template to obtain BSIF. The HOGs are applied on BSIFs to extract initial features. The final feature is
obtained by fusing three HOGs. The Euclidian Distance is used to compare the final feature of database
image with test image final features to compute performance parameters. It is observed that the
performance of the proposed method is better compared to existing methods.
Detection and Tracking of Objects: A Detailed StudyIJEACS
Detecting and tracking objects are the most widespread and challenging tasks that a surveillance system must achieve to determine expressive events and activities, and automatically interpret and recover video content. An object can be a queue of people, a human, a head or a face. The goal of this article is to state the Detecting and tracking methods, classify them into different categories, and identify new trends, we introduce main trends and provide method to give a perception to fundamental ideas as well as to show their limitations in the object detection and tracking for more effective video analytics.
Our life’s important part is Image. Without disturbing its overall structure of images, we can
remove the unwanted part of image with the help of image inpainting. There is simpler the inpainting of
the low resolution images than that of the high resolution images. In this system low resolution image
contained in different super resolution image inpainting methodologies and there are combined all these
methodologies to form the highly in painted image results. For this reason our system uses the super
resolution algorithm which is responsiblefor inpainting of singleimage.
This document discusses using wavelet domain saliency maps for secret communication in RGB images. It proposes a method to compute saliency maps using both approximation and detail coefficients from discrete wavelet transforms of the color channels. Higher numbers of secret bits would be embedded in less salient regions according to the saliency map. The saliency map approach is compared to other methods and could make steganography more secure by embedding data in less noticeable image regions.
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.
This document summarizes a research paper that proposes an improved deconvolution algorithm to estimate blood flow velocity in nailfold vessels more accurately. The paper describes limitations in existing algorithms related to blurring and proposes using deconvolution and other image enhancement techniques. Results show the new algorithm takes less time (20-21 seconds vs 42-43 seconds) and tracks particle movement more accurately, allowing more precise flow measurements. This helps diagnosis of diseases. Future work could involve additional segmentation and machine learning to further automate and improve reliability.
Iaetsd latent fingerprint recognition and matchingIaetsd Iaetsd
The document discusses latent fingerprint recognition and matching using statistical texture analysis. It proposes extracting three statistical features from fingerprints - entropy coefficient from intensity histogram, correlation coefficient using Wiener filter, and wavelet energy coefficient from 5-level wavelet decomposition. These features are used to represent fingerprints mathematically and provide efficient fingerprint recognition. Existing fingerprint recognition methods are also discussed, including those based on minutiae matching and dealing with nonlinear distortions. However, these do not fully address the problem. The proposed statistical analysis approach can provide more accurate recognition results.
Fingerprint Registration Using Zernike Moments : An Approach for a Supervised...CSCJournals
In this work, we deal with contactless fingerprint biometrics. More specifically, we are interested in solving the problem of registration by taking into consideration some constraints such as finger rotation and translation. In the proposed method, the registration requires: (1) a segmentation technique to extract streaks, (2) a skeletonization technique to extract the center line streaks and (3) and landmarks extraction technique. The correspondence between the sets of control points, is obtained by calculating the descriptor vector of Zernike moments on a window of size RxR centered at each point. Comparison of correlation coefficients between the descriptor vectors of Zernike moments helps define the corresponding points. The estimation of parameters of the existing deformation between images is performed using RANSAC algorithm (Random SAmple Consensus) that suppresses wrong matches. Finally, performance evaluation is achieved on a set of fingerprint images where promising results are reported.
The document discusses techniques for object recognition in images. It begins by outlining some of the challenges in object recognition, such as varying lighting, position, scale, and occlusion. It then describes several common object recognition techniques:
1. Template matching involves comparing images to stored templates but can be affected by changes in lighting, position, etc.
2. Color-based techniques use color histograms to match objects but require photometric invariance.
3. Local features represent objects with descriptors of local image patches but have limitations, while global features provide better recognition but are more complex to extract.
4. Shape-based methods match edge maps and contours between images and templates but require good segmentation.
The document
Review of three categories of fingerprint recognition 2prjpublications
This document reviews three categories of fingerprint recognition techniques: correlation-based, minutiae-based, and pattern-based. Minutiae-based matching is the most popular as minutiae points require less storage than images but it is more time-consuming than other methods. The correlation-based method matches entire fingerprint images and handles poor quality prints better but is computationally expensive. Pattern-based matching compares fingerprint swirl/loop patterns but requires consistent image alignment. Challenges include enhancing low-quality images and improving feature extraction, matching, and alignment algorithms.
A Review on Classification Based Approaches for STEGanalysis DetectionEditor IJCATR
This document summarizes two approaches for image steganalysis detection. The first approach proposes novel steganalysis algorithms based on how data hiding affects the rate-distortion characteristics of images. Features are extracted based on increased image entropy and small, imperceptible distortions from data embedding. A Bayesian classifier is then trained on these features. The second approach uses contourlet transform to represent images. It extracts features based on the first four normalized statistical moments of high and low frequency subbands and structural similarity measure of medium frequency subbands. A non-linear support vector machine is then used for classification. Experimental results show the proposed approaches can efficiently detect stego images with high accuracy and low computational cost.
Optimization of Macro Block Size for Adaptive Rood Pattern Search Block Match...IJERA Editor
In area of video compression, Motion Estimation is one of the most important modules and play an important role
to design and implementation of any the video encoder. It consumes more than 85% of video encoding time due to
searching of a candidate block in the search window of the reference frame. Various block matching methods have
been developed to minimize the search time. In this context, Adaptive Rood Pattern Search is one of the less
expensive block matching methods, which is widely acceptable for better Motion Estimation in video data
processing. In this paper we have proposed to optimize the macro block size used in adaptive rood pattern search
method for improvement in motion estimation.
This project includes two face recognition systems implemented with the help of Principal Component Analysis (PCA) and Morphological Shared-Weight Neural Network(MSNN).From these systems we will evaluate the performance of both the techniques and based on the accuracy achieved we determine which technique will be better for the face recognition
Steganography Using Reversible Texture Synthesis1crore projects
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IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document summarizes a research paper that proposes a new image interpolation technique to reconstruct high-resolution images from low-resolution counterparts while preserving edge structures. The technique estimates each pixel to be interpolated in two orthogonal directions and fuses the estimates using linear minimum mean square error estimation. This adaptive fusion approach can better discriminate edge directions in the local window compared to interpolating in a single direction. The technique aims to improve on traditional linear interpolation methods by adapting to local image gradients to reduce artifacts while preserving sharp edges. A simplified version is also presented to reduce computational costs with minimal impact on performance. Experiments showed the new technique can better preserve edges and reduce artifacts compared to other methods.
This document is a report on real-time 3D segmentation authored by three students - Gunjan Kumar Singh, Saurabh Bhardwaj, and Divya Sanghi. It was prepared for Practice School-I at CEERI under the guidance of Dr. Jagdish Raheja. The report describes an algorithm for segmenting cluttered 3D scenes in real-time by first segmenting depth images into surface patches and then combining surface patches into object hypotheses using adjacency, co-planarity, and curvature matching while handling occlusion. Code implementation details and results are also provided.
This document summarizes a research paper that proposes a new method for finger image identification using score-level fusion of finger vein and fingerprint images. The proposed system captures finger vein and low-resolution fingerprint images simultaneously and combines them using a novel score-level fusion strategy. This approach is found to have better identification performance than existing finger vein-only methods. The paper develops and evaluates two new score-level combination methods called holistic and nonlinear fusion, and finds they outperform other popular score-level fusion approaches. Preprocessing, feature extraction using Gabor filters, and score-level matching steps are described for both finger vein and fingerprint identification. Experimental results on a large database suggest the proposed multimodal approach has significantly improved identification accuracy over
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
The document proposes a reliable fingerprint matching system using filter-based and Euclidean distance algorithms. It aims to improve accuracy of fingerprint matching by addressing issues caused by fingertip surface conditions and image quality. The proposed system extracts minutiae points using Gabor filters and matches fingerprints based on minutiae configuration and pore distances calculated using k-nearest neighbors algorithm. Testing on 20 fingerprints showed an average matching accuracy of 95-99% using this approach.
This document discusses the implementation of a fingerprint matching algorithm. It begins with an introduction to fingerprint recognition and matching. It then discusses the literature on fingerprint matching algorithms. The proposed algorithm involves three main steps: fingerprint pre-processing (including enhancement and binarization), minutiae extraction, and post-processing (including false minutiae removal). Experimental results on the FVC2002 database show that the proposed algorithm has a lower matching time and better accuracy rates compared to an existing method. The algorithm is concluded to be effective for fingerprint image identification.
Facial expression recongnition Techniques, Database and Classifiers Rupinder Saini
This document discusses various techniques for facial expression recognition including eigenface approach, principal component analysis (PCA), Gabor wavelets, PCA with singular value decomposition, independent component analysis with PCA, local Gabor binary patterns, and support vector machines. It describes databases commonly used for facial expression recognition research and classifiers such as Euclidean distance, backpropagation neural networks, PCA, and linear discriminant analysis. The document concludes that combining multiple techniques can achieve more accurate facial expression recognition compared to individual techniques alone by extracting relevant features and evaluating results.
MULTI SCALE ICA BASED IRIS RECOGNITION USING BSIF AND HOG sipij
Iris is a physiological biometric trait, which is unique among all biometric traits to recognize person
effectively. In this paper we propose Multi-scale Independent Component Analysis (ICA) based Iris
Recognition using Binarized Statistical Image Features (BSIF) and Histogram of Gradient orientation
(HOG). The Left and Right portion is extracted from eye images of CASIA V 1.0 database leaving top and
bottom portion of iris. The multi-scale ICA filter sizes of 5X5, 7X7 and 17X17 are used to correlate with
iris template to obtain BSIF. The HOGs are applied on BSIFs to extract initial features. The final feature is
obtained by fusing three HOGs. The Euclidian Distance is used to compare the final feature of database
image with test image final features to compute performance parameters. It is observed that the
performance of the proposed method is better compared to existing methods.
Detection and Tracking of Objects: A Detailed StudyIJEACS
Detecting and tracking objects are the most widespread and challenging tasks that a surveillance system must achieve to determine expressive events and activities, and automatically interpret and recover video content. An object can be a queue of people, a human, a head or a face. The goal of this article is to state the Detecting and tracking methods, classify them into different categories, and identify new trends, we introduce main trends and provide method to give a perception to fundamental ideas as well as to show their limitations in the object detection and tracking for more effective video analytics.
Our life’s important part is Image. Without disturbing its overall structure of images, we can
remove the unwanted part of image with the help of image inpainting. There is simpler the inpainting of
the low resolution images than that of the high resolution images. In this system low resolution image
contained in different super resolution image inpainting methodologies and there are combined all these
methodologies to form the highly in painted image results. For this reason our system uses the super
resolution algorithm which is responsiblefor inpainting of singleimage.
This document discusses using wavelet domain saliency maps for secret communication in RGB images. It proposes a method to compute saliency maps using both approximation and detail coefficients from discrete wavelet transforms of the color channels. Higher numbers of secret bits would be embedded in less salient regions according to the saliency map. The saliency map approach is compared to other methods and could make steganography more secure by embedding data in less noticeable image regions.
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.
This document summarizes a research paper that proposes an improved deconvolution algorithm to estimate blood flow velocity in nailfold vessels more accurately. The paper describes limitations in existing algorithms related to blurring and proposes using deconvolution and other image enhancement techniques. Results show the new algorithm takes less time (20-21 seconds vs 42-43 seconds) and tracks particle movement more accurately, allowing more precise flow measurements. This helps diagnosis of diseases. Future work could involve additional segmentation and machine learning to further automate and improve reliability.
Iaetsd latent fingerprint recognition and matchingIaetsd Iaetsd
The document discusses latent fingerprint recognition and matching using statistical texture analysis. It proposes extracting three statistical features from fingerprints - entropy coefficient from intensity histogram, correlation coefficient using Wiener filter, and wavelet energy coefficient from 5-level wavelet decomposition. These features are used to represent fingerprints mathematically and provide efficient fingerprint recognition. Existing fingerprint recognition methods are also discussed, including those based on minutiae matching and dealing with nonlinear distortions. However, these do not fully address the problem. The proposed statistical analysis approach can provide more accurate recognition results.
Fingerprint Registration Using Zernike Moments : An Approach for a Supervised...CSCJournals
In this work, we deal with contactless fingerprint biometrics. More specifically, we are interested in solving the problem of registration by taking into consideration some constraints such as finger rotation and translation. In the proposed method, the registration requires: (1) a segmentation technique to extract streaks, (2) a skeletonization technique to extract the center line streaks and (3) and landmarks extraction technique. The correspondence between the sets of control points, is obtained by calculating the descriptor vector of Zernike moments on a window of size RxR centered at each point. Comparison of correlation coefficients between the descriptor vectors of Zernike moments helps define the corresponding points. The estimation of parameters of the existing deformation between images is performed using RANSAC algorithm (Random SAmple Consensus) that suppresses wrong matches. Finally, performance evaluation is achieved on a set of fingerprint images where promising results are reported.
The document discusses techniques for object recognition in images. It begins by outlining some of the challenges in object recognition, such as varying lighting, position, scale, and occlusion. It then describes several common object recognition techniques:
1. Template matching involves comparing images to stored templates but can be affected by changes in lighting, position, etc.
2. Color-based techniques use color histograms to match objects but require photometric invariance.
3. Local features represent objects with descriptors of local image patches but have limitations, while global features provide better recognition but are more complex to extract.
4. Shape-based methods match edge maps and contours between images and templates but require good segmentation.
The document
Review of three categories of fingerprint recognition 2prjpublications
This document reviews three categories of fingerprint recognition techniques: correlation-based, minutiae-based, and pattern-based. Minutiae-based matching is the most popular as minutiae points require less storage than images but it is more time-consuming than other methods. The correlation-based method matches entire fingerprint images and handles poor quality prints better but is computationally expensive. Pattern-based matching compares fingerprint swirl/loop patterns but requires consistent image alignment. Challenges include enhancing low-quality images and improving feature extraction, matching, and alignment algorithms.
A Review on Classification Based Approaches for STEGanalysis DetectionEditor IJCATR
This document summarizes two approaches for image steganalysis detection. The first approach proposes novel steganalysis algorithms based on how data hiding affects the rate-distortion characteristics of images. Features are extracted based on increased image entropy and small, imperceptible distortions from data embedding. A Bayesian classifier is then trained on these features. The second approach uses contourlet transform to represent images. It extracts features based on the first four normalized statistical moments of high and low frequency subbands and structural similarity measure of medium frequency subbands. A non-linear support vector machine is then used for classification. Experimental results show the proposed approaches can efficiently detect stego images with high accuracy and low computational cost.
Optimization of Macro Block Size for Adaptive Rood Pattern Search Block Match...IJERA Editor
In area of video compression, Motion Estimation is one of the most important modules and play an important role
to design and implementation of any the video encoder. It consumes more than 85% of video encoding time due to
searching of a candidate block in the search window of the reference frame. Various block matching methods have
been developed to minimize the search time. In this context, Adaptive Rood Pattern Search is one of the less
expensive block matching methods, which is widely acceptable for better Motion Estimation in video data
processing. In this paper we have proposed to optimize the macro block size used in adaptive rood pattern search
method for improvement in motion estimation.
This project includes two face recognition systems implemented with the help of Principal Component Analysis (PCA) and Morphological Shared-Weight Neural Network(MSNN).From these systems we will evaluate the performance of both the techniques and based on the accuracy achieved we determine which technique will be better for the face recognition
Steganography Using Reversible Texture Synthesis1crore projects
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Java Project Domain list 2015
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IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document summarizes a research paper that proposes a new image interpolation technique to reconstruct high-resolution images from low-resolution counterparts while preserving edge structures. The technique estimates each pixel to be interpolated in two orthogonal directions and fuses the estimates using linear minimum mean square error estimation. This adaptive fusion approach can better discriminate edge directions in the local window compared to interpolating in a single direction. The technique aims to improve on traditional linear interpolation methods by adapting to local image gradients to reduce artifacts while preserving sharp edges. A simplified version is also presented to reduce computational costs with minimal impact on performance. Experiments showed the new technique can better preserve edges and reduce artifacts compared to other methods.
This document is a report on real-time 3D segmentation authored by three students - Gunjan Kumar Singh, Saurabh Bhardwaj, and Divya Sanghi. It was prepared for Practice School-I at CEERI under the guidance of Dr. Jagdish Raheja. The report describes an algorithm for segmenting cluttered 3D scenes in real-time by first segmenting depth images into surface patches and then combining surface patches into object hypotheses using adjacency, co-planarity, and curvature matching while handling occlusion. Code implementation details and results are also provided.
This document summarizes a research paper that proposes a new method for finger image identification using score-level fusion of finger vein and fingerprint images. The proposed system captures finger vein and low-resolution fingerprint images simultaneously and combines them using a novel score-level fusion strategy. This approach is found to have better identification performance than existing finger vein-only methods. The paper develops and evaluates two new score-level combination methods called holistic and nonlinear fusion, and finds they outperform other popular score-level fusion approaches. Preprocessing, feature extraction using Gabor filters, and score-level matching steps are described for both finger vein and fingerprint identification. Experimental results on a large database suggest the proposed multimodal approach has significantly improved identification accuracy over
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
The document proposes a reliable fingerprint matching system using filter-based and Euclidean distance algorithms. It aims to improve accuracy of fingerprint matching by addressing issues caused by fingertip surface conditions and image quality. The proposed system extracts minutiae points using Gabor filters and matches fingerprints based on minutiae configuration and pore distances calculated using k-nearest neighbors algorithm. Testing on 20 fingerprints showed an average matching accuracy of 95-99% using this approach.
A review on Development of novel algorithm by combining Wavelet based Enhance...IJSRD
Data mining is a technology used in different disciplines to search for significant relationships among variables in n number of data sets. Data mining is frequently used in all types’ areas as well as applications. In this paper the application of data mining is attached with the field of education. The relationship between student’s university entrance examination results and their success was studied using cluster analysis and k-means algorithm techniques.
A review on Development of novel algorithm by combining Wavelet based Enhance...IJSRD
The conventional Canny edge recognition calculation issensitive to commotion, along these lines, it's anything but difficult to lose powerless edge information when sifting through the clamor, and its settled parameters show poor versatility. In light of these issues, this paper proposed an enhanced calculation in view of Canny algorithm. This calculation presented the idea of gravitational field intensity to supplant picture inclination, and acquired the gravitational field force administrator. Two versatile threshold selection systems in view of the mean of picture gradient magnitude and standard deviation were advanced for two sorts of run of the mill pictures (one has less edge data, and the other has rich edge data) individually. The improved Canny calculation is straightforward and simple to figure it out. Experimental results demonstrate that the calculation can protect more valuable edge information and more vigorous to clamor.
Highly Secured Bio-Metric Authentication Model with Palm Print IdentificationIJERA Editor
The document presents a highly secured palm print authentication system using undecimated bi-orthogonal wavelet (UDBW) transform. The proposed system has three main modules: registration, testing, and palm matching. In the registration module, morphological operations and region of interest extraction are used to preprocess palm images. Distance transform and 3-level UDBW transform are then used to extract low-level features and create feature vectors for registered palm prints. In testing, low-level features are extracted from input palm prints using the same approach. Palm matching involves comparing feature vectors of registered and input palm prints to identify matches. Simulation results show the system provides accurate recognition rates for palm print authentication.
Top Cited Articles in Signal & Image Processing 2021-2022sipij
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.
A Review Paper on Real-Time Hand Motion CaptureIRJET Journal
This document reviews various techniques for real-time hand motion capture. It discusses previous work that used particle swarm optimization and convolutional neural networks to estimate hand poses from RGB images or depth maps. More recent approaches use deep learning methods like generative adversarial networks to generate synthetic training data and improve generalization to real images. Current state-of-the-art methods leverage neural rendering and iterative model fitting to estimate 3D hand meshes and poses from single RGB images. These learning-based approaches achieve more accurate and robust real-time hand pose estimation compared to previous optimization-based methods.
Review A DCNN APPROACH FOR REAL TIME UNCONSTRAINED FACE.pptxAravindHari22
1) The document proposes a real-time unconstrained face recognition system using deep convolutional neural networks (DCNN).
2) The system performs face detection, extracts DCNN features, and computes similarity to perform face recognition on images and video frames.
3) It was tested on challenging datasets like CASIA-WebFace, IJB-A, and LFW and was able to achieve accurate recognition with variations in pose, illumination, expression, resolution and occlusion.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
As we know the fingerprint is unique of every living objects. It is quite difficult to find out the prints.
Usually the Forensics use Fine powder and duct tapes to identify the prints of living object. As powder is
exceptionally muddled, so such molecule can cause loss of information after that examination the information is
coordinated with the system. The proposed system consists of an embedded device in which it consists of ultra
light to glow the fingerprints details. After that we can detect the fingerprint, analysis and it will checks on the
database, and it will return the output after matching. For matching and analysis of the Fingerprint, we will be
using the Algorithm for matching.
The document discusses a proposed multimodal biometric system that uses feature-level fusion of fingerprints, face, and eye tracking biometrics. It extracts features from each biometric individually and then uses a joint sparse representation method to fuse the features together while accounting for noise and occlusion. This sparse representation forcing the different biometric features to interact through shared sparse coefficients. The proposed system aims to make multimodal biometrics recognition more robust to problems like noisy data, non-universality of single biometrics, intraclass variations, and spoof attacks.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Finger vein based biometric security systemeSAT Journals
Abstract Finger vein recognition is a kind of biometric authentication system. This is one among many forms of biometrics used to recognize the individuals and to verify their identity. This paper presents a finger vein authentication system using template matching. Implementation using Matlab shows that the finger vein authentication system performs well for user identification. Keywords – biometric, feature extraction, figure vein, security system
This document summarizes an analysis of iris recognition based on false acceptance rate (FAR) and false rejection rate (FRR) using the Hough transform. It first provides an overview of iris recognition and its typical stages: image acquisition, localization/segmentation, normalization, feature extraction, and pattern matching. It then describes existing methods used in each stage, including the Hough transform and rubber sheet model for localization and normalization. The proposed methodology applies Canny edge detection, Hough transform for boundary detection, normalization with the rubber sheet model, and calculates metrics like mean squared error, root mean squared error, signal-to-noise ratio, and root signal-to-noise ratio to evaluate the accuracy of iris recognition using FAR
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
A Comprehensive Approach for Multi Biometric Recognition Using Sclera Vein an...IJTET Journal
Sclera and finger print vein fusion is a new biometric approach for uniquely identifying humans. First, Sclera vein is identified and refined using image enhancement techniques. Then Y shape feature extraction algorithm is used to obtain Y shape pattern which are then fused with finger vein pattern. Second, Finger vein pattern is obtained using CCD camera by passing infrared light through the finger. The obtained image is then enhanced. A line shape feature extraction algorithm is used to get line patterns from enhanced finger vein image. Finally Sclera vein image pattern and Finger vein image pattern were combined to get the final fused image. The image thus obtained can be used to uniquely identify a person. The proposed multimodal system will produce accurate results as it combines two main traits of an individual. Therefore, it can be used in human identification and authentication systems.
Similar to OPTIMIZED FINGERPRINT COMPRESSION WITHOUT LOSS OF DATAProposed workblessy updated (20)
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OPTIMIZED FINGERPRINT COMPRESSION WITHOUT LOSS OF DATAProposed workblessy updated
1. OPTIMIZED FINGERPRINT COMPRESSION WITHOUT LOSS OF
DATA
ABSTRACT:
The popular Biometric used to authenticate a person is Fingerprint which
is unique and permanent throughout a person’s life. A minutia matching is widely used for
fingerprint recognition and can be classified as ridge ending and ridge bifurcation. In this
paper we projected Fingerprint Recognition using Minutia Score Matching method
(FRMSM). For Fingerprint thinning, the Block Filter is used, which scans the image at the
boundary to preserves the quality of the image and extract the minutiae from the thinned
image. The false matching ratio is better compared to the existing algorithm.
Biometric systems operate on behavioral and physiological biometric data to
identify a person. The behavioral biometric parameters are signature, gait, speech and
keystroke, these parameters change with age and environment. However physiological
characteristics such as face, fingerprint, palm print and iris remains unchanged through out
the life time of a person. The biometric system operates as verification mode or identification
mode depending on the requirement of an application. The verification mode validates a
person’s identity by comparing captured biometric data with ready made template. The
identification mode recognizes a person’s identity by performing matches against multiple
fingerprint biometric templates. Fingerprints are widely used in daily life for more than 100
years due to its feasibility, distinctiveness, permanence, accuracy, reliability, and
acceptability. Fingerprint is a pattern of ridges, furrows and minutiae, which are extracted
using inked impression on a paper or sensors. A good quality fingerprint contains 25 to 80
minutiae depending on sensor resolution and finger placement on the sensor.
Existing system:
2. 1.In this developed a method for enhancing the ridge pattern by using a process of oriented
diffusion by adaptation of anisotropic diffusion to smooth the image in the direction parallel
to the ridge flow.
2.The image intensity varies smoothly as one traverse along the ridges or valleys by
removing most of the small irregularities and breaks but with the identity of the individual
ridges and valleys preserved.
3.Proposed a method for fingerprint verification which includes both minutiae and model
based
orientation field is used.
4. It gives robust discriminatory information other than minutiae points. Fingerprint matching
is done by combining the decisions of the matchers based on the orientation field and
minutiae.
Proposed system:
1.It proposes a method to describe a fingerprint matching based on lines
extraction
and graph matching principles by adopting a hybrid scheme which consists of a genetic
algorithm phase and a local search phase.
2.Experimental results demonstrate the robustness of algorithm.
3.proposed a method for estimating four direction orientation field by considering four steps,
i) preprocessing fingerprint image, ii) determining the primary ridge of fingerprint block
using neuron pulse coupled neural network, iii) estimating block direction by projective
distance variance of a ridge, instead of a full block, iv) correcting the estimated orientation
field. obtain principal curves for auto fingerprint identification system.
4. From principal curves, minutiae extraction algorithm is used to extract the minutiae of the
fingerprint. The experimental results shows curves obtained from graph algorithm are
smoother than the thinning algorithm developed a method for minutiae based fingerprint and
its approach to the problem as two - class pattern recognition.
3. 5. The obtained feature vector by minutiae matching is classified into genuine or imposter
by Support Vector Machine resulting remarkable performance improvement proposed a
method to overcome non linear distortion using Local Relative Error Descriptor (LRLED).
6.The algorithm consists of three steps i) a pair wise alignment method to achieve fingerprint
alignment ii) a matched minutiae pair set is obtained with a threshold to reduce non-matches
finally iii) the LRLED – based similarity measure.
7.LRLED is good at distinguishing between corresponding and non corresponding minutiae-
pairs and works well for fingerprint minutiae matching
MODULES USED:
• Authentication
• Image Capturing
• Fingerprint matching
• Fingerprint Binarization
• Performance Evalution
MODULES EXPLANATION:
Authentication:
In this section authentication will be provided to the user. Once the user gets logged inside the
process, it will check whether the user is authenticated person or not .If the user is valid person, the
user is allowed for further process. Authentication is mainly used for the purpose of preserving the
documents from the third parties.
Image Capturing:
Advancements in sensor technology have led to many new and novel imaging
sensors. However, images captured by these sophisticated sensors still suffer from systematic
noise components such as PRNU noise .These imperfections affect the light sensitivity of
each individual pixel and form a constant noise pattern. Since every image captured by the
4. same sensor exhibits the same pattern, PRNU noise can be used as a fingerprint of the sensor.
To determine whether an image is captured by a given imaging device, a fingerprint is
extracted from the individual image by the same denoising procedure used for obtaining the
sensor fingerprint.
Fingerprint matching:
The efficiency of sensor fingerprint matching in large databases, in this paper, we propose to
apply an information reduction operation and represent sensor fingerprints in a quantized
form. Ideally, we would like to obtain a representation as compact as possible. Therefore, we
particularly focus on binary quantization and, essentially, use each element’s sign
information only and disregard magnitude information completely.
Fingerprint Binarization:
Binarization of sensor fingerprints is an effective method that offers considerable
storage gain and complexity reduction without significant reduction in fingerprint matching
accuracy. Performance of fingerprint matching with binary fingerprints examine the gain
obtained in terms of storage and computational requirements. We proposed to create a
compact representation of fingerprints through quantization. Although many different
quantization strategies are possible, we focused on the most severe form of quantization by
quantizing every element of sensor fingerprints into a single bit.
Performance Evalution:
To improve on existing matching methods by addressing more practical concerns like
I/O and storage requirements and computation time while still maintaining an acceptable
matching accuracy. Analysis and experiments were conducted to determine the change in the
performance due to loss of information caused by binarization. It should be possible to get 64
times improvement in storage gain, memory operations. Our experiments, involving actual
fingerprints, showed that we can achieve 64 times reduction in storage requirements, 21 times
speedup in loading to memory, and nine times faster computation by our unoptimized
implementations.
Architecture diagram:
5. LITERATURE SURVEY
1. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust facerecognition
via sparse representation,”
INTRODUCTION
We consider the problem of automatically recognizing human faces from frontal views
with varying expression and illumination, as well as occlusion and disguise. We cast the
recognition problem as one of classifying among multiple linear regression models and argue
that new theory from sparse signal representation offers the key to addressing this problem.
Based on a sparse representation computed by l{1}-minimization, we propose a general
classification algorithm for (image-based) object recognition. This new framework provides
new insights into two crucial issues in face recognition: feature extraction and robustness to
occlusion. For feature extraction, we show that if sparsity in the recognition problem is
properly harnessed, the choice of features is no longer critical.
PROBLEM STATEMENT
6. What is critical, however, is whether the number of features is sufficiently large and
whether the sparse representation is correctly computed. Unconventional features such as
downsampled images and random projections perform just as well as conventional features
such as Eigenfaces and Laplacianfaces, as long as the dimension of the feature space
surpasses certain threshold, predicted by the theory of sparse representation. This framework
can handle errors due to occlusion and corruption uniformly by exploiting the fact that these
errors are often sparse with respect to the standard (pixel) basis. The theory of sparse
representation helps predict how much occlusion the recognition algorithm can handle and
how to choose the training images to maximize robustness to occlusion. We conduct
extensive experiments on publicly available databases to verify the efficacy of the proposed
algorithm and corroborate the above claims.
2. E. Candes and Y. Plan, “Near-ideal model selection by _1 minimization,”
INTRODUCTION
We consider the fundamental problem of estimating the mean of a vector y = Xβ + z,
where X is an n × p design matrix in which one can have far more variables than
observations, and z is a stochastic error term—the so-called “p > n” setup. When β is sparse,
or, more generally, when there is a sparse subset of covariates providing a close
approximation to the unknown mean vector, we ask whether or not it is possible to accurately
estimate Xβ using a computationally tractable algorithm. We show that, in a surprisingly
wide range of situations, the lasso happens to nearly select the best subset of variables.
Interestingly, our results describe the average performance of the lasso; that is, the
performance one can expect in an vast majority of cases where Xβ is a sparse or nearly sparse
superposition of variables, but not in all cases.
PROBLEM STATEMENT
On the one hand, these examples show that, even with highly incoherent matrices, one
cannot expect good performance in all cases unless the sparsity level is very small. And on
the other hand, one cannot really eliminate our assumption about the coherence, since we
have shown that, with coherent matrices, the lasso would fail to work well on generically
sparse objects. One could of course consider other statistical descriptions of sparse β’s and/or
ideal models, and leave this issue open for further research.
3. A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholdingalgorithm for
linear inverse problems”,
7. INTRODUCTION
This class of methods, which can be viewed as an extension of the classical gradient
algorithm, is attractive due to its simplicity and thus is adequate for solving large-scale
problems even with dense matrix data. However, such methods are also known to converge
quite slowly. In this paper we present a new fast iterative shrinkage-thresholding algorithm
(FISTA) which preserves the computational simplicity of ISTA but with a global rate of
convergence which is proven to be significantly better, both theoretically and practically.
PROBLEM STATEMENT
Initial promising numerical results for wavelet-based image deblurring demonstrate the
capabilities of FISTA which is shown to be faster than ISTA by several orders of magnitude.
These preliminary computational results indicate that FISTA is a simple and promising
iterative scheme, which can be even faster than the proven predicted theoretical rate. Its
potential for analyzing and designing faster algorithms in other application areas and
withother types of regularizers, as well as a more thorough computational study, are topics of
future research.
4. J. Yang, J. Wright, T. Huang, and Y. Ma, “Image super-resolutionas sparse
representation of raw image patches,”
INTRODUCTION
In this paper, we focus on the problem of recovering the super-resolution version of a
given low-resolution image. Although our method can be readily extended to handle multiple
input images, we mostly deal with a single input image. Like the aforementioned learning-
based methods, we will rely on patches from example images. Our method does not require
any learning on the high-resolution patches, instead working directly with the low-resolution
training patches or their features
PROBLEM STATEMENT
However, one of the most important questions for future investigation is to determine, in
terms of the within-category variation, the number of raw sample patches required to generate
a dictionary satisfying the sparse representation prior. Tighter connections to the theory of
compressed sensing may also yield conditions on the appropriate patch size or feature
dimension. From a more practical standpoint, it would be desirable to have a way of
effectively combining dictionaries to work with images containing multiple types of textures
or multiple object categories. One approach to this would integrate supervised image
segmentation and super-resolution, applying the appropriate dictionary within each segment.
8. 5. O. Bryt and M. Elad, “Compression of facial images using theK-SVD algorithm,”
INTRODUCTION
Compression of still images is a very active and matured field of research, vivid in both
research and engineering communities. Compression of images is possible because of their
vast spatial redundancy and the ability to absorb moderate errors in the reconstructed image.
This field of work offers many contributions, some of which became standard algorithms that
are wide-spread and popularAmong the many methods for image compression, one of the
best is the JPEG2000 standard—a general purpose wavelet based image compression
algorithm with very good compression performance
PROBLEM STATEMENT
In this paper we present a facial image compression method, based on sparse and
redundant representations and the K-SVD dictionary learning algorithm. The proposed
compression method is tested in various bit-rates and options, and compared to several
known compression techniques with great success. Results on the importance of redundancy
in the deployed dictionaries are presented. The contribution of this work has several facets:
first, while sparse and redundant representations and learned dictionaries have shown to be
effective in various image processing problems, their role in compression has been less
explored, and this work provides the first evidence to its success in this arena as well.
Second, the proposed scheme is very practical, and could be the foundation for systems that
use large databases of face images. Third, among the various ways to imitate the VQ and yet
be practical, the proposed method stands as an interesting option that should be further
explored. As for future work, we are currently exploring several extensions of this activity,
such as reducing or eliminating the much troubling blockiness effects due to the slicing to
patches, generalization to compression of color images, and adopting the ideas in this work
for compression of finger-prints images. The horizon and the ultimate goal, in this respect, is
a successful harnessing of the presented methodology for general images, in a way that
surpasses the JPEG2000 performance—we believe that this is achievable.