IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Offline Signiture and Numeral Recognition in Context of ChequeIJERA Editor
Signature is considered as one of the biometrics. Signature Verification System is required in almost all places where it is compulsory to authenticate a person or his/her credentials to proceed further transaction especially when it comes to bank cheques. For this purpose signature verification system must be powerful and accurate. Till date various methods have been used to make signature verification system powerful and accurate. Research here is related to offline signature verification. Shape Contexts have been used to verify whether 2 shapes are similar or not. It has been used for various applications such as digit recognition, 3D Object recognition, trademark retrieval etc. In this paper we present a modified version of shape context for signature verification on bank cheques using K-Nearest Neighbor classifier.
COMPOSITE TEXTURE SHAPE CLASSIFICATION BASED ON MORPHOLOGICAL SKELETON AND RE...sipij
After several decades of research, the development of an effective feature extraction method for texture
classification is still an ongoing effort. Therefore , several techniques have been proposed to resolve such
problems. In this paper a novel composite texture classification method based on innovative pre-processing
techniques, skeletonization and Regional moments (RM) is proposed. This proposed texture classification
approach, takes into account the ambiguity brought in by noise and the different caption and digitization
processes. To offer better classification rate, innovative pre-processing methods are applied on various
texture images first. Pre-processing mechanisms describe various methods of converting a grey level image
into binary image with minimal consideration of the noise model. Then shape features are evaluated using
RM on the proposed Morphological Skeleton (MS) method by suitable numerical characterization
measures for a precise classification. This texture classification study using MS and RM has given a good
performance. Good classification result is achieved from a single region moment RM10 while others failed
in classification.
Nowadays character recognition has gained lot of attention in the field of pattern recognition due to its application in various fields. It is one of the most successful applications of automatic pattern recognition. Research in OCR is popular for its application potential in banks, post offices, office automation etc. HCR is useful in cheque processing in banks; almost all kind of form processing systems, handwritten postal address resolution and many more. This paper presents a simple and efficient approach for the implementation of OCR and translation of scanned images of printed text into machine-encoded text. It makes use of different image analysis phases followed by image detection via pre-processing and post-processing. This paper also describes scanning the entire document (same as the segmentation in our case) and recognizing individual characters from image irrespective of their position, size and various font styles and it deals with recognition of the symbols from English language, which is internationally accepted.
Feature integration for image information retrieval using image mining techni...iaemedu
This document discusses feature extraction techniques for image information retrieval. It proposes integrating features using image mining to generate a super set of features. It describes extracting primitive features of color, texture, and shape. Color is extracted using histograms in RGB color space. Texture is extracted statistically using co-occurrence matrices and wavelet transforms. Shape is extracted using boundary-based and region-based methods like Canny edge detection. The document asserts that integrating features, such as color and texture or texture and shape, results in better performance than using features individually for image retrieval.
Object recognition from image using grid based color moments feature extracti...eSAT Publishing House
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.
F ACIAL E XPRESSION R ECOGNITION B ASED ON E DGE D ETECTIONIJCSES Journal
Relational Over the last two decades, the
advances in computer vision and pattern recognition power have
opened the door to new opportunity of automatic facial expression recognition system[1]. This paper
use
Canny edge detection method for facial expression recognition. Image color space transfor
mation in the
first place and then to identify and locate human face .Next pick up the edge of eyes and mouth's fe
atures
extraction. Last we judge the facial expressions after compared with the expressions we known in the
database. This proposed approach p
rovides full automatic solution of human expressions as well as
overcoming facial expressions variation and intensity problems.
Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face RecognitionPeachy Essay
To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a
new scheme to extract “Multi-Directional Multi-Level Dual-Cross Patterns” (MDML-DCPs) from face images. Specifically, the MDMLDCPs scheme exploits the first derivative of Gaussian operator to reduce the impact of differences in illumination and then computes the DCP feature at both the holistic and component levels. DCP is a novel face image descriptor inspired by the unique textural structure of human faces. It is computationally efficient and only doubles the cost of computing local binary patterns, yet is extremely robust to pose and expression variations. MDML-DCPs comprehensively yet efficiently encodes the invariant characteristics of a face image from multiple levels into patterns that are highly discriminative of inter-personal differences but robust to intra-personal variations.
Experimental results on the FERET, CAS-PERL-R1, FRGC 2.0, and LFW databases indicate that DCP outperforms the state-of-the-art local descriptors (e.g. LBP, LTP, LPQ, POEM, tLBP, and LGXP) for both face identification and face verification tasks. More impressively, the best performance is achieved on the challenging LFW and FRGC 2.0 databases by deploying MDML-DCPs in a simple recognition scheme.
Offline Signiture and Numeral Recognition in Context of ChequeIJERA Editor
Signature is considered as one of the biometrics. Signature Verification System is required in almost all places where it is compulsory to authenticate a person or his/her credentials to proceed further transaction especially when it comes to bank cheques. For this purpose signature verification system must be powerful and accurate. Till date various methods have been used to make signature verification system powerful and accurate. Research here is related to offline signature verification. Shape Contexts have been used to verify whether 2 shapes are similar or not. It has been used for various applications such as digit recognition, 3D Object recognition, trademark retrieval etc. In this paper we present a modified version of shape context for signature verification on bank cheques using K-Nearest Neighbor classifier.
COMPOSITE TEXTURE SHAPE CLASSIFICATION BASED ON MORPHOLOGICAL SKELETON AND RE...sipij
After several decades of research, the development of an effective feature extraction method for texture
classification is still an ongoing effort. Therefore , several techniques have been proposed to resolve such
problems. In this paper a novel composite texture classification method based on innovative pre-processing
techniques, skeletonization and Regional moments (RM) is proposed. This proposed texture classification
approach, takes into account the ambiguity brought in by noise and the different caption and digitization
processes. To offer better classification rate, innovative pre-processing methods are applied on various
texture images first. Pre-processing mechanisms describe various methods of converting a grey level image
into binary image with minimal consideration of the noise model. Then shape features are evaluated using
RM on the proposed Morphological Skeleton (MS) method by suitable numerical characterization
measures for a precise classification. This texture classification study using MS and RM has given a good
performance. Good classification result is achieved from a single region moment RM10 while others failed
in classification.
Nowadays character recognition has gained lot of attention in the field of pattern recognition due to its application in various fields. It is one of the most successful applications of automatic pattern recognition. Research in OCR is popular for its application potential in banks, post offices, office automation etc. HCR is useful in cheque processing in banks; almost all kind of form processing systems, handwritten postal address resolution and many more. This paper presents a simple and efficient approach for the implementation of OCR and translation of scanned images of printed text into machine-encoded text. It makes use of different image analysis phases followed by image detection via pre-processing and post-processing. This paper also describes scanning the entire document (same as the segmentation in our case) and recognizing individual characters from image irrespective of their position, size and various font styles and it deals with recognition of the symbols from English language, which is internationally accepted.
Feature integration for image information retrieval using image mining techni...iaemedu
This document discusses feature extraction techniques for image information retrieval. It proposes integrating features using image mining to generate a super set of features. It describes extracting primitive features of color, texture, and shape. Color is extracted using histograms in RGB color space. Texture is extracted statistically using co-occurrence matrices and wavelet transforms. Shape is extracted using boundary-based and region-based methods like Canny edge detection. The document asserts that integrating features, such as color and texture or texture and shape, results in better performance than using features individually for image retrieval.
Object recognition from image using grid based color moments feature extracti...eSAT Publishing House
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.
F ACIAL E XPRESSION R ECOGNITION B ASED ON E DGE D ETECTIONIJCSES Journal
Relational Over the last two decades, the
advances in computer vision and pattern recognition power have
opened the door to new opportunity of automatic facial expression recognition system[1]. This paper
use
Canny edge detection method for facial expression recognition. Image color space transfor
mation in the
first place and then to identify and locate human face .Next pick up the edge of eyes and mouth's fe
atures
extraction. Last we judge the facial expressions after compared with the expressions we known in the
database. This proposed approach p
rovides full automatic solution of human expressions as well as
overcoming facial expressions variation and intensity problems.
Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face RecognitionPeachy Essay
To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a
new scheme to extract “Multi-Directional Multi-Level Dual-Cross Patterns” (MDML-DCPs) from face images. Specifically, the MDMLDCPs scheme exploits the first derivative of Gaussian operator to reduce the impact of differences in illumination and then computes the DCP feature at both the holistic and component levels. DCP is a novel face image descriptor inspired by the unique textural structure of human faces. It is computationally efficient and only doubles the cost of computing local binary patterns, yet is extremely robust to pose and expression variations. MDML-DCPs comprehensively yet efficiently encodes the invariant characteristics of a face image from multiple levels into patterns that are highly discriminative of inter-personal differences but robust to intra-personal variations.
Experimental results on the FERET, CAS-PERL-R1, FRGC 2.0, and LFW databases indicate that DCP outperforms the state-of-the-art local descriptors (e.g. LBP, LTP, LPQ, POEM, tLBP, and LGXP) for both face identification and face verification tasks. More impressively, the best performance is achieved on the challenging LFW and FRGC 2.0 databases by deploying MDML-DCPs in a simple recognition scheme.
GEOMETRIC CORRECTION FOR BRAILLE DOCUMENT IMAGEScsandit
Braille system has been used by the visually impaired people for reading.The shortage of Braille
books has caused a need for conversion of Braille to text. This paper addresses the geometric
correction of a Braille document images. Due to the standard measurement of the Braille cells,
identification of Braille characters could be achieved by simple cell overlapping procedure. The
standard measurement varies in a scaled document and fitting of the cells become difficult if the
document is tilted. This paper proposes a line fitting algorithm for identifying the tilt (skew)
angle. The horizontal and vertical scale factor is identified based on the ratio of distance
between characters to the distance between dots. These are used in geometric transformation
matrix for correction. Rotation correction is done prior to scale correction. This process aids in
increased accuracy. The results for various Braille documents are tabulated.
Content Based Image Retrieval using Color Boosted Salient Points and Shape fe...CSCJournals
Salient points are locations in an image where there is a significant variation with respect to a chosen image feature. Since the set of salient points in an image capture important local characteristics of that image, they can form the basis of a good image representation for content-based image retrieval (CBIR). Salient features are generally determined from the local differential structure of images. They focus on the shape saliency of the local neighborhood. Most of these detectors are luminance based which have the disadvantage that the distinctiveness of the local color information is completely ignored in determining salient image features. To fully exploit the possibilities of salient point detection in color images, color distinctiveness should be taken into account in addition to shape distinctiveness. This paper presents a method for salient points determination based on color saliency. The color and texture information around these points of interest serve as the local descriptors of the image. In addition, the shape information is captured in terms of edge images computed using Gradient Vector Flow fields. Invariant moments are then used to record the shape features. The combination of the local color, texture and the global shape features provides a robust feature set for image retrieval. The experimental results demonstrate the efficacy of the method.
Object recognition is the challenging problem in the real world application. Object recognition can be achieved through the shape matching. Shape matching is preceded by i) detecting the edges of the objects from the images. ii) Finding the correspondence between the shapes. iii) Measuring the dissimilarity between the shapes using the correspondence. iv) Classifying the object into classes by using this dissimilarity measures. In order to solve the correspondence problem, we attach a descriptor, the shape context, to each point. The shape context at a reference point captures the distribution of the remaining points relative to it, thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts. The one to one correspondence is achieved through the cost based bipartite graph matching. The cost matrix is reduced through Hungarian algorithm. The dissimilarity between the two shapes is computed using Canberra distance. The nearest neighbor classifier is used to classify the objects with the matching error. The results are obtained using the MATLAB for MINIST hand written digits.
Image segmentation Based on Chan-Vese Active Contours using Finite Difference...ijsrd.com
There are a lot of image segmentation techniques that try to differentiate between backgrounds and object pixels but many of them fail to discriminate between different objects that are close to each other, e.g. low contrast between foreground and background regions increase the difficulty for segmenting images. So we introduced the Chan-Vese active contours model for image segmentation to detect the objects in given image, which is built based on techniques of curve evolution and level set method. The Chan-Vese model is a special case of Mumford-Shah functional for segmentation and level sets. It differs from other active contour models in that it is not edge dependent, therefore it is more capable of detecting objects whose boundaries may not be defined by a gradient. Finally, we developed code in Matlab 7.8 for solving resulting Partial differential equation numerically by the finite differences schemes on pixel-by-pixel domain.
Recognition of Persian handwritten characters has been considered as a significant field of research for
the last few years under pattern analysing technique. In this paper, a new approach for robust handwritten
Persian numerals recognition using strong feature set and a classifier fusion method is scrutinized to
increase the recognition percentage. For implementing the classifier fusion technique, we have considered
k nearest neighbour (KNN), linear classifier (LC) and support vector machine (SVM) classifiers. The
innovation of this tactic is to attain better precision with few features using classifier fusion method. For
evaluation of the proposed method we considered a Persian numerals database with 20,000 handwritten
samples. Spending 15,000 samples for training stage, we verified our technique on other 5,000 samples,
and the correct recognition ratio achievedapproximately 99.90%. Additional, we got 99.97% exactness
using four-fold cross validation procedure on 20,000 databases.
This document presents a novel approach for scale invariant partial shape matching of binary images. It discusses existing techniques for shape matching and their limitations, including problems related to scale, distortion, and the need for partial matching of open and closed contours. The proposed approach uses shape descriptors computed along open or closed contours to represent global shape. It then applies an alternative to dynamic time warping matching to compare shape representations in a way that is invariant to transformations and can match closed contours as a special case. The method is intended to improve on existing techniques by providing solutions to more matching problems through use of an extensive dataset and more flexible matching procedure.
NOVEL ALGORITHM FOR SKIN COLOR BASED SEGMENTATION USING MIXTURE OF GMMSsipij
1) The document proposes a novel algorithm for skin color segmentation using a mixture of Gaussian mixture models (GMMs). It models four common color spaces (RGB, HSV, YCbCr, L*a*b*) each with a single GMM.
2) These GMM models are then combined into a single superior model called a mixture of GMMs (MiGMM) by assigning a weight to each GMM based on its classification rate.
3) The algorithm is evaluated on 100 test images using three metrics: correct detection rate, false detection rate, and classification rate, achieving higher recognition rates than single GMM models of individual color spaces.
BAG OF VISUAL WORDS FOR WORD SPOTTING IN HANDWRITTEN DOCUMENTS BASED ON CURVA...ijcsit
In this paper, we present a segmentation-based word spotting method for handwritten documents using
Bag of Visual Words (BoVW) framework based on curvature features. The BoVW based word spotting
methods extract SIFT or SURF features at each keypoint using fixed sized window. The drawbacks of these
techniques are that they are memory intensive; the window size cannot be adapted to the length of the
query and requires alignment between the keypoint sets. In order to overcome the drawbacks of SIFT or
SURF local features based existing methods, we proposed to extract curvature feature at each keypoint of
word image in BoVW framework. The curvature feature is scalar value describes the geometrical shape of
the strokes and requires less memory space to store. The proposed method is evaluated using mean
Average Precision metric through experimentation conducted on popular datasets such as GW, IAM and
Bentham datasets. The yielded performances confirmed that our method outperforms existing word spotting
techniques.
A Comprehensive Study On Handwritten Character Recognition Systemiosrjce
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.
LITERATURE SURVEY ON SPARSE REPRESENTATION FOR NEURAL NETWORK BASED FACE DETE...csijjournal
Face detection and recognition is a challenging problem in the field of image processing. In this paper, we reviewed some of the recent research works on face recognition. Issues with the previous face recognition
techniques are , time required is more for face recognition , recognition rate and database required to store the data . To overcome these problems sparse representation based classifier technique can be used.
Survey on Brain MRI Segmentation TechniquesEditor IJMTER
Image segmentation is aimed at cutting out, a ROI (Region of Interest) from an image. For
medical images, segmentation is done for: studying the anatomical structure, identifying ROI ie tumor
or any other abnormalities, identifying the increase in tissue volume in a region, treatment planning.
Currently there are many different algorithms available for image segmentation. This paper lists and
compares some of them. Each has their own advantages and limitations.
An exhaustive font and size invariant classification scheme for ocr of devana...ijnlc
The document presents a classification scheme for recognizing Devanagari characters that is invariant to font and size. It identifies the basic symbols that commonly appear in the middle zone of Devanagari text across different fonts and sizes. Through an analysis of over 465,000 words from various sources, it finds that 345 symbols account for 99.97% of text and aims to classify these into groups based on structural properties like the presence or absence of vertical bars. The proposed classification scheme is validated on 25 fonts and 3 sizes to demonstrate its font and size invariance.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Study on a Hybrid Segmentation Approach for Handwritten Numeral Strings in Fo...inventionjournals
This paper presents a hybrid approach to segment single- or multiple-touching handwritten numeral strings in form document, the core of which is the combined use of foreground, background and recognition analysis. The algorithm first located some feature points on both the foreground and background skeleton images containing connected numeral strings in form document. Possible segmentation paths were then constructed by matching these feature points, with an unexpected benefit of removing useless strokes. Subsequently, all these segmentation paths were validated and ranked by a recognition-based analysis, where a well-trained two-stage classifier was applied to each separated digit image to obtain its reliability. Finally, by introducing a locally optimal strategy to accelerate the recognition process, the top ranked segmentation path survived to help make a decision on whether to accept or not. Experimental results show that the proposed method can achieve a correct segmentation rate of 96.2 percent on a large dataset collected by our own.
DEVNAGARI NUMERALS CLASSIFICATION AND RECOGNITION USING AN INTEGRATED APPROACHijfcstjournal
Character recognition has always been a challenging field for the researchers. There has been an astounding progress in the development of the systems for character recognition. OCR performs the recognition of the text in the scanned document image and converts it into editable form. The OCR process can have several stages like preprocessing, segmentation, recognition and post processing. The recognition generally, consists of feature extraction and classification. The choice of features and classification scheme affects the performance of OCR largely. In this paper, a classification scheme is proposed for the Devnagari numerals, which forms the basis for recognition. This approach integrates the structural features and water reservoir analogy based feature to classify the Devnagari numeral. In order to classify a single numeral, at most four checks are required. This increases the efficiency of the proposed scheme.
This document discusses the performance analysis and minimization of black hole attacks in mobile ad hoc networks (MANETs). It begins with an introduction to MANETs and discusses how they are vulnerable to black hole attacks. The document then describes the AODV routing protocol and how black hole attacks exploit vulnerabilities in the route discovery process. Existing detection and prevention techniques are outlined. The document proposes modifying the AODV protocol to implement an intrusion detection system (IDSAODV) that can detect and discard fraudulent route replies from black hole nodes, improving packet delivery. Simulation scenarios of varying node counts with and without black holes are used to analyze black hole behavior and evaluate the effectiveness of the IDSAODV approach.
The document proposes two new authentication schemes for PDAs that use session passwords. Session passwords are one-time passwords generated for each login. The first scheme generates passwords based on pairs of letters from a secret text password and their intersections on a grid. The second scheme has users rate colors during registration, and session passwords are generated by the intersections of those colors on a color grid and number grid displayed during login. Both schemes aim to be resistant to dictionary attacks, brute force attacks, and shoulder surfing by changing the grids each time. The techniques were proposed to provide authentication for PDAs but require further testing for usability and effectiveness.
This document discusses software security metrics and validating UML diagrams using metrics. It provides background on using metrics to measure quality attributes of object-oriented designs. Traditional code-level security metrics are insufficient and evaluating security at the design level is important. The paper proposes a system that applies design-level security metrics using genetic algorithms to generate secure design options from a UML diagram. It then implements code from the designs and applies the same metrics at the code level to validate that the code matches the intended secure design. This allows discovering and fixing security issues earlier in the development process.
This document summarizes and compares four routing algorithms for mobile ad hoc networks: Disjoint Multipath Routing, Trust based Multipath Routing, Message Trust based Multipath Routing, and a new proposed algorithm called Friend Based Ad-hoc Routing. It describes the key mechanisms of each algorithm, including how they establish routes, incorporate trust levels, and handle packet routing. The proposed FACES algorithm aims to improve security and efficiency by using friend, unauthenticated, and question mark lists to identify trusted routes and avoid malicious nodes.
The document describes a genetic algorithm approach to optimizing the design of steel-concrete composite plane frames to minimize cost. The algorithm uses design variables like beam and column cross-sectional properties to represent potential solutions. It evaluates solutions based on structural analysis and design constraints like moments, shear, buckling and axial forces. The best solution from each generation is preserved to guide the evolution toward an optimal, cost-effective frame design. The approach is demonstrated on example frames.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document summarizes a research paper that proposes using a genetic algorithm to generate high-quality association rules for measuring data quality. The genetic algorithm evaluates rules based on four metrics: confidence, completeness, comprehensibility, and interestingness. It aims to discover high-level prediction rules that perform better than traditional greedy rule induction algorithms at handling attribute interactions. The genetic algorithm represents rules as chromosomes and uses the four metrics as an objective fitness function to evaluate the quality of each rule.
GEOMETRIC CORRECTION FOR BRAILLE DOCUMENT IMAGEScsandit
Braille system has been used by the visually impaired people for reading.The shortage of Braille
books has caused a need for conversion of Braille to text. This paper addresses the geometric
correction of a Braille document images. Due to the standard measurement of the Braille cells,
identification of Braille characters could be achieved by simple cell overlapping procedure. The
standard measurement varies in a scaled document and fitting of the cells become difficult if the
document is tilted. This paper proposes a line fitting algorithm for identifying the tilt (skew)
angle. The horizontal and vertical scale factor is identified based on the ratio of distance
between characters to the distance between dots. These are used in geometric transformation
matrix for correction. Rotation correction is done prior to scale correction. This process aids in
increased accuracy. The results for various Braille documents are tabulated.
Content Based Image Retrieval using Color Boosted Salient Points and Shape fe...CSCJournals
Salient points are locations in an image where there is a significant variation with respect to a chosen image feature. Since the set of salient points in an image capture important local characteristics of that image, they can form the basis of a good image representation for content-based image retrieval (CBIR). Salient features are generally determined from the local differential structure of images. They focus on the shape saliency of the local neighborhood. Most of these detectors are luminance based which have the disadvantage that the distinctiveness of the local color information is completely ignored in determining salient image features. To fully exploit the possibilities of salient point detection in color images, color distinctiveness should be taken into account in addition to shape distinctiveness. This paper presents a method for salient points determination based on color saliency. The color and texture information around these points of interest serve as the local descriptors of the image. In addition, the shape information is captured in terms of edge images computed using Gradient Vector Flow fields. Invariant moments are then used to record the shape features. The combination of the local color, texture and the global shape features provides a robust feature set for image retrieval. The experimental results demonstrate the efficacy of the method.
Object recognition is the challenging problem in the real world application. Object recognition can be achieved through the shape matching. Shape matching is preceded by i) detecting the edges of the objects from the images. ii) Finding the correspondence between the shapes. iii) Measuring the dissimilarity between the shapes using the correspondence. iv) Classifying the object into classes by using this dissimilarity measures. In order to solve the correspondence problem, we attach a descriptor, the shape context, to each point. The shape context at a reference point captures the distribution of the remaining points relative to it, thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts. The one to one correspondence is achieved through the cost based bipartite graph matching. The cost matrix is reduced through Hungarian algorithm. The dissimilarity between the two shapes is computed using Canberra distance. The nearest neighbor classifier is used to classify the objects with the matching error. The results are obtained using the MATLAB for MINIST hand written digits.
Image segmentation Based on Chan-Vese Active Contours using Finite Difference...ijsrd.com
There are a lot of image segmentation techniques that try to differentiate between backgrounds and object pixels but many of them fail to discriminate between different objects that are close to each other, e.g. low contrast between foreground and background regions increase the difficulty for segmenting images. So we introduced the Chan-Vese active contours model for image segmentation to detect the objects in given image, which is built based on techniques of curve evolution and level set method. The Chan-Vese model is a special case of Mumford-Shah functional for segmentation and level sets. It differs from other active contour models in that it is not edge dependent, therefore it is more capable of detecting objects whose boundaries may not be defined by a gradient. Finally, we developed code in Matlab 7.8 for solving resulting Partial differential equation numerically by the finite differences schemes on pixel-by-pixel domain.
Recognition of Persian handwritten characters has been considered as a significant field of research for
the last few years under pattern analysing technique. In this paper, a new approach for robust handwritten
Persian numerals recognition using strong feature set and a classifier fusion method is scrutinized to
increase the recognition percentage. For implementing the classifier fusion technique, we have considered
k nearest neighbour (KNN), linear classifier (LC) and support vector machine (SVM) classifiers. The
innovation of this tactic is to attain better precision with few features using classifier fusion method. For
evaluation of the proposed method we considered a Persian numerals database with 20,000 handwritten
samples. Spending 15,000 samples for training stage, we verified our technique on other 5,000 samples,
and the correct recognition ratio achievedapproximately 99.90%. Additional, we got 99.97% exactness
using four-fold cross validation procedure on 20,000 databases.
This document presents a novel approach for scale invariant partial shape matching of binary images. It discusses existing techniques for shape matching and their limitations, including problems related to scale, distortion, and the need for partial matching of open and closed contours. The proposed approach uses shape descriptors computed along open or closed contours to represent global shape. It then applies an alternative to dynamic time warping matching to compare shape representations in a way that is invariant to transformations and can match closed contours as a special case. The method is intended to improve on existing techniques by providing solutions to more matching problems through use of an extensive dataset and more flexible matching procedure.
NOVEL ALGORITHM FOR SKIN COLOR BASED SEGMENTATION USING MIXTURE OF GMMSsipij
1) The document proposes a novel algorithm for skin color segmentation using a mixture of Gaussian mixture models (GMMs). It models four common color spaces (RGB, HSV, YCbCr, L*a*b*) each with a single GMM.
2) These GMM models are then combined into a single superior model called a mixture of GMMs (MiGMM) by assigning a weight to each GMM based on its classification rate.
3) The algorithm is evaluated on 100 test images using three metrics: correct detection rate, false detection rate, and classification rate, achieving higher recognition rates than single GMM models of individual color spaces.
BAG OF VISUAL WORDS FOR WORD SPOTTING IN HANDWRITTEN DOCUMENTS BASED ON CURVA...ijcsit
In this paper, we present a segmentation-based word spotting method for handwritten documents using
Bag of Visual Words (BoVW) framework based on curvature features. The BoVW based word spotting
methods extract SIFT or SURF features at each keypoint using fixed sized window. The drawbacks of these
techniques are that they are memory intensive; the window size cannot be adapted to the length of the
query and requires alignment between the keypoint sets. In order to overcome the drawbacks of SIFT or
SURF local features based existing methods, we proposed to extract curvature feature at each keypoint of
word image in BoVW framework. The curvature feature is scalar value describes the geometrical shape of
the strokes and requires less memory space to store. The proposed method is evaluated using mean
Average Precision metric through experimentation conducted on popular datasets such as GW, IAM and
Bentham datasets. The yielded performances confirmed that our method outperforms existing word spotting
techniques.
A Comprehensive Study On Handwritten Character Recognition Systemiosrjce
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.
LITERATURE SURVEY ON SPARSE REPRESENTATION FOR NEURAL NETWORK BASED FACE DETE...csijjournal
Face detection and recognition is a challenging problem in the field of image processing. In this paper, we reviewed some of the recent research works on face recognition. Issues with the previous face recognition
techniques are , time required is more for face recognition , recognition rate and database required to store the data . To overcome these problems sparse representation based classifier technique can be used.
Survey on Brain MRI Segmentation TechniquesEditor IJMTER
Image segmentation is aimed at cutting out, a ROI (Region of Interest) from an image. For
medical images, segmentation is done for: studying the anatomical structure, identifying ROI ie tumor
or any other abnormalities, identifying the increase in tissue volume in a region, treatment planning.
Currently there are many different algorithms available for image segmentation. This paper lists and
compares some of them. Each has their own advantages and limitations.
An exhaustive font and size invariant classification scheme for ocr of devana...ijnlc
The document presents a classification scheme for recognizing Devanagari characters that is invariant to font and size. It identifies the basic symbols that commonly appear in the middle zone of Devanagari text across different fonts and sizes. Through an analysis of over 465,000 words from various sources, it finds that 345 symbols account for 99.97% of text and aims to classify these into groups based on structural properties like the presence or absence of vertical bars. The proposed classification scheme is validated on 25 fonts and 3 sizes to demonstrate its font and size invariance.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Study on a Hybrid Segmentation Approach for Handwritten Numeral Strings in Fo...inventionjournals
This paper presents a hybrid approach to segment single- or multiple-touching handwritten numeral strings in form document, the core of which is the combined use of foreground, background and recognition analysis. The algorithm first located some feature points on both the foreground and background skeleton images containing connected numeral strings in form document. Possible segmentation paths were then constructed by matching these feature points, with an unexpected benefit of removing useless strokes. Subsequently, all these segmentation paths were validated and ranked by a recognition-based analysis, where a well-trained two-stage classifier was applied to each separated digit image to obtain its reliability. Finally, by introducing a locally optimal strategy to accelerate the recognition process, the top ranked segmentation path survived to help make a decision on whether to accept or not. Experimental results show that the proposed method can achieve a correct segmentation rate of 96.2 percent on a large dataset collected by our own.
DEVNAGARI NUMERALS CLASSIFICATION AND RECOGNITION USING AN INTEGRATED APPROACHijfcstjournal
Character recognition has always been a challenging field for the researchers. There has been an astounding progress in the development of the systems for character recognition. OCR performs the recognition of the text in the scanned document image and converts it into editable form. The OCR process can have several stages like preprocessing, segmentation, recognition and post processing. The recognition generally, consists of feature extraction and classification. The choice of features and classification scheme affects the performance of OCR largely. In this paper, a classification scheme is proposed for the Devnagari numerals, which forms the basis for recognition. This approach integrates the structural features and water reservoir analogy based feature to classify the Devnagari numeral. In order to classify a single numeral, at most four checks are required. This increases the efficiency of the proposed scheme.
This document discusses the performance analysis and minimization of black hole attacks in mobile ad hoc networks (MANETs). It begins with an introduction to MANETs and discusses how they are vulnerable to black hole attacks. The document then describes the AODV routing protocol and how black hole attacks exploit vulnerabilities in the route discovery process. Existing detection and prevention techniques are outlined. The document proposes modifying the AODV protocol to implement an intrusion detection system (IDSAODV) that can detect and discard fraudulent route replies from black hole nodes, improving packet delivery. Simulation scenarios of varying node counts with and without black holes are used to analyze black hole behavior and evaluate the effectiveness of the IDSAODV approach.
The document proposes two new authentication schemes for PDAs that use session passwords. Session passwords are one-time passwords generated for each login. The first scheme generates passwords based on pairs of letters from a secret text password and their intersections on a grid. The second scheme has users rate colors during registration, and session passwords are generated by the intersections of those colors on a color grid and number grid displayed during login. Both schemes aim to be resistant to dictionary attacks, brute force attacks, and shoulder surfing by changing the grids each time. The techniques were proposed to provide authentication for PDAs but require further testing for usability and effectiveness.
This document discusses software security metrics and validating UML diagrams using metrics. It provides background on using metrics to measure quality attributes of object-oriented designs. Traditional code-level security metrics are insufficient and evaluating security at the design level is important. The paper proposes a system that applies design-level security metrics using genetic algorithms to generate secure design options from a UML diagram. It then implements code from the designs and applies the same metrics at the code level to validate that the code matches the intended secure design. This allows discovering and fixing security issues earlier in the development process.
This document summarizes and compares four routing algorithms for mobile ad hoc networks: Disjoint Multipath Routing, Trust based Multipath Routing, Message Trust based Multipath Routing, and a new proposed algorithm called Friend Based Ad-hoc Routing. It describes the key mechanisms of each algorithm, including how they establish routes, incorporate trust levels, and handle packet routing. The proposed FACES algorithm aims to improve security and efficiency by using friend, unauthenticated, and question mark lists to identify trusted routes and avoid malicious nodes.
The document describes a genetic algorithm approach to optimizing the design of steel-concrete composite plane frames to minimize cost. The algorithm uses design variables like beam and column cross-sectional properties to represent potential solutions. It evaluates solutions based on structural analysis and design constraints like moments, shear, buckling and axial forces. The best solution from each generation is preserved to guide the evolution toward an optimal, cost-effective frame design. The approach is demonstrated on example frames.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document summarizes a research paper that proposes using a genetic algorithm to generate high-quality association rules for measuring data quality. The genetic algorithm evaluates rules based on four metrics: confidence, completeness, comprehensibility, and interestingness. It aims to discover high-level prediction rules that perform better than traditional greedy rule induction algorithms at handling attribute interactions. The genetic algorithm represents rules as chromosomes and uses the four metrics as an objective fitness function to evaluate the quality of each rule.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document describes the design and development of a microcontroller-based system for measuring blood glucose levels. The system uses an amperometric method that relies on glucose oxidase enzymes and a mediator compound to transfer electrons from blood glucose to an electrode, generating an electrical signal. A PIC 18F4520 microcontroller processes, amplifies and converts the signal to a display on an LCD module. The system is intended to be low-cost, portable, and provide frequent blood glucose monitoring to help control diabetes and reduce complications. It works by measuring the current produced from the reaction of blood glucose with glucose oxidase and a mediator compound.
This document summarizes a research paper that proposes a machine learning approach for detecting phishing websites. It discusses using heuristic features from CANTINA to train machine learning models. A new domain top-page similarity feature is introduced to improve accuracy. Various modules are described, including site training, site capturing, a phishing dictionary, and image correlation to measure similarity. Experimental results show the approach achieves up to 92.5% f-measure and a 7.5% error rate for phishing detection.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
The document discusses the characterization and numerical optimization of chromium-free nickel alloy filler materials for dissimilar welding between stainless steel SS304. Eight alloys with compositions ranging from 40-43.5% Ni, 4-20% Mo, 0-16% Co, 10% Cu, 22-25% Fe, 0.5% Al, 1% Ti, and 0.001% C were analyzed. JMatPro software was used to simulate phases present at different temperatures. Welding simulations using ANSYS evaluated residual stresses in the welds. The alloy with 43.499% Ni, 0.5% Al, 14% Co, 6% Mo, 10% Cu, 23% Fe, 2% Mn, 1
This document presents a study on using color texture feature analysis to detect surface defects on pomegranates. The researchers developed a method involving cropping images of pomegranates, converting them to HSI color space, generating SGDM matrices to extract 18 texture features for each image, and using support vector machines (SVM) classification to identify the best features for detecting infections. The optimal features identified were cluster shade, product moment, and mean intensity, achieving classification accuracy of 99.88%, 99.88%, and 99.81% respectively.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Keenan Jellison-Knock was a person who inspired Sheena Brubaker from 1994 to 2013. Brubaker wrote about her inspiration, Keenan Jellison-Knock, in a short piece that included his name and dates of life but provided no further details about who he was or what specifically inspired Brubaker.
A hybrid approach for categorizing images based on complex networks and neur...IJECEIAES
There are several methods for categorizing images, the most of which are statistical, geometric, model-based and structural methods. In this paper, a new method for describing images based on complex network models is presented. Each image contains a number of key points that can be identified through standard edge detection algorithms. To understand each image better, we can use these points to create a graph of the image. In order to facilitate the use of graphs, generated graphs are created in the form of a complex network of small-worlds. Complex grid features such as topological and dynamic features can be used to display image-related features. After generating this information, it normalizes them and uses them as suitable features for categorizing images. For this purpose, the generated information is given to the neural network. Based on these features and the use of neural networks, comparisons between new images are performed. The results of the article show that this method has a good performance in identifying similarities and finally categorizing them.
IRJET- Shape based Image Classification using Geometric –PropertiesIRJET Journal
This document discusses shape-based image classification using geometric properties. It proposes classifying shapes based on extracting geometric properties like area, perimeter, circularity, and eccentricity. The Discrete Wavelet Transform is used to remove noise and compress images. Then a K-Nearest Neighbor classifier is used to classify objects like squares, circles, ellipses and rectangles. The method is evaluated on the MPEG-7 dataset and achieves a maximum accuracy. Geometric properties provide powerful representations for shape recognition in content-based image retrieval applications.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Dictionary Based Automatic Target RecognitionIJESM JOURNAL
1. The document describes a content-based image retrieval method for automatic target recognition using a dictionary. It extracts color, texture, and shape features from images to build a feature dictionary.
2. For a test image, it extracts the same features and compares them to features in the dictionary to find the nearest matching trained image. If no match is found, it can retrieve the neighboring images in the dictionary.
3. The method provides greater recognition accuracy compared to other methods like PCA and LDA, as it can recognize targets from different angles and configurations using the correlation between multiple views in the dictionary.
Image Information Retrieval From Incomplete Queries Using Color and Shape Fea...sipij
Content based image retrieval (CBIR) is the task of searching digital images from a large database based on the extraction of features, such as color, texture and shape of the image. Most of the research in CBIR has been carried out with complete queries which were present in the database. This paper investigates utility of CBIR techniques for retrieval of incomplete and distorted queries. Studies were made in two categories of the query: first is complete and second is incomplete. The query image is considered to be distorted or incomplete image if it has some missing information, some undesirable objects, blurring, noise due to disturbance at the time of image acquisition etc. Color (hue, saturation and value (HSV) color space model) and shape (moment invariants and Fourier descriptor) features are used to represent the image. The algorithm was tested on database consisting of 1875 images. The results show that retrieval accuracy of incomplete queries is highly increased by fusing color and shape features giving precision of 79.87%. MATLAB ® 7.01 and its image processing toolbox have been used to implement the algorithm.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document summarizes a research paper that proposes an algorithm for detecting brain tumors in MRI images based on analyzing bilateral symmetry. The algorithm first performs preprocessing like smoothing and contrast enhancement. It then identifies the bilateral symmetry axis of the brain. Next, it segments the image into symmetric regions, enhancing asymmetric edges that may indicate a tumor. Experiments showed the algorithm can automatically detect tumor positions and boundaries. The algorithm leverages the fact that brain MRI of a healthy person is nearly bilaterally symmetric, while a tumor disrupts this symmetry.
This document summarizes and analyzes image segmentation and edge detection techniques for medical images. It discusses several current segmentation methods like histogram-based, edge detection, region growing, level set, and graph partitioning methods. The document then proposes a new active contour model for image segmentation that uses both edge and region information to segment images with undefined boundaries. It also discusses solving computational difficulties of models using level set theory. In conclusion, the proposed segmentation algorithms are shown to outperform some well-known methods in accuracy and processing speed.
This document describes a proposed content-based image retrieval system using backpropagation neural networks (BPNN) and k-means clustering. It begins by discussing CBIR techniques and features like color, texture, and shape. It then outlines the proposed system which includes training a BPNN on image features, validating images, and testing by querying and retrieving similar images. Performance is analyzed based on metrics like accuracy, efficiency, and classification rate. Results show the system achieves up to 98% classification accuracy within 5-6 seconds.
A comparative study on content based image retrieval methodsIJLT EMAS
Content-based image retrieval (CBIR) is a method of
finding images from a huge image database according to persons’
interests. Content-based here means that the search involves
analysis the actual content present in the image. As database of
images is growing daybyday, researchers/scholars are searching
for better techniques for retrieval of images maintaining good
efficiency. This paper presents the visual features and various
ways for image retrieval from the huge image database.
This document presents a content-based image retrieval semantic model for shaped and unshaped objects. It proposes classifying objects into two categories: shaped objects with a fixed shape like animals and objects, and unshaped objects without a fixed shape like landscapes. For unshaped objects, local regions are classified by frequency of occurrence and semantic concepts are evaluated using color, shape, and regional dissimilarity factors. For shaped objects, semantic concepts are measured using normalized color, edge detection, particle removal, and shape similarity. Several existing content-based image retrieval techniques are also briefly discussed.
This document summarizes various image segmentation techniques including region-based, edge-based, thresholding, feature-based clustering, and model-based segmentation. It provides details on each technique, including advantages and disadvantages. Region-based segmentation groups similar pixels into regions while edge-based segmentation detects boundaries between regions. Thresholding uses threshold values from histograms to segment images. Feature-based clustering groups pixels based on characteristics like intensity. Model-based segmentation uses probabilistic models like Markov random fields. The document concludes that the best technique depends on the application and image type, though thresholding is simplest computationally.
Mayank Raj - 4th Year Project on CBIR (Content Based Image Retrieval)mayankraj86
This project was my undergrad final year project in which was taken from my internship at IIIT Ahmedabad, India. Little to know CBIR now being utilized everywhere in the image retrieval world. Google images do a great job of recognizing color palates.
A Review of Feature Extraction Techniques for CBIR based on SVMIJEEE
As with the advancement of multimedia technologies, users are not gratified with the conventional retrieval system techniques. So a application “Content Based Image Retrieval System” is introduced. CBIR is the application to retrieve the images or to search the digital images from the large database .The term “content” deals with the colour, shape, texture and all the information which is extracted from the image itself. This paper reviews the CBIR system which uses SVM classifier based algorithms for feature extraction phase.
An evaluation approach for detection of contours with 4 d images a revieweSAT Journals
Abstract Abstract This paper presents a survey of contour detection and the actual use of contour in image processing. Image processing is
an enhanced area in computer science. Contour detection is the part of image processing. Contours are highly depends on quality
of an image. Contour is nothing but the simple boundaries or outlines in an image. Contour detection is nearly related with image
segmentation, classification and recognition of any object in an image. With help of contour detection we can achieve the high
accuracy of the results. Object recognition image retrieval uses the concept of contour detection to achieve the high accuracy in
the results, so it’s an enhanced and popular method in image processing. Active contour model is also one of the main techniques
in contour detection. Active contour is one of the successful models in image processing. This is a modified method of contour
detection. It consists of evolving an image with help of boundaries. Active contour model is also called as snake. Contour
detection plays an important role in recognition.
Keywords: 4D images, Contour Detection, Image Segmentation, Image Classification etc…
A Review of Some Local Feature Detection AlgorithmsCSCJournals
This document reviews and summarizes local feature detection algorithms published prior to 2010. It begins by classifying methods into those for grayscale versus color images, with grayscale methods further divided. Edges are defined and classified based on 1D and 2D features. Several important edge detection techniques are then introduced, including approaches based on gradient, orientation analysis, model fitting, fuzzy logic, statistical learning, morphology, wavelets, diffusion, and more. Canny's algorithm is discussed as an influential optimal approach. The challenges of edge detection in color images are also reviewed.
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.
A novel tool for stereo matching of imageseSAT Journals
Abstract Stereo matching techniques play an important role in many real world applications like robot stereo vision and image sequence analysis. From given pair of stereo pairs of images, it is possible to have matching techniques to obtain image descriptors or phenomena to compare the images. The goal of stereo matching can be achieved using either relational matching or feature or signal. However, the signal approach is most widely used. Recently Lemmens [10] provided a comprehensive review of many stereo matching techniques. In this paper we implement the techniques that can help in the real world. We build a prototype application that demonstrates the proof of concept. The empirical results revealed that the proposed application has good utility. Keywords – Stereo images, stereo matching,
1. V.Harichandana, Dr.Sandeep.V.M / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1315-1319
Efficient And Robust Shape Signatures For Object Recognition
V.HARICHANDANA1 Dr.SANDEEP.V.M2
ECE Dept., JPNCE, Mahabubnagar, Andhra Pradesh1
HOD & Prof.,ECE Dept.,JPNCE, Mahabubnagar, Andhra Pradesh2
Abstract-
Content Based Image Retrieval (CBIR) This paper aims at addressing the CBIR
is an important issue in the computer vision challenges using only the shape signatures. This
community. Both visual and textual descriptions allows us to neglect the other information about the
are employed when the user formulates his object, such as color and texture while retrieving
queries. Shape feature is one of the most the images, enabling us to use silhouette of the
important visual features. The shape feature is images instead of the images themselves. The
essential as it corresponds to the region of paper proposes various shape signatures that help
interest in images. Consequently, the shape in achieving better recall rates & precision. The
representation is fundamental. The shape paper is arranged as follows: The next section
comparisons must be compact and accurate, and provides the necessary background for CBIR.
must own properties of invariance to several Section 3 deals with the shape descriptors under
geometric transformations such as translation, use and the performance of the present work are
rotation and scaling, though the representation evaluated in section 4.The paper are concluded in
itself may be variant to rotation. This paper section 5.
presents simple, efficient and shape descriptors
for efficient image mining. The main strength of II. BACKGROUND
the method is its simplicity. Shape analysis involves several important
tasks, starting from image acquisition, reaching to
Keywords- Distance mapping, Image mining, shape classification. This section gives an overview
Moment invariance, Object Recognition, Shape of three major tasks of shape analysis problem:
Descriptor, Shape Recognition, Shape signature. 1) Shape Description: Characterizes the shape
and generates a shape descriptor vector (also called
I. INTRODUCTION feature vector) from a given shape.
In recent years, content based image 2) Shape Similarity: Establishes the criteria to
retrieval has been studied with more attention as allow objective measures of how much two shapes
huge amounts of image data accumulate in various are similar to each other.
fields, e.g., medical images, satellite images, art 3) Shape Recognition: Labels the class to the
collections, commercial images and general input shape.
photographs. Image databases are usually very big,
and in most cases, the images are indexed only by 2.1 Shape Description:
keywords given by a human. Although keywords The problem of shape analysis has been
are the most useful in retrieving images that a user pursued by many authors, thus, resulting in a great
wants, sometimes the keyword approach is not amount of research. Recent review papers [6], [8]
sufficient. Instead, Query-by-example or pictorial- as well as books [2], [3] provide a good resource of
query approaches make the system return similar references. In most of the studies, the terms shape
images to the example image given by a user. The representation and descriptions are used
example images can be a photograph, user-painted interchangeably. Since some of the representation
example, or line- drawing sketch. methods are inherently used as shape descriptors,
Searching for images using shape features there is no well-defined separation between the
has attracted much attention. Shape representation shape representation and description. However,
and description is a difficult task. This is because shape representation and description methods are
when a 3-D real world object is projected onto a 2- defined in [1] as follows. Shape representation
D image plane, one dimension of object result in non-numeric values of the original shape.
information is lost. As a result, the shape extracted Shape description refers to the methods that result
from the image only partially represents the in a numeric values and is a step subsequent to
projected object. To make the problem even more shape representation. For the sake of simplicity, we
complex, shape is often corrupted with noise, consider the representation and description together
defects, arbitrary distortion and occlusion. There throughout the section and refer them as shape
are many shape representation and description description methods.
techniques in the literature.
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2. V.Harichandana, Dr.Sandeep.V.M / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1315-1319
Shape description methods can be satisfied by the shape representation. With the
classified according to the use of shape boundary reference to [1], different similarity methods are
points or the interior of the shape: Region based Minkowsky Distance, Hausdorff Distance,
methods and Boundary based methods. Bottleneck Distance, Turning Function Distance,
Frechet Distance, Nonlinear Elastic Matching
1) Region Based Methods: Region based shape Distance, and Reflection Distance.
descriptors express pixel distribution within a 2-D
object region. It describes a complex object 2.3 Shape Recognition
consisting of multiple disconnected regions as well Shape analysis systems extensively use
as a simple object with or without holes. Since it is the methodologies of pattern recognition, which
based on the regional property of an object, the assigns an unknown sample into a pre-defined
descriptor is insensitive to noise that may be class. With reference to [4], numerous techniques
introduced inevitably in the process of for pattern recognition can be investigated in four
segmentation. Region based methods classified in general approaches:
[3] as follows: Moments, Angular Radial 1. Template Matching,
Transformation, Shape Decomposition, Shape 2. Statistical Techniques,
Matrices and Vectors, Medial Axis Transform, 3. Structural Techniques,
Bounding Regions, Scalar Shape Descriptors. 4. Neural Networks.
The above approaches are neither necessarily
2) Boundary Based Methods: Boundary based independent nor disjoint from each other.
shape description methods exploit only objects Occasionally, a recognition technique in one
boundary information. The shape properties of approach can also be considered to be a member of
object boundary are crucial to human perception in other approaches.
judging shape similarity and recognition. Many
authors, who study on the human visual perception III. EFFICIENT AND ROBUST SHAPE
system, agree on the significance of high curvature DESCRIPTORS
points of the shape boundary in visual perception. Shape based image retrieval primarily
In the psychological experiments, it is suggested involves three steps: shape descriptor, shape
that corners have high information content and, for similarity measures and shape recognition.
the purpose of shape description, corners are used 3.1Shape Descriptor:
as points of high curvature. Therefore, the shape There are generally two types of shape
boundary contains more information than the shape representations: one is contour based and other is
interior, in terms of perception. Boundary based region based methods. Contour based method need
methods classified in [3] as follows: Polygon extraction of boundary information which in some
Approximation, Scale Space Filtering, Stochastic cases may not available. Region based methods,
Representation, Boundary Approximation, Set of however, do not necessary rely on shape boundary
Boundary Points, Fourier Descriptors, Coding, and information, but they do not reflect local features
Simple Boundary Functions. on shape. So in this experiment for generic
purposes, both types of shape representation are
2.2. Shape Similarity Measurements necessary.
Many pattern matching and recognition 1) Scalar Shape Descriptor: The large number of
techniques are based on a similarity measures scalar shape description techniques is presented by
between patterns. A similarity measure is a heuristic approaches, which yield acceptable results
function defined on pairs of patterns indicating the for description of simple shapes. A shape
degree of resemblance between the patterns. It is description method generates a shape descriptor
possible that our prior knowledge of objects plays a feature vector from a given shape. The required
significant role in our similarity judgments, a role properties of a shape description scheme are
which may vary considerably depending on the invariance to translation, scale and rotation. Scalar
shapes we view. Since perceptual similarity is not a shape descriptor includes the following features
well-known phenomenon, none of the available like eccentricity and aspect ratio.
similarity measures are fully consistent with the
Human Visual System. 2) Simple Boundary Functions: The following
In this section, we list some desirable descriptors are mostly based on geometric
properties of similarity measures. Depending on the properties of the boundary. All of them are
application, a property, which is useful in some sensitive to image resolution. The following are
cases, may be undesirable in some other cases. some of the geometric descriptors like centroid
Combinations of properties may be contradictory. distance and circularity.
While some of the properties are satisfied by the
distance function and the algorithm used in 3) Shape signature by level set method: The level
similarity calculation, the others are inherently set technique is a geometric deformable model
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3. V.Harichandana, Dr.Sandeep.V.M / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1315-1319
implemented to segment a given image to extract three features i.e., eccentricity, circularity and
the region of interest. The output of this technique centric distance for the database. These features for
is a distance mapped function wherein the the images in the database are computed and
boundary of the object is zero level set and other stored. The features of the query image are
points are assigned signed distance from the computed and the Euclidian distance to the mean of
boundary of the object segmented by level set the features for each class is then computed. The
techniques [10], [9], and [11]. query image is classified through Nearest Neighbor
The shape signatures are obtained from method. The retrieval rate was found to be poor
the distance mapped level set function. The number i.e.60%. This low retrieval rate alarmed us about
of points with different distance from the boundary the inadequacy of feature set and 3 more features-
can be a good shape signature. Here, number of aspect ratio, centroid distance & distance mapped
pixels on the object boundary I0, unit distance away signatures, were added.
from boundary I1 and two distances away from
boundary I2 has unique relationship that depends on
the shape of object. The normalize difference are
computed by
City block distance mapping is more suitable for
this shape signature than Euclidean distances. This
provides an additional advantage by reducing the
computational complexity. Fig1: Query: CUP
3.2 Shape similarity
The shape descriptors eccentricity,
elongatedness, centroid distance, circularity, r10, r20,
r21, provides an excellent feature set in
discriminative the shape of different classes. It
provides a large distance between classes and at the
same time maintains lower distances for objects
belonging to same class.
3.3. Shape recognition
Shape of object has a strong connection to
image retrieval, where the task is to retrieve a
“matching” image from a (possibly large) database.
The best match can then be determined after the
objects present have been recognized.
1) Feature analysis and matching technique Fig2: Query: ELEPHANT
The simplest way of shape recognition is
based on matching the stored prototypes against the Some sample retrieved images are shown in figures
unknown shape to be recognized. General 1 and 2. Here, in each set, the top image is the
speaking, matching operations determines the query image and top 20 retrieved images in the
degree of similarity between two vectors in the descending order of match are shown. The scale
feature space. The set of features those represents a and rotation invariance of the retrieval can be easily
characteristic portion of a shape or a group of observed. The mismatched shapes have some
shapes is compared to the feature vector of the resemblances to the query image.
ideal shape class. The description that matches
most “closely” according to the distance measure Many different measures for evaluating
provides recognition. the performance of image retrieval systems have
been proposed. The measures require a collection
3.4. Performance evolution: images in database and a query image. The
Experiment deals with 16 classes of common retrieval performance measures –precision
images in database each class containing 20 and recall are used to evaluate.
images. The experiment started considering only
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4. V.Harichandana, Dr.Sandeep.V.M / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1315-1319
1) Precision: Precision is the fraction of the shapes The overall performance of our method is
retrieved that are relevant to the users’ measured in terms of Recall rate & Precision. The
requirements. rigorous experiments are conducted to evaluate the
performance. The table 1 shows the results of
retrieving top 5, top 10, top 15 & top 20 shapes
from the database. With all the features discussed,
2) Recall: Recall is the fraction of the shapes that the retrieval rate has increased. From the table it
are relevant to the query that are successfully can be seen that the monotonous decrease in
retrieved. precision with increase in no. of images retrieved
indicates that the result are not accidental.
TABLE 1
Recall rates & Precision for various query shapes.
Relevant Total Recall Precision
S.No Input Top- Top- Top- Top- Releva
nt R(20) P(5) P(10) P(15) P(20)
5 10 15 20
1 Apple 5 9 13 14 20 70% 100% 90% 87% 70%
2 Bat 5 10 13 15 20 75% 100% 100% 87% 75%
3 Bottle 5 10 15 17 20 85% 100% 100% 100% 85%
4 Car 5 10 15 17 20 85% 100% 100% 100% 85%
5 Child 5 8 12 14 20 70% 100% 80% 80% 70%
6 Cup 5 10 15 16 20 80% 100% 100% 100% 80%
7 Box 5 10 15 20 20 100% 100% 100% 100% 100%
8 Flower 5 10 13 13 20 65% 100% 100% 87% 65%
Elepha
9 5 10 15 17 20 85% 100% 100% 100% 85%
nt
10 Horse 5 10 13 14 20 70% 100% 100% 87% 70%
11 Snail 5 10 12 12 20 60% 100% 100% 80% 60%
12 Teddy 5 10 13 15 20 75% 100% 100% 87% 75%
Average 76.6 100 97.5 91.5 76.6
IV. CONCLUSION & FUTURE WORK query, then repeating the search with the new
Shape is one of the most valuable features information.
to identify or describe objects represented in As a major conclusion we stand that our
images. This paper presents a simple and efficient method demonstrated usefulness and effectiveness
method based on a few set of image features to for both retrieval and recognition purpose,
describe shapes. This method aims to be simple and particularly if taken into account its simplicity.
to result in a short description.
Several improvements are intended to be References:
carried as future work. A first one is to learn [1] Chan Tony F, Vese Lumanita A, “Active
feature weights using, as for instance, evolutionary contours without edge”, IEEE Trans. “On
algorithms (e.g. genetic algorithms) to properly Image processing”, 10(2): 266-277, 2001.
tune the used similarity distance metric. This [2] L. F. Costa and R. M. Cesar Jr. “Shape
process is expected to increase the accuracy of the Analysis and Classification: Theory and
classifier for a given dataset. These results can be Practice”. CRC Press, 2001.
also valuable for retrieval purposes if these weights [3] R. O. Duda, P. E. Hart, and D. G. Stork.
demonstrate stability among several datasets. “Pattern Classification”. John Wiley and
Another improvement to the retrieval Sons”, Inc., New York, 2001.
process is to make use of relevance feedback, [4] M. Hagedoorn. “Pattern Matching Using
where the user progressively refines the search Similarity Measures”, PhD Thesis.Utrecht
results by marking images in the results as University, 2000.
"relevant", "not relevant", or "neutral" to the search
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5. V.Harichandana, Dr.Sandeep.V.M / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1315-1319
[5] A.E. Johnson and M. Hebert.”Recognizing Processing, Pattern Recognition, Communication,
objects by matching oriented points”, Electromagnetics. He is Reviewer for Pattern
Proc. IEEE “Conf. Computer Vision and Recognition Letters (PRL). He acted as Reviewer
Pattern Recognition”, pages 684–689, for many International Conferences.He has 24
1997. years of teaching experience. He is member of
[6] S. Loncaric. “A survey of shape analysis LMIST – Life Member Instrument Society of India
techniques”.” Pattern Recognition”, (IISc, Bangalore). And he guided more than 100
31:983–1001, 1998. projects at UG level and 35 at PG level.And
[7] Nafiz Arica.”SHAPE: Representation, published more than 10 papers in international
Description, Similarity and Recognition”, journals and 9 Conference Proceedings.
PhD Thesis.
[8] T. Pavlidis.” A review of algorithms for
shape analysis”. “Computer Graphics
Image Processing”, 7:243–258, 1978.
[9] Sandeep V.M, Subhash Kulkarni,
Vinayadatt Kohir,”Level set issues for
efficient image segmentation”,
International Journal of “Image and Data
Fusion”, Vol2, No1, March 2011, 75-92.
[10] Sandeep V.M, M, Narayana, Subhash
Kulkarni, ”A Scale & rotation invariant
fast image mining for shapes’, IEEE
conference on “AI tools in
Engineering”,Pune,India,2008
[11] Sandeep V.M, Subhash Kulkarni, ”Curve
invariant fast distance mapping technique
for level sets”, IEEE’s ICSIP 2006, Hubli,
India,777-780,2006.
Authors Information:
1.V.Hari Chandana Pursuing M.Tech(DSCE)
from JayaPrakash Narayana College of
Engineering B.Tech(ECE) from JayaPrakash
Narayana College of Engineering Currently she is
working as Assistant Professor at Jayaprakash
narayan college of engineering And has 2 years of
Experience in teaching . Her areas of interest
include,Image Processing ,wireless networks,
signal processing.
2. Dr. Sandeep V.M. completed Ph.D in Faculty of
Electrical and Electronics Engineering, Sciences,
from Visveswaraiah Technological University,
Belgaum, and M.Tech from Gulbarga University
and B.Tech from Gulbarga University. His research
interests are in the areas of Signal and Image
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