Abstract A human face conveys a lot of information about the identity and emotional state of the person. So now a day’s face recognition has become an interesting and challenging problem. Face recognition plays a vital role in many applications such as authenticating a person, system security, verification and identification for law enforcement and personal identification among others. So our research work mainly consists of three parts, namely face representation, feature extraction and classification. The first part, Face representation represents how to model a face and check which algorithms can be used for detection and recognition purpose. In the second phase i.e. feature extraction phase we compute the unique features of the face image. In the classification phase the computed DLBP face image is compared with the images from the database. In our research work, we use Double Coding Local Binary Patterns to evaluate face recognition which concentrate over both the shape and texture information to represent face images for person independent face recognition. The face area is firstly cut into small regions from which Local Binary Patterns (LBP), then we compute histograms to generate LBP image then we compute single oriented mean image from which we again compute histogram values small regions and at last concatenated into a single feature vectors and generate D-LBP image. This feature are used for the representation of the face and to measure similarities between images. Keywords: local binary pattern (LBP), double coding local binary pattern (D-LBP), features extraction, classification, pattern recognition, histogram, feature vector.
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.
J.K.Jeevitha ,B.Karthika,E.Devipriya "Face Recognition using LDN Code", International Research Journal of Engineering and Technology (IRJET), Volume2,issue-01 April 2015.e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net
Abstract
LDN characterizes both the texture and contrast information of facial components in a compact way, producing a more discriminative code than other available methods. An LDN code is obtained by computing the edge response values in 8 directions at each pixel with the aid of a compass mask. Image analysis and understanding has recently received significant attention, especially during the past several years. At least two reasons can be accounted for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after nearly 30 years of research. In this paper we propose a novel local feature descriptor, called Local Directional Number Pattern (LDN), for face analysis, i.e., face and expression recognition. LDN characterizes both the texture and contrast information of facial components in a compact way, producing a more discriminative code than other available methods.
Facial recognition using modified local binary pattern and random forestijaia
This paper presents an efficient algorithm for face recognition using the local binary pattern (LBP) and
random forest (RF). The novelty of this research effort is that a modified local binary pattern (MLBP),
which combines both the sign and magnitude features for the improvement of facial texture classification
performance, is applied. Furthermore, RF is used to select the most important features from the extracted
feature sequence. The performance of the proposed scheme is validated using a complex dataset, namely
Craniofacial Longitudinal Morphological Face (MORPH) Album 1 dataset
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.
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.
DEVELOPMENT OF AN ALPHABETIC CHARACTER RECOGNITION SYSTEM USING MATLAB FOR BA...Mohammad Liton Hossain
Character recognition technique, associates a symbolic identity with the image of the character, is an important area in pattern recognition and image processing. The principal idea here is to convert raw images (scanned from document, typed, pictured etcetera) into editable text like html, doc, txt or other formats. There is a very limited number of Bangla Character recognition system, if available they can’t recognize the whole alphabet set. Motivated by this, this paper demonstrates a MATLAB based Character Recognition system from printed Bangla writings. It can also compare the characters of one image file to another one. Processing steps here involved binarization, noise removal and segmentation in various levels, features extraction and recognition.
Improving Performance of Texture Based Face Recognition Systems by Segmenting...IDES Editor
Textures play an important role in recognition of
images. This paper investigates the efficiency of performance
of three texture based feature extraction methods for face
recognition. The methods for comparative study are Grey Level
Co_occurence Matrix (GLCM), Local Binary Pattern (LBP)
and Elliptical Local Binary Template (ELBT). Experiments
were conducted on a facial expression database, Japanese
Female Facial Expression (JAFFE). With all facial expressions
LBP with 16 vicinity pixels is found to be a better face
recognition method among the tested methods. Experimental
results show that classification based on segmenting face
region improves recognition accuracy.
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.
J.K.Jeevitha ,B.Karthika,E.Devipriya "Face Recognition using LDN Code", International Research Journal of Engineering and Technology (IRJET), Volume2,issue-01 April 2015.e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net
Abstract
LDN characterizes both the texture and contrast information of facial components in a compact way, producing a more discriminative code than other available methods. An LDN code is obtained by computing the edge response values in 8 directions at each pixel with the aid of a compass mask. Image analysis and understanding has recently received significant attention, especially during the past several years. At least two reasons can be accounted for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after nearly 30 years of research. In this paper we propose a novel local feature descriptor, called Local Directional Number Pattern (LDN), for face analysis, i.e., face and expression recognition. LDN characterizes both the texture and contrast information of facial components in a compact way, producing a more discriminative code than other available methods.
Facial recognition using modified local binary pattern and random forestijaia
This paper presents an efficient algorithm for face recognition using the local binary pattern (LBP) and
random forest (RF). The novelty of this research effort is that a modified local binary pattern (MLBP),
which combines both the sign and magnitude features for the improvement of facial texture classification
performance, is applied. Furthermore, RF is used to select the most important features from the extracted
feature sequence. The performance of the proposed scheme is validated using a complex dataset, namely
Craniofacial Longitudinal Morphological Face (MORPH) Album 1 dataset
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.
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.
DEVELOPMENT OF AN ALPHABETIC CHARACTER RECOGNITION SYSTEM USING MATLAB FOR BA...Mohammad Liton Hossain
Character recognition technique, associates a symbolic identity with the image of the character, is an important area in pattern recognition and image processing. The principal idea here is to convert raw images (scanned from document, typed, pictured etcetera) into editable text like html, doc, txt or other formats. There is a very limited number of Bangla Character recognition system, if available they can’t recognize the whole alphabet set. Motivated by this, this paper demonstrates a MATLAB based Character Recognition system from printed Bangla writings. It can also compare the characters of one image file to another one. Processing steps here involved binarization, noise removal and segmentation in various levels, features extraction and recognition.
Improving Performance of Texture Based Face Recognition Systems by Segmenting...IDES Editor
Textures play an important role in recognition of
images. This paper investigates the efficiency of performance
of three texture based feature extraction methods for face
recognition. The methods for comparative study are Grey Level
Co_occurence Matrix (GLCM), Local Binary Pattern (LBP)
and Elliptical Local Binary Template (ELBT). Experiments
were conducted on a facial expression database, Japanese
Female Facial Expression (JAFFE). With all facial expressions
LBP with 16 vicinity pixels is found to be a better face
recognition method among the tested methods. Experimental
results show that classification based on segmenting face
region improves recognition accuracy.
A Color Boosted Local Feature Extraction Method for Mobile Product Searchidescitation
Mobile visual search is one popular and promising research area for product
search and image retrieval. We present a novel color boosted local feature extraction
method based on the SIFT descriptor, which not only maintains robustness and
repeatability to certain imaging condition variation, but also retains the salient color and
local pattern of the apparel products. The experiments demonstrate the effectiveness of our
approach, and show that the proposed method outperforms those available methods on all
tested retrieval rates.
Improvement of Objective Image Quality Evaluation Applying Colour Differences...CSCJournals
In this work perceived colour distance is employed in a simple and functional way in order to improve full-reference image quality assessment. The difference between colours in the CIELAB colour space is employed as perceived colour distance. This quantity is used to process images that are to be feed to full-reference image quality algorithms. This image processing stage consists of identifying the image regions or pixels that are expected to be perceived identically by a human observer in both the reference image and the image having its quality evaluated. In order to verify the validity of the proposal, objective scores are compared with subjective ones for public available image databases. Despite being a very simple strategy, the proposed approach was effective to improve the agreement between subjective and the SSIM (Structural Similarity Index Metric) objective score.
Face skin color based recognition using local spectral and gray scale featureseSAT Journals
Abstract Human face conveys more information about identity of person. Human face recognition is one of the most challenging problem and it can be used in many applications at different security places in airports, defense and banking sectors etc.In this work used colored features obtained from color segmentation because in real time scenario color provides the more information than gray scale image but it has a drawback. To overcome this drawback gray scale feature extracted from co-occurrence matrix of an image and for efficient face recognition of human Face under different illumination conditions spectral features can be extracted from face texture. These three feature vectors concatenated into a single feature vector and applied Lenc-Kral matching technique to measure similarity between the database and query image, the similarity is high then face is recognized. Keywords: Face recognition, illumination condition, local texture features, color segmentation.
Hashing is popular technique of image authentication to identify malicious attacks and it also allows appearance changes in an image in controlled way. Image hashing is quality summarization of images. Quality summarization implies extraction and representation of powerful low level features in compact form. Proposed adaptive CSLBP compressed hashing method uses modified CSLBP (Center Symmetric Local Binary Pattern) as a basic method for texture extraction and color weight factor derived from L*a*b* color space. Image hash is generated from image texture. Color weight factors are used adaptively in average and difference forms to enhance discrimination capability of hash. For smooth region, averaging of colours used while for non-smooth region, color differencing is used. Adaptive CSLBP histogram is a compressed form of CSLBP and its quality is improved by adaptive color weight factor. Experimental results are demonstrated with two benchmarks, normalized hamming distance and ROC characteristics. Proposed method successfully differentiate between content change and content persevering modifications for color images.
Local feature extraction based facial emotion recognition: A surveyIJECEIAES
Notwithstanding the recent technological advancement, the identification of facial and emotional expressions is still one of the greatest challenges scientists have ever faced. Generally, the human face is identified as a composition made up of textures arranged in micro-patterns. Currently, there has been a tremendous increase in the use of Local Binary Pattern based texture algorithms which have invariably been identified to being essential in the completion of a variety of tasks and in the extraction of essential attributes from an image. Over the years, lots of LBP variants have been literally reviewed. However, what is left is a thorough and comprehensive analysis of their independent performance. This research work aims at filling this gap by performing a large-scale performance evaluation of 46 recent state-of-the-art LBP variants for facial expression recognition. Extensive experimental results on the well-known challenging and benchmark KDEF, JAFFE, CK and MUG databases taken under different facial expression conditions, indicate that a number of evaluated state-of-the-art LBP-like methods achieve promising results, which are better or competitive than several recent state-of-the-art facial recognition systems. Recognition rates of 100%, 98.57%, 95.92% and 100% have been reached for CK, JAFFE, KDEF and MUG databases, respectively.
In image processing colour segmentation is used to extract features of an object in both special and frequency domains. The objective of this paper is to use colour segmentation technique to identify the defected region of fruits and corresponding percentage of frequency components from its Spectrogram. Here we separate the defective portion of fruit using colour segmentation technique taking four images from four directions to get the appropriate result of 3D images. The percentage of the defective portion is determined using scatterplot of the colours of the image. Next, we apply the similar concept to spectrogram of an image (even applicable in speech signal) to extract the percentages of frequency components of the signal.
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.
A Novel Facial Recognition Method using Discrete Wavelet Transform Multiresolution Pyramid..........1
G. Preethi
Enhancing Energy Efficiency in WSN using Energy Potential and Energy Balancing Concepts ................. 9
Sheetalrani R. Kawale
DNS: Dynamic Network Selection Scheme for Vertical Handover in Heterogeneous Wireless Networks
.................................................................................................................................................................... 19
M. Deva Priya, D. Prithviraj and Dr. M. L Valarmathi
Implementation of Image based Flower Classification System................................................................ 35
Tanvi Kulkarni and Nilesh. J. Uke
A Survey on Knowledge Analytics of Text from Social Media .................................................................. 45
Dr. J. Akilandeswari and K. Rajalakshm
Progression of String Matching Practices in Web Mining – A Survey ..................................................... 62
Kaladevi A. C. and Nivetha S. M.
Virtualizing the Inter Communication of Clouds ...............................................................................72
Subho Roy Chowdhury, Sambit Kumar Patel, Ankita Vinod Mandekar and G. Usha Devi
Tracing the Adversaries using Packet Marking and Packet Logging ....................................................... 86
A. Santhosh and Dr. J. Senthil Kumar
An Improved Energy Efficient Clustering Algorithm for Non Availability of Spectrum in Cognitive Radio
Users ....................................................................................................................................................... 101
Hierarchical Approach for Total Variation Digital Image InpaintingIJCSEA Journal
The art of recovering an image from damage in an undetectable form is known as inpainting. The manual work of inpainting is most often a very time consum ing process. Due to digitalization of this technique, it is automatic and faster. In this paper, after the user selects the regions to be reconstructed, the algorithm automatically reconstruct the lost regions with the help of the information surrounding them. The existing methods perform very well when the region to be reconstructed is very small, but fails in proper reconstruction as the area increases. This paper describes a Hierarchical method by which the area to be inpainted is reduced in multiple levels and Total Variation(TV) method is used to inpaint in each level. This algorithm gives better performance when compared with other existing algorithms such as nearest neighbor interpolation, Inpainting through Blurring and Sobolev Inpainting.
To give sheltered and symphonies stream of movement , particular standards are given by each administration . There are certain rules laid down by the government of most of the countries for the safe, systematic and orderly movement of vehicles and traffic. Many of these rules are displayed through traffic signs to help the driver to follow while driving. The Traffic Sign Detection and Recognition (TSDR) perceives the activity signs by dissecting pictures /recordings taken from a camera system and helps in detecting and recognizing the traffic signs by analyzing images / videos captured by the camera planted on the car. This framework has many elements which help the driver in enhancing the well being and solace; today it is broadly utilized as a part of discovery and acknowledgement of our perceived signs. The framework of system can be developed using two algorithms, first is the detection of the image followed by its recognition. In wake of getting the best possible information for recognition we utilize the diverse morphological handling procedures. Colour Image processing play an vital role in the detection of the traffic sign. In this paper we chiefly accentuation on the color/shade of the activity sign for its recognition on the grounds that in many spots a standard arrangement of hues are utilized as a part of movement sign like red is used in prohibition signs. In the directional signs blue is the foundation shading in the directional signs and yellow is chosen for warning signs.
An Automatic Color Feature Vector Classification Based on Clustering MethodRSIS International
In computer vision application, visual features such as
shape, color and texture are extracted to characterize images.
Each of the features is represented using one or more feature
descriptors. One of the important requirements in image
retrieval, indexing, classification, clustering, etc. is extracting
efficient features from images. The color feature is one of the
most widely used visual features. Use of color histogram is the
most common way for representing color feature. One of
disadvantage of the color histogram is that it does not take the
color spatial distribution into consideration. In this paper an
automatic color feature vector classification based on clustering
approach is presented, which effectively describes the spatial
information of color features. The image retrieval results are
compare to improved color feature vector show the acceptable
efficiency of this approach. It propose an automatic color feature
vector classification of satellite images using clustering approach.
The intention is to study cluster a set of satellite images in several
categories on the color similarity basis. The images are processed
using LAB color space in the feature extraction stage. The
resulted color-based feature vectors are clustered using an
automatic unsupervised classification algorithm. Some
experiments based on the proposed recognition technique have
also been performed. More research, however, is needed to
identify and reduce uncertainties in the image processing chain
to improve classification accuracy. The mathematical training
and prediction analysis of a general familiarity with satellite
classifications meet typical map accuracy standards.
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.
A Color Boosted Local Feature Extraction Method for Mobile Product Searchidescitation
Mobile visual search is one popular and promising research area for product
search and image retrieval. We present a novel color boosted local feature extraction
method based on the SIFT descriptor, which not only maintains robustness and
repeatability to certain imaging condition variation, but also retains the salient color and
local pattern of the apparel products. The experiments demonstrate the effectiveness of our
approach, and show that the proposed method outperforms those available methods on all
tested retrieval rates.
Improvement of Objective Image Quality Evaluation Applying Colour Differences...CSCJournals
In this work perceived colour distance is employed in a simple and functional way in order to improve full-reference image quality assessment. The difference between colours in the CIELAB colour space is employed as perceived colour distance. This quantity is used to process images that are to be feed to full-reference image quality algorithms. This image processing stage consists of identifying the image regions or pixels that are expected to be perceived identically by a human observer in both the reference image and the image having its quality evaluated. In order to verify the validity of the proposal, objective scores are compared with subjective ones for public available image databases. Despite being a very simple strategy, the proposed approach was effective to improve the agreement between subjective and the SSIM (Structural Similarity Index Metric) objective score.
Face skin color based recognition using local spectral and gray scale featureseSAT Journals
Abstract Human face conveys more information about identity of person. Human face recognition is one of the most challenging problem and it can be used in many applications at different security places in airports, defense and banking sectors etc.In this work used colored features obtained from color segmentation because in real time scenario color provides the more information than gray scale image but it has a drawback. To overcome this drawback gray scale feature extracted from co-occurrence matrix of an image and for efficient face recognition of human Face under different illumination conditions spectral features can be extracted from face texture. These three feature vectors concatenated into a single feature vector and applied Lenc-Kral matching technique to measure similarity between the database and query image, the similarity is high then face is recognized. Keywords: Face recognition, illumination condition, local texture features, color segmentation.
Hashing is popular technique of image authentication to identify malicious attacks and it also allows appearance changes in an image in controlled way. Image hashing is quality summarization of images. Quality summarization implies extraction and representation of powerful low level features in compact form. Proposed adaptive CSLBP compressed hashing method uses modified CSLBP (Center Symmetric Local Binary Pattern) as a basic method for texture extraction and color weight factor derived from L*a*b* color space. Image hash is generated from image texture. Color weight factors are used adaptively in average and difference forms to enhance discrimination capability of hash. For smooth region, averaging of colours used while for non-smooth region, color differencing is used. Adaptive CSLBP histogram is a compressed form of CSLBP and its quality is improved by adaptive color weight factor. Experimental results are demonstrated with two benchmarks, normalized hamming distance and ROC characteristics. Proposed method successfully differentiate between content change and content persevering modifications for color images.
Local feature extraction based facial emotion recognition: A surveyIJECEIAES
Notwithstanding the recent technological advancement, the identification of facial and emotional expressions is still one of the greatest challenges scientists have ever faced. Generally, the human face is identified as a composition made up of textures arranged in micro-patterns. Currently, there has been a tremendous increase in the use of Local Binary Pattern based texture algorithms which have invariably been identified to being essential in the completion of a variety of tasks and in the extraction of essential attributes from an image. Over the years, lots of LBP variants have been literally reviewed. However, what is left is a thorough and comprehensive analysis of their independent performance. This research work aims at filling this gap by performing a large-scale performance evaluation of 46 recent state-of-the-art LBP variants for facial expression recognition. Extensive experimental results on the well-known challenging and benchmark KDEF, JAFFE, CK and MUG databases taken under different facial expression conditions, indicate that a number of evaluated state-of-the-art LBP-like methods achieve promising results, which are better or competitive than several recent state-of-the-art facial recognition systems. Recognition rates of 100%, 98.57%, 95.92% and 100% have been reached for CK, JAFFE, KDEF and MUG databases, respectively.
In image processing colour segmentation is used to extract features of an object in both special and frequency domains. The objective of this paper is to use colour segmentation technique to identify the defected region of fruits and corresponding percentage of frequency components from its Spectrogram. Here we separate the defective portion of fruit using colour segmentation technique taking four images from four directions to get the appropriate result of 3D images. The percentage of the defective portion is determined using scatterplot of the colours of the image. Next, we apply the similar concept to spectrogram of an image (even applicable in speech signal) to extract the percentages of frequency components of the signal.
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.
A Novel Facial Recognition Method using Discrete Wavelet Transform Multiresolution Pyramid..........1
G. Preethi
Enhancing Energy Efficiency in WSN using Energy Potential and Energy Balancing Concepts ................. 9
Sheetalrani R. Kawale
DNS: Dynamic Network Selection Scheme for Vertical Handover in Heterogeneous Wireless Networks
.................................................................................................................................................................... 19
M. Deva Priya, D. Prithviraj and Dr. M. L Valarmathi
Implementation of Image based Flower Classification System................................................................ 35
Tanvi Kulkarni and Nilesh. J. Uke
A Survey on Knowledge Analytics of Text from Social Media .................................................................. 45
Dr. J. Akilandeswari and K. Rajalakshm
Progression of String Matching Practices in Web Mining – A Survey ..................................................... 62
Kaladevi A. C. and Nivetha S. M.
Virtualizing the Inter Communication of Clouds ...............................................................................72
Subho Roy Chowdhury, Sambit Kumar Patel, Ankita Vinod Mandekar and G. Usha Devi
Tracing the Adversaries using Packet Marking and Packet Logging ....................................................... 86
A. Santhosh and Dr. J. Senthil Kumar
An Improved Energy Efficient Clustering Algorithm for Non Availability of Spectrum in Cognitive Radio
Users ....................................................................................................................................................... 101
Hierarchical Approach for Total Variation Digital Image InpaintingIJCSEA Journal
The art of recovering an image from damage in an undetectable form is known as inpainting. The manual work of inpainting is most often a very time consum ing process. Due to digitalization of this technique, it is automatic and faster. In this paper, after the user selects the regions to be reconstructed, the algorithm automatically reconstruct the lost regions with the help of the information surrounding them. The existing methods perform very well when the region to be reconstructed is very small, but fails in proper reconstruction as the area increases. This paper describes a Hierarchical method by which the area to be inpainted is reduced in multiple levels and Total Variation(TV) method is used to inpaint in each level. This algorithm gives better performance when compared with other existing algorithms such as nearest neighbor interpolation, Inpainting through Blurring and Sobolev Inpainting.
To give sheltered and symphonies stream of movement , particular standards are given by each administration . There are certain rules laid down by the government of most of the countries for the safe, systematic and orderly movement of vehicles and traffic. Many of these rules are displayed through traffic signs to help the driver to follow while driving. The Traffic Sign Detection and Recognition (TSDR) perceives the activity signs by dissecting pictures /recordings taken from a camera system and helps in detecting and recognizing the traffic signs by analyzing images / videos captured by the camera planted on the car. This framework has many elements which help the driver in enhancing the well being and solace; today it is broadly utilized as a part of discovery and acknowledgement of our perceived signs. The framework of system can be developed using two algorithms, first is the detection of the image followed by its recognition. In wake of getting the best possible information for recognition we utilize the diverse morphological handling procedures. Colour Image processing play an vital role in the detection of the traffic sign. In this paper we chiefly accentuation on the color/shade of the activity sign for its recognition on the grounds that in many spots a standard arrangement of hues are utilized as a part of movement sign like red is used in prohibition signs. In the directional signs blue is the foundation shading in the directional signs and yellow is chosen for warning signs.
An Automatic Color Feature Vector Classification Based on Clustering MethodRSIS International
In computer vision application, visual features such as
shape, color and texture are extracted to characterize images.
Each of the features is represented using one or more feature
descriptors. One of the important requirements in image
retrieval, indexing, classification, clustering, etc. is extracting
efficient features from images. The color feature is one of the
most widely used visual features. Use of color histogram is the
most common way for representing color feature. One of
disadvantage of the color histogram is that it does not take the
color spatial distribution into consideration. In this paper an
automatic color feature vector classification based on clustering
approach is presented, which effectively describes the spatial
information of color features. The image retrieval results are
compare to improved color feature vector show the acceptable
efficiency of this approach. It propose an automatic color feature
vector classification of satellite images using clustering approach.
The intention is to study cluster a set of satellite images in several
categories on the color similarity basis. The images are processed
using LAB color space in the feature extraction stage. The
resulted color-based feature vectors are clustered using an
automatic unsupervised classification algorithm. Some
experiments based on the proposed recognition technique have
also been performed. More research, however, is needed to
identify and reduce uncertainties in the image processing chain
to improve classification accuracy. The mathematical training
and prediction analysis of a general familiarity with satellite
classifications meet typical map accuracy standards.
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.
Divide the examined window into cells (e.g. 16x16 pixels for each cell).
2- For each pixel in a cell, compare the pixel to each of its 8 neighbors (on its left-top, leftmiddle,
left-bottom, right-top, etc.). Follow the pixels along a circle, i.e. clockwise or counterclockwise.
3- Where the center pixel's value is greater than the neighbor's value, write "1". Otherwise,
write "0". This gives an 8-digit binary number (which is usually converted to decimal for
convenience).
4- Compute the histogram, over the cell, of the frequency of each "number" occurring (i.e.,
each combination of which pixels are smaller and which are greater than the center).
Facial Expression Recognition Using SVM Classifierijeei-iaes
Facial feature tracking and facial actions recognition from image sequence attracted great attention in computer vision field. Computational facial expression analysis is a challenging research topic in computer vision. It is required by many applications such as human-computer interaction, computer graphic animation and automatic facial expression recognition. In recent years, plenty of computer vision techniques have been developed to track or recognize the facial activities in three levels. First, in the bottom level, facial feature tracking, which usually detects and tracks prominent landmarks surrounding facial components (i.e., mouth, eyebrow, etc), captures the detailed face shape information; Second, facial actions recognition, i.e., recognize facial action units (AUs) defined in FACS, try to recognize some meaningful facial activities (i.e., lid tightener, eyebrow raiser, etc); In the top level, facial expression analysis attempts to recognize some meaningful facial activities (i.e., lid tightener, eyebrow raiser, etc); In the top level, facial expression analysis attempts to recognize facial expressions that represent the human emotion states. In this proposed algorithm initially detecting eye and mouth, features of eye and mouth are extracted using Gabor filter, (Local Binary Pattern) LBP and PCA is used to reduce the dimensions of the features. Finally SVM is used to classification of expression and facial action units.
Pose and Illumination in Face Recognition Using Enhanced Gabor LBP & PCA IJMER
This paper presents the face recognition based on Enhanced GABOR LBP and PCA. Some
of the challenges in face recognition are occlusion, pose and illumination .In this paper, we are more
focused on varying pose and illumination. We divided this algorithm into five stages. First stage finds
the fiducial points on face using Gabor filter bank as this filter is well known for illumination
compensation. Second stage applies the morphological techniques for reduce useless fiducial points.
Third stage applies the LBP on reduced fiducial points with neighborhood pixel for improving the pose
variation. Forth stage uses PCA to detect the best variance points which are necessary to characterize
the training images. The last recognition stage includes finding the Euclidean norm of the feature
weight vectors with the test weight vector. In this project, we used 20 images of 20 different persons
from ORL database for training. For testing, we used images with varying illumination, pose and
occluded images of the same training
Recognition of Facial Expressions using Local Binary Patterns of Important Fa...CSCJournals
Facial Expression Recognition is one of the exciting and challenging field; it has important applications in many areas such as data driven animation, human computer interaction and robotics. Extracting effective features from the human face is an important step for successful facial expression recognition. In this paper we have evaluated Local Binary Patterns of some important parts of human face, for person independent as well as person dependent facial expression recognition. Extensive experiments on JAFFE database are conducted. The experiment results show that person dependent method is highly accurate and outperform many existing methods.
Effect of Different Occlusion on Facial Expressions RecognitionIJERA Editor
Occlusions around facial parts complicate the task of recognizing facial expressions from their facial images. We propose facial expressions recognition method based on local facial regions, which provides better recognition rate in the presence of facial occlusions. Proposed method uses Uniform Local Binary pattern as a feature extractor, which extract discriminative features from some important parts of facial image. Feature vectors are classified using simplest classifier that is template matching with chi square distance measure. Extensive experiments are performed on JAFFE database.
Feature extraction is becoming popular in face recognition method. Face recognition is the interesting and growing area in real time applications. In last decades many of face recognitions methods has been developed. Feature extraction is the one of the emerging technique in the face recognition methods. In this method an attempt to show best faces recognition method. Here used different descriptors combination like LBP and SIFT, LBP and HOG for feature extraction. Using a single descriptor is difficult to address all variations so combining multiple features in common. Find LBP and SIFT features separately from the images and fuse them with a canonical correlation analysis and same procedure also done using LBP and HOG. The SIFT features have some limitations they don’t work well with lighting changes, quite slow, and mathematically complicated and computationally heavy. The combinations of HOG and LBP features make the system robust against some variations like illumination and expressions. Also, face recognition technique used a different classifier to extract the useful information from images to solve the problems. This paper is organized into four sections. Introduction in the first section. The second section describes feature descriptors and the third section describes proposed methods, final sections describes experiments result and conclusion phase.
tScene classification using pyramid histogram ofijcsa
Pyramid Histogram of Multi-scale Block Local Binary Pattern (PH-MBLBP) descriptor for recognizing
scene categories, is presented in this paper. We show that scene categorization, especially for indoor and
outdoor environments, requires its visual descriptor to process properties that are different from other
vision domains (e.g., SIFT descriptor used for object categorization). Our proposed PH-MBLBP satisfies
these properties and suits the scene categorization task. Since the proposed PH-MBLBP mainly encodes
micro- and macro-structures of image patterns, thus, it provides relatively more complete image descriptor
than the basic LBP operator. Moreover, our PH-MBLBP descriptor is more powerful texture descriptor
than the conventional operator and it can also be calculated extremely fast. Our experiments demonstrate
that PH-MBLBP outperforms the other descriptor such as SIFT.
A Face Recognition Using Linear-Diagonal Binary Graph Pattern Feature Extract...ijfcstjournal
Face recognition is one the most interesting topic in the field in computer vision and image processing.
Face recognition is a processing system that recognizes and identifies individuals human by their faces.
Automatic face recognition is powerful way to provide, authorized access to control their system. Face
recognition has many challenging problems (like face pose, face expression variation, illumination
variation, face orientation and noise) in the field of image analysis and computer vision. This method is
work on feature extraction part of face recognition. New way to extract face feature using LD-BGP code
operator it is like LGS and LBP feature extraction operator. In our LD-BGP-code operator work in two
direction first linear then diagonal. In both direction, its create eight digits code to every pixel of image.
Means of these two directional are taken so that is cover all neighbor of center pixel. First linear direction,
only horizontal and vertical pixel are taken. Second diagonal direction only diagonal pixels taken. In
matching phase, we use Euclidean distance to match a face image. We perform the Linear and diagonal
directional operator method on face database ORL. We get accuracy 95.3 %. LD-BGP method also works
on different type image like illuminated and expression variation image.
A FACE RECOGNITION USING LINEAR-DIAGONAL BINARY GRAPH PATTERN FEATURE EXTRACT...ijfcstjournal
Face recognition is one the most interesting topic in the field in computer vision and image processing.
Face recognition is a processing system that recognizes and identifies individuals human by their faces.
Automatic face recognition is powerful way to provide, authorized access to control their system. Face
recognition has many challenging problems (like face pose, face expression variation, illumination
variation, face orientation and noise) in the field of image analysis and computer vision. This method is
work on feature extraction part of face recognition. New way to extract face feature using LD-BGP code
operator it is like LGS and LBP feature extraction operator. In our LD-BGP-code operator work in two
direction first linear then diagonal. In both direction, its create eight digits code to every pixel of image.
Means of these two directional are taken so that is cover all neighbor of center pixel. First linear direction,
only horizontal and vertical pixel are taken. Second diagonal direction only diagonal pixels taken. In
matching phase, we use Euclidean distance to match a face image. We perform the Linear and diagonal
directional operator method on face database ORL. We get accuracy 95.3 %. LD-BGP method also works
on different type image like illuminated and expression variation image.
A FACE RECOGNITION USING LINEAR-DIAGONAL BINARY GRAPH PATTERN FEATURE EXTRACT...ijfcstjournal
Face recognition is one the most interesting topic in the field in computer vision and image processing.
Face recognition is a processing system that recognizes and identifies individuals human by their faces.
Automatic face recognition is powerful way to provide, authorized access to control their system. Face
recognition has many challenging problems (like face pose, face expression variation, illumination
variation, face orientation and noise) in the field of image analysis and computer vision. This method is
work on feature extraction part of face recognition. New way to extract face feature using LD-BGP code
operator it is like LGS and LBP feature extraction operator. In our LD-BGP-code operator work in two
direction first linear then diagonal. In both direction, its create eight digits code to every pixel of image.
Means of these two directional are taken so that is cover all neighbor of center pixel. First linear direction,
only horizontal and vertical pixel are taken. Second diagonal direction only diagonal pixels taken. In
matching phase, we use Euclidean distance to match a face image. We perform the Linear and diagonal
directional operator method on face database ORL. We get accuracy 95.3 %. LD-BGP method also works
on different type image like illuminated and expression variation image.
Illumination Invariant Face Recognition System using Local Directional Patter...Editor IJCATR
In this paper, we propose an illumination-robust face recognition system using local directional pattern images. Usually,
local pattern descriptors including local binary pattern and local directional pattern have been used in the field of the face recognition
and facial expression recognition, since local pattern descriptors have important properties to be robust against the illumination
changes and computational simplicity. Thus, this paper represents the face recognition approach that employs the local directional
pattern descriptor and two-dimensional principal analysis algorithms to achieve enhanced recognition accuracy. In particular, we
propose a novel methodology that utilizes the transformed image obtained from local directional pattern descriptor as the direct input
image of two-dimensional principal analysis algorithms, unlike that most of previous works employed the local pattern descriptors to
acquire the histogram features. The performance evaluation of proposed system was performed using well-known approaches such as
principal component analysis and Gabor-wavelets based on local binary pattern, and publicly available databases including the Yale B
database and the CMU-PIE database were employed.
In this paper, an attempt has been made to extract texture
features from facial images using an improved method of
Illumination Invariant Feature Descriptor. The proposed local
ternary Pattern based feature extractor viz., Steady Illumination
Local Ternary Pattern (SIcLTP) has been used to extract texture
features from Indian face database. The similarity matching
between two extracted feature sets has been obtained using Zero
Mean Sum of Squared Differences (ZSSD). The RGB facial images
are first converted into the YIQ colour space to reduce the
redundancy of the RGB images. The result obtained has been
analysed using Receiver Operating Characteristic curve, and is
found to be promising. Finally the results are validated with
standard local binary pattern (LBP) extractor.
Mechanical properties of hybrid fiber reinforced concrete for pavementseSAT Journals
Abstract
The effect of addition of mono fibers and hybrid fibers on the mechanical properties of concrete mixture is studied in the present
investigation. Steel fibers of 1% and polypropylene fibers 0.036% were added individually to the concrete mixture as mono fibers and
then they were added together to form a hybrid fiber reinforced concrete. Mechanical properties such as compressive, split tensile and
flexural strength were determined. The results show that hybrid fibers improve the compressive strength marginally as compared to
mono fibers. Whereas, hybridization improves split tensile strength and flexural strength noticeably.
Keywords:-Hybridization, mono fibers, steel fiber, polypropylene fiber, Improvement in mechanical properties.
Material management in construction – a case studyeSAT Journals
Abstract
The objective of the present study is to understand about all the problems occurring in the company because of improper application
of material management. In construction project operation, often there is a project cost variance in terms of the material, equipments,
manpower, subcontractor, overhead cost, and general condition. Material is the main component in construction projects. Therefore,
if the material management is not properly managed it will create a project cost variance. Project cost can be controlled by taking
corrective actions towards the cost variance. Therefore a methodology is used to diagnose and evaluate the procurement process
involved in material management and launch a continuous improvement was developed and applied. A thorough study was carried
out along with study of cases, surveys and interviews to professionals involved in this area. As a result, a methodology for diagnosis
and improvement was proposed and tested in selected projects. The results obtained show that the main problem of procurement is
related to schedule delays and lack of specified quality for the project. To prevent this situation it is often necessary to dedicate
important resources like money, personnel, time, etc. To monitor and control the process. A great potential for improvement was
detected if state of the art technologies such as, electronic mail, electronic data interchange (EDI), and analysis were applied to the
procurement process. These helped to eliminate the root causes for many types of problems that were detected.
Managing drought short term strategies in semi arid regions a case studyeSAT Journals
Abstract
Drought management needs multidisciplinary action. Interdisciplinary efforts among the experts in various fields of the droughts
prone areas are helpful to achieve tangible and permanent solution for this recurring problem. The Gulbarga district having the total
area around 16, 240 sq.km, and accounts 8.45 per cent of the Karnataka state area. The district has been situated with latitude 17º 19'
60" North and longitude of 76 º 49' 60" east. The district is situated entirely on the Deccan plateau positioned at a height of 300 to
750 m above MSL. Sub-tropical, semi-arid type is one among the drought prone districts of Karnataka State. The drought
management is very important for a district like Gulbarga. In this paper various short term strategies are discussed to mitigate the
drought condition in the district.
Keywords: Drought, South-West monsoon, Semi-Arid, Rainfall, Strategies etc.
Life cycle cost analysis of overlay for an urban road in bangaloreeSAT Journals
Abstract
Pavements are subjected to severe condition of stresses and weathering effects from the day they are constructed and opened to traffic
mainly due to its fatigue behavior and environmental effects. Therefore, pavement rehabilitation is one of the most important
components of entire road systems. This paper highlights the design of concrete pavement with added mono fibers like polypropylene,
steel and hybrid fibres for a widened portion of existing concrete pavement and various overlay alternatives for an existing
bituminous pavement in an urban road in Bangalore. Along with this, Life cycle cost analyses at these sections are done by Net
Present Value (NPV) method to identify the most feasible option. The results show that though the initial cost of construction of
concrete overlay is high, over a period of time it prove to be better than the bituminous overlay considering the whole life cycle cost.
The economic analysis also indicates that, out of the three fibre options, hybrid reinforced concrete would be economical without
compromising the performance of the pavement.
Keywords: - Fatigue, Life cycle cost analysis, Net Present Value method, Overlay, Rehabilitation
Laboratory studies of dense bituminous mixes ii with reclaimed asphalt materialseSAT Journals
Abstract
The issue of growing demand on our nation’s roadways over that past couple of decades, decreasing budgetary funds, and the need to
provide a safe, efficient, and cost effective roadway system has led to a dramatic increase in the need to rehabilitate our existing
pavements and the issue of building sustainable road infrastructure in India. With these emergency of the mentioned needs and this
are today’s burning issue and has become the purpose of the study.
In the present study, the samples of existing bituminous layer materials were collected from NH-48(Devahalli to Hassan) site.The
mixtures were designed by Marshall Method as per Asphalt institute (MS-II) at 20% and 30% Reclaimed Asphalt Pavement (RAP).
RAP material was blended with virgin aggregate such that all specimens tested for the, Dense Bituminous Macadam-II (DBM-II)
gradation as per Ministry of Roads, Transport, and Highways (MoRT&H) and cost analysis were carried out to know the economics.
Laboratory results and analysis showed the use of recycled materials showed significant variability in Marshall Stability, and the
variability increased with the increase in RAP content. The saving can be realized from utilization of recycled materials as per the
methodology, the reduction in the total cost is 19%, 30%, comparing with the virgin mixes.
Keywords: Reclaimed Asphalt Pavement, Marshall Stability, MS-II, Dense Bituminous Macadam-II
Laboratory investigation of expansive soil stabilized with natural inorganic ...eSAT Journals
Abstract
Soil stabilization has proven to be one of the oldest techniques to improve the soil properties. Literature review conducted revealed
that uses of natural inorganic stabilizers are found to be one of the best options for soil stabilization. In this regard an attempt has
been made to evaluate the influence of RBI-81 stabilizer on properties of black cotton soil through laboratory investigations. Black
cotton soil with varying percentages of RBI-81 viz., 0, 0.5, 1, 1.5, 2, and 2.5 percent were studied for moisture density relationships
and strength behaviour of soils. Also the effect of curing period was evaluated as literature review clearly emphasized the strength
gain of soils stabilized with RBI-81 over a period of time. The results obtained shows that the unconfined compressive strength of
specimens treated with RBI-81 increased approximately by 250% for a curing period of 28 days as compared to virgin soil. Further
the CBR value improved approximately by 400%. The studies indicated an increasing trend for soil strength behaviour with
increasing percentage of RBI-81 suggesting its potential applications in soil stabilization.
Influence of reinforcement on the behavior of hollow concrete block masonry p...eSAT Journals
Abstract
Reinforced masonry was developed to exploit the strength potential of masonry and to solve its lack of tensile strength. Experimental
and analytical studies have been carried out to investigate the effect of reinforcement on the behavior of hollow concrete block
masonry prisms under compression and to predict ultimate failure compressive strength. In the numerical program, three dimensional
non-linear finite elements (FE) model based on the micro-modeling approach is developed for both unreinforced and reinforced
masonry prisms using ANSYS (14.5). The proposed FE model uses multi-linear stress-strain relationships to model the non-linear
behavior of hollow concrete block, mortar, and grout. Willam-Warnke’s five parameter failure theory has been adopted to model the
failure of masonry materials. The comparison of the numerical and experimental results indicates that the FE models can successfully
capture the highly nonlinear behavior of the physical specimens and accurately predict their strength and failure mechanisms.
Keywords: Structural masonry, Hollow concrete block prism, grout, Compression failure, Finite element method,
Numerical modeling.
Influence of compaction energy on soil stabilized with chemical stabilizereSAT Journals
Abstract
Increase in traffic along with heavier magnitude of wheel loads cause rapid deterioration in pavements. There is a need to improve
density, strength of soil subgrade and other pavement layers. In this study an attempt is made to improve the properties of locally
available loamy soil using twin approaches viz., i) increasing the compaction of soil and ii) treating the soil with chemical stabilizer.
Laboratory studies are carried out on both untreated and treated soil samples compacted by different compaction efforts. Studies
show that increase in compaction effort results in increase in density of soil. However in soil treated with chemical stabilizer, rate of
increase in density is not significant. The soil treated with chemical stabilizer exhibits improvement in both strength and performance
properties.
Keywords: compaction, density, subgradestabilization, resilient modulus
Geographical information system (gis) for water resources managementeSAT Journals
Abstract
Water resources projects are inherited with overlapping and at times conflicting objectives. These projects are often of varied sizes
ranging from major projects with command areas of millions of hectares to very small projects implemented at the local level. Thus,
in all these projects there is seldom proper coordination which is essential for ensuring collective sustainability.
Integrated watershed development and management is the accepted answer but in turn requires a comprehensive framework that can
enable planning process involving all the stakeholders at different levels and scales is compulsory. Such a unified hydrological
framework is essential to evaluate the cause and effect of all the proposed actions within the drainage basins.
The present paper describes a hydrological framework developed in the form of a Hydrologic Information System (HIS) which is
intended to meet the specific information needs of the various line departments of a typical State connected with water related aspects.
The HIS consist of a hydrologic information database coupled with tools for collating primary and secondary data and tools for
analyzing and visualizing the data and information. The HIS also incorporates hydrological model base for indirect assessment of
various entities of water balance in space and time. The framework would be maintained and updated to reflect fully the most
accurate ground truth data and the infrastructure requirements for planning and management.
Keywords: Hydrological Information System (HIS); WebGIS; Data Model; Web Mapping Services
Forest type mapping of bidar forest division, karnataka using geoinformatics ...eSAT Journals
Abstract
The study demonstrate the potentiality of satellite remote sensing technique for the generation of baseline information on forest types
including tree plantation details in Bidar forest division, Karnataka covering an area of 5814.60Sq.Kms. The Total Area of Bidar
forest division is 5814Sq.Kms analysis of the satellite data in the study area reveals that about 84% of the total area is Covered by
crop land, 1.778% of the area is covered by dry deciduous forest, 1.38 % of mixed plantation, which is very threatening to the
environmental stability of the forest, future plantation site has been mapped. With the use of latest Geo-informatics technology proper
and exact condition of the trees can be observed and necessary precautions can be taken for future plantation works in an appropriate
manner
Keywords:-RS, GIS, GPS, Forest Type, Tree Plantation
Factors influencing compressive strength of geopolymer concreteeSAT Journals
Abstract
To study effects of several factors on the properties of fly ash based geopolymer concrete on the compressive strength and also the
cost comparison with the normal concrete. The test variables were molarities of sodium hydroxide(NaOH) 8M,14M and 16M, ratio of
NaOH to sodium silicate (Na2SiO3) 1, 1.5, 2 and 2.5, alkaline liquid to fly ash ratio 0.35 and 0.40 and replacement of water in
Na2SiO3 solution by 10%, 20% and 30% were used in the present study. The test results indicated that the highest compressive
strength 54 MPa was observed for 16M of NaOH, ratio of NaOH to Na2SiO3 2.5 and alkaline liquid to fly ash ratio of 0.35. Lowest
compressive strength of 27 MPa was observed for 8M of NaOH, ratio of NaOH to Na2SiO3 is 1 and alkaline liquid to fly ash ratio of
0.40. Alkaline liquid to fly ash ratio of 0.35, water replacement of 10% and 30% for 8 and 16 molarity of NaOH and has resulted in
compressive strength of 36 MPa and 20 MPa respectively. Superplasticiser dosage of 2 % by weight of fly ash has given higher
strength in all cases.
Keywords: compressive strength, alkaline liquid, fly ash
Experimental investigation on circular hollow steel columns in filled with li...eSAT Journals
Abstract
Composite Circular hollow Steel tubes with and without GFRP infill for three different grades of Light weight concrete are tested for
ultimate load capacity and axial shortening , under Cyclic loading. Steel tubes are compared for different lengths, cross sections and
thickness. Specimens were tested separately after adopting Taguchi’s L9 (Latin Squares) Orthogonal array in order to save the initial
experimental cost on number of specimens and experimental duration. Analysis was carried out using ANN (Artificial Neural
Network) technique with the assistance of Mini Tab- a statistical soft tool. Comparison for predicted, experimental & ANN output is
obtained from linear regression plots. From this research study, it can be concluded that *Cross sectional area of steel tube has most
significant effect on ultimate load carrying capacity, *as length of steel tube increased- load carrying capacity decreased & *ANN
modeling predicted acceptable results. Thus ANN tool can be utilized for predicting ultimate load carrying capacity for composite
columns.
Keywords: Light weight concrete, GFRP, Artificial Neural Network, Linear Regression, Back propagation, orthogonal
Array, Latin Squares
Experimental behavior of circular hsscfrc filled steel tubular columns under ...eSAT Journals
Abstract
This paper presents an outlook on experimental behavior and a comparison with predicted formula on the behaviour of circular
concentrically loaded self-consolidating fibre reinforced concrete filled steel tube columns (HSSCFRC). Forty-five specimens were
tested. The main parameters varied in the tests are: (1) percentage of fiber (2) tube diameter or width to wall thickness ratio (D/t
from 15 to 25) (3) L/d ratio from 2.97 to 7.04 the results from these predictions were compared with the experimental data. The
experimental results) were also validated in this study.
Keywords: Self-compacting concrete; Concrete-filled steel tube; axial load behavior; Ultimate capacity.
Evaluation of punching shear in flat slabseSAT Journals
Abstract
Flat-slab construction has been widely used in construction today because of many advantages that it offers. The basic philosophy in
the design of flat slab is to consider only gravity forces; this method ignores the effect of punching shear due to unbalanced moments
at the slab column junction which is critical. An attempt has been made to generate generalized design sheets which accounts both
punching shear due to gravity loads and unbalanced moments for cases (a) interior column; (b) edge column (bending perpendicular
to shorter edge); (c) edge column (bending parallel to shorter edge); (d) corner column. These design sheets are prepared as per
codal provisions of IS 456-2000. These design sheets will be helpful in calculating the shear reinforcement to be provided at the
critical section which is ignored in many design offices. Apart from its usefulness in evaluating punching shear and the necessary
shear reinforcement, the design sheets developed will enable the designer to fix the depth of flat slab during the initial phase of the
design.
Keywords: Flat slabs, punching shear, unbalanced moment.
Evaluation of performance of intake tower dam for recent earthquake in indiaeSAT Journals
Abstract
Intake towers are typically tall, hollow, reinforced concrete structures and form entrance to reservoir outlet works. A parametric
study on dynamic behavior of circular cylindrical towers can be carried out to study the effect of depth of submergence, wall thickness
and slenderness ratio, and also effect on tower considering dynamic analysis for time history function of different soil condition and
by Goyal and Chopra accounting interaction effects of added hydrodynamic mass of surrounding and inside water in intake tower of
dam
Key words: Hydrodynamic mass, Depth of submergence, Reservoir, Time history analysis,
Evaluation of operational efficiency of urban road network using travel time ...eSAT Journals
Abstract
Efficiency of the road network system is analyzed by travel time reliability measures. The study overlooks on an important measure of
travel time reliability and prioritizing Tiruchirappalli road network. Traffic volume and travel time were collected using license plate
matching method. Travel time measures were estimated from average travel time and 95th travel time. Effect of non-motorized vehicle
on efficiency of road system was evaluated. Relation between buffer time index and traffic volume was created. Travel time model has
been developed and travel time measure was validated. Then service quality of road sections in network were graded based on
travel time reliability measures.
Keywords: Buffer Time Index (BTI); Average Travel Time (ATT); Travel Time Reliability (TTR); Buffer Time (BT).
Estimation of surface runoff in nallur amanikere watershed using scs cn methodeSAT Journals
Abstract
The development of watershed aims at productive utilization of all the available natural resources in the entire area extending from
ridge line to stream outlet. The per capita availability of land for cultivation has been decreasing over the years. Therefore, water and
the related land resources must be developed, utilized and managed in an integrated and comprehensive manner. Remote sensing and
GIS techniques are being increasingly used for planning, management and development of natural resources. The study area, Nallur
Amanikere watershed geographically lies between 110 38’ and 110 52’ N latitude and 760 30’ and 760 50’ E longitude with an area of
415.68 Sq. km. The thematic layers such as land use/land cover and soil maps were derived from remotely sensed data and overlayed
through ArcGIS software to assign the curve number on polygon wise. The daily rainfall data of six rain gauge stations in and around
the watershed (2001-2011) was used to estimate the daily runoff from the watershed using Soil Conservation Service - Curve Number
(SCS-CN) method. The runoff estimated from the SCS-CN model was then used to know the variation of runoff potential with different
land use/land cover and with different soil conditions.
Keywords: Watershed, Nallur watershed, Surface runoff, Rainfall-Runoff, SCS-CN, Remote Sensing, GIS.
Estimation of morphometric parameters and runoff using rs & gis techniqueseSAT Journals
Abstract
Land and water are the two vital natural resources, the optimal management of these resources with minimum adverse environmental
impact are essential not only for sustainable development but also for human survival. Satellite remote sensing with geographic
information system has a pragmatic approach to map and generate spatial input layers of predicting response behavior and yield of
watershed. Hence, in the present study an attempt has been made to understand the hydrological process of the catchment at the
watershed level by drawing the inferences from moprhometric analysis and runoff. The study area chosen for the present study is
Yagachi catchment situated in Chickamaglur and Hassan district lies geographically at a longitude 75⁰52’08.77”E and
13⁰10’50.77”N latitude. It covers an area of 559.493 Sq.km. Morphometric analysis is carried out to estimate morphometric
parameters at Micro-watershed to understand the hydrological response of the catchment at the Micro-watershed level. Daily runoff
is estimated using USDA SCS curve number model for a period of 10 years from 2001 to 2010. The rainfall runoff relationship of the
study shows there is a positive correlation.
Keywords: morphometric analysis, runoff, remote sensing and GIS, SCS - method
-
Effect of variation of plastic hinge length on the results of non linear anal...eSAT Journals
Abstract The nonlinear Static procedure also well known as pushover analysis is method where in monotonically increasing loads are applied to the structure till the structure is unable to resist any further load. It is a popular tool for seismic performance evaluation of existing and new structures. In literature lot of research has been carried out on conventional pushover analysis and after knowing deficiency efforts have been made to improve it. But actual test results to verify the analytically obtained pushover results are rarely available. It has been found that some amount of variation is always expected to exist in seismic demand prediction of pushover analysis. Initial study is carried out by considering user defined hinge properties and default hinge length. Attempt is being made to assess the variation of pushover analysis results by considering user defined hinge properties and various hinge length formulations available in literature and results compared with experimentally obtained results based on test carried out on a G+2 storied RCC framed structure. For the present study two geometric models viz bare frame and rigid frame model is considered and it is found that the results of pushover analysis are very sensitive to geometric model and hinge length adopted. Keywords: Pushover analysis, Base shear, Displacement, hinge length, moment curvature analysis
Effect of use of recycled materials on indirect tensile strength of asphalt c...eSAT Journals
Abstract
Depletion of natural resources and aggregate quarries for the road construction is a serious problem to procure materials. Hence
recycling or reuse of material is beneficial. On emphasizing development in sustainable construction in the present era, recycling of
asphalt pavements is one of the effective and proven rehabilitation processes. For the laboratory investigations reclaimed asphalt
pavement (RAP) from NH-4 and crumb rubber modified binder (CRMB-55) was used. Foundry waste was used as a replacement to
conventional filler. Laboratory tests were conducted on asphalt concrete mixes with 30, 40, 50, and 60 percent replacement with RAP.
These test results were compared with conventional mixes and asphalt concrete mixes with complete binder extracted RAP
aggregates. Mix design was carried out by Marshall Method. The Marshall Tests indicated highest stability values for asphalt
concrete (AC) mixes with 60% RAP. The optimum binder content (OBC) decreased with increased in RAP in AC mixes. The Indirect
Tensile Strength (ITS) for AC mixes with RAP also was found to be higher when compared to conventional AC mixes at 300C.
Keywords: Reclaimed asphalt pavement, Foundry waste, Recycling, Marshall Stability, Indirect tensile strength.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
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An improved double coding local binary pattern algorithm for face recognition
1. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 04 Issue: 08 | August-2015, Available @ http://www.ijret.org 343
AN IMPROVED DOUBLE CODING LOCAL BINARY PATTERN
ALGORITHM FOR FACE RECOGNITION
Saurabh Asija1
, Rakesh Singh2
1
Research Scholar (Computer Engineering Department), Punjabi University, Patiala.
2
Asst. Professor (Computer Engineering Department), Punjabi University, Patiala.
Abstract
A human face conveys a lot of information about the identity and emotional state of the person. So now a day’s face recognition
has become an interesting and challenging problem. Face recognition plays a vital role in many applications such as
authenticating a person, system security, verification and identification for law enforcement and personal identification among
others. So our research work mainly consists of three parts, namely face representation, feature extraction and classification. The
first part, Face representation represents how to model a face and check which algorithms can be used for detection and
recognition purpose. In the second phase i.e. feature extraction phase we compute the unique features of the face image. In the
classification phase the computed DLBP face image is compared with the images from the database. In our research work, we use
Double Coding Local Binary Patterns to evaluate face recognition which concentrate over both the shape and texture
information to represent face images for person independent face recognition. The face area is firstly cut into small regions from
which Local Binary Patterns (LBP), then we compute histograms to generate LBP image then we compute single oriented mean
image from which we again compute histogram values small regions and at last concatenated into a single feature vectors and
generate D-LBP image. This feature are used for the representation of the face and to measure similarities between images.
Keywords: local binary pattern (LBP), double coding local binary pattern (D-LBP), features extraction,
classification, pattern recognition, histogram, feature vector.
--------------------------------------------------------------------***----------------------------------------------------------------------
1. INTRODUCTION
Image processing can be defined as any form of signal
processing which takes an image as a input, which can be a
video frame or a photograph for which the output can be an
image itself or a set of its characteristics or parameters
closely related to the image. Face Recognition is a biometric
trait used widely now a days and it make use of computer
software to identify of the individual. In face recognition the
automatic recognition of a person take place using
distinguishing traits. In social life our main focus is on face,
which plays a vital role in conveying identity and emotions.
We recognize many of faces seen in our life and identify
familiar faces at a short stamp even after many years of gap.
Because of quite robustness, despite large changes in the
visual stimulus due to age, viewing effects, beards, and
distractions such as glasses, expression or changes in hair
style. Face recognition plays a vital role in many
applications such as authenticating a person, system
security, verification and identification for law enforcement
and personal identification among others aspects [7].
Local binary pattern (LBP) is used in computer vision for
the purpose classification. In Texture Spectrum model LBP
was proposed in 1990. LBP was first described in 1994.
(LBP) is a texture operator simple yet efficient in use. it
extracts pixels of an image and label them by calculating
the threshold of the neighbors of each pixel and then
consider the result in binary form. Due to its computational
simplicity and discriminative power, it proves to be a better
approach in various applications.
Double coding Local binary patterns is an invariant of LBP.
In LBP we compute only amplitude threshold θ while in d-
LBP we compute distance threshold too ƹ.
θ= 1/𝑝 |ik − ic|
𝑝−1
𝑘=0 ,p =4
ƹ= 1/𝑝 ik − ic
𝑝−1
𝑘=0 ,p =4
2. FACE RECOGNITION PROCESS
Although there are number of professional and commercial
face recognition systems yet this way of identification
continues to be an interesting topic for researchers. This is
due to the fact that the under the feasible conditions the
current systems perform well but when variations occurs its
performance decreases. The variation factors are viewpoint,
facial expressions, time (when the pictures are made), pose
and illumination (lightening changes)[8]. The goal in this
research area is to minimize the influence of these factors
and create robust face recognition system. A model for face
recognition is shown in Figure-1.1.
2. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 04 Issue: 08 | August-2015, Available @ http://www.ijret.org 344
Figure 1.1: Principle of an identification process with Face
Recognition
The process of person identification by using face
recognition can be split into three main phases (figure 1.1).
These are face representation, feature extraction and
classification [3].The first part, Face representation
represents how to model a face and check which algorithms
can be used for detection and recognition purpose. In the
second phase i.e. feature extraction phase we compute the
unique features of the face image. In the classification phase
the computed DLBP face image is compared with the
images from the database [3]. The output of the
classification part is the identity of a face image from the
database with the highest matching score, thus with the
smallest differences compared to the input face image. Also
a threshold value can be used to determine if the differences
are small enough. After all, it could be that a certain face is
not in the database at all.
3. LOCAL BINARY PATTERN
For face recognition there exist several methods to extract
the most useful features from the face images .Now a day’s
Local Binary Pattern (LBP) method is mostly used method
for feature extraction. This relative new approach was
introduced in 1996 by Ojala et al. [3]. With the help of LBP
it become easy to describe the shape and texture of a face
image. In this method for the purpose of feature extraction
an image is divided into several small regions. From each
individual region the features are extracted.
Figure 1.2: A preprocessed image divided into 64 regions
These features are coded into binary patterns to describe the
surroundings of pixels in the regions. Each and every region
is processed to compute the features and these computed
features from all regions are combined into a single feature
histogram, which forms a representation of the image.
Images are compared by measuring the similarity between
their histograms. According to studies face recognition
using the LBP method had provided very good results, both
in terms of speed and discrimination performance. Due to its
describing power of image texture and shape it seems to be
quite useful in recognizing face images with different
lightening conditions, image rotation, different facial
expressions and aging of persons.
4. DOUBLECODINGLOCAL BINARY PATTERN
Basic LBP operator works on center pixel and neighborhood
pixels. It computes the differences between the gray values
between center pixel and neighborhood pixels, but the
amplitude relationship between center pixel gray value and
the neighborhood pixel gray values are ignored. Because
every pixel gray value cannot be made full use of, it could
lead to a drop in the final recognition rate when the face
texture features are extracted. Moreover, too much sampling
points will make the algorithm more complicated, which
may result in the decrease of the rate of recognition. Due to
the this problems, this we presents a double coding local
binary pattern (d-LBP).Firstly, to reduce the complexity of
the algorithm, the sampling points of d-LBP operator is
reduced to 4 from 8 of the basic LBP operator
Figure 1.3: Reducing Sample Size
Then, making full use of the relationship among the gray
values of each pixel within a certain local area. So θ is
defined as the amplitude threshold and ƹ is defined as the
difference threshold [7].
θ= 1/𝑝 |ik − ic|
𝑝−1
𝑘=0 ,p=4
ƹ= 1/𝑝 ik − ic
𝑝−1
𝑘=0 ,p=4
Where: 𝑖 𝑐 is the gray value of the center pixel in the local
area; 𝑖 𝑘 shows the k-th grey value of sampling point in the
center pixel neighborhood area; p is the number of sampling
points. Finally, in order to describe the facial texture
information in detail, the basic LBP operator with one
binary coding is replaced by the improved LBP operator
with two binary encoding. The first binary code is related
with the difference in gray value of the neighborhood pixels
and the center pixel. Compared with difference threshold
(ƹ) if it is larger, the binary code is 1, on the other hand, is
marked as 0. The second binary code is related with
amplitude between neighborhood pixels gray values and the
center pixel gray value. Compared with amplitude threshold
(θ), if it is larger, the binary code is marked as 1, otherwise
0. As shown in formula [7].
Face
representation
Feature
Extraction
Image
Database
ClassificationIdentity of Face
3. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 04 Issue: 08 | August-2015, Available @ http://www.ijret.org 345
dLBP= 22𝑘𝑝−1
𝑘 𝑠(𝑖 𝑘 , 𝑖 𝑐)
𝑠 𝑖 𝑘, 𝑖 𝑐 =
00, 𝑖 𝑘 − 𝑖 𝑐 < ƹ ≡ 𝑖 𝑘 − 𝑖 𝑐 < 𝜃
01, 𝑖 𝑘 − 𝑖 𝑐 < ƹ ≡ 𝑖 𝑘 − 𝑖 𝑐 > 𝜃
10, 𝑖 𝑘 − 𝑖 𝑐 > ƹ ≡ 𝑖 𝑘 − 𝑖 𝑐 < 𝜃
11, 𝑖 𝑘 − 𝑖 𝑐 > ƹ ≡ 𝑖 𝑘 − 𝑖 𝑐 > 𝜃
p=4
Figure 1.4: Double Binary Coding
θ=1/4(0+37+2+18) = 22;
ƹ=1/4(8+75+40+20) -38 = -2;
d-LBP = 01111000 = 120
In order to increase the accuracy of the d-LBP operator and
make better use of d-LBP to describe face image. First of
all, the d-LBP map of face image should be blocked; then,
the images of each block are calculated to get the histogram
of d-LBP respectively; finally, each partitioned histograms
is connected according to a certain order to get a composite
feature vector, that is, the d-LBP histogram of overall face
image.
Figure 1.5: D-LBP Process
Nonparametric statistical method is used to determine the
histogram similarity between samples after getting a d-LBP
histogram.
φ2
(𝐻1, 𝐻2) =
H1 i − H2(i) 2
H1 i + H2(i)
𝑖
Where: is the training sample;𝐻1(i)is the sample to be
classified; Similarity 𝐻2(i) is measured by the distance of
𝜑2
, the smaller the d istance, the more similar the two faces.
4.1 Implementation
The process of Face recognition is not a simple problem
because when input image is an unknown then face image
seen in the extraction phase is usually different from the
face image seen in the classification phase. Although local
binary features has been extracted from the face image and
DLBP image is computed for face recognition that there are
several face image uses in the database that compared with
the input face image. The face image depends on viewing
lighting and environmental conditions. In addition the face
image changes according to the expressions. In the research
work, which is flexible and efficient, should be solved the
problems.
4.2 D-Lbp Face Recognition Algorithm
To implement the face recognition in this research work, we
proposed the Double Coding Local Binary patterns
methodology. D-LBP works on local features that uses D-
LBP operator which summarizes the local special structure
of a face image.
In order to describe the facial texture information in detail,
the basic LBP operator with one binary coding is replaced
by the improved LBP operator with two binary encoding.
The first binary code is related with the difference in gray
value of the neighborhood pixels and the center pixel.
Compared with difference threshold (ƹ), if it is larger, the
binary code is 1, on the other hand, is marked as 0. The
second binary code is related with amplitude between
neighborhood pixels gray values and the center pixel gray
value. Compared with amplitude threshold (θ), if it is larger,
the binary code is marked as 1, otherwise 0.
dLBP = 22𝑘𝑝−1
𝑘 𝑠(𝑖 𝑘 , 𝑖 𝑐)
𝑠 𝑖 𝑘, 𝑖 𝑐 =
00, 𝑖 𝑘 − 𝑖 𝑐 < ƹ ≡ 𝑖 𝑘 − 𝑖 𝑐 < 𝜃
01, 𝑖 𝑘 − 𝑖 𝑐 < ƹ ≡ 𝑖 𝑘 − 𝑖 𝑐 > 𝜃
10, 𝑖 𝑘 − 𝑖 𝑐 > ƹ ≡ 𝑖 𝑘 − 𝑖 𝑐 < 𝜃
11, 𝑖 𝑘 − 𝑖 𝑐 > ƹ ≡ 𝑖 𝑘 − 𝑖 𝑐 > 𝜃
p=4
The Face Recognition Algorithm
Input: Training Image set.
Output: Feature extracted from face image and compared
with centre pixel and recognition with unknown face image.
1) Initialize temp1 = 0
2) FOR each image 𝐼 𝑚 in the training set)
3) Initialize the LBP histogram, H = 0
4) FOR each center pixel 𝑖 𝑐 in 𝐼 𝑚
5) Compute the gray value of 𝑖 𝑐, LBP(1)
6) Increase the corresponding bit by 1.
7) END FOR LOOP
8) Compute the LBP feature for each facial image and
combined into single vector.
9) Compare it with test image in database.
10) If it matches it’s most similar face in database then
successfully recognized
4. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 04 Issue: 08 | August-2015, Available @ http://www.ijret.org 346
Figure 1.6: Flowchart of the LBP Process
Figure 1.7: Flowchart of the Proposed System
5. RESULTS AND DISCUSSION
This performance of the DLBP-method is tested by
implementation on different kind of face images. The image
is divided into of the 64 regions several parameters like LBP
image, DLBP operator, non-weighted or weighted regions
are computed and judge the influence of these parameters on
the performance. For this research purpose we have
collected lots of face images.
We compare the face image of an unknown identity with
face images of known individuals from a large database. In
the figure 1.8 we can see the input facial images used for
input for face recognition are given below:
Figure 1.8: Input images
And also in the figure 1.9 we can see the facial images that
are stored in the database which compared with the input
facial images. If the input face images are found or the more
similarities face images are matched in the database then we
say the face image is successfully recognized.
Figure 1.9: Database images
Based on the algorithm the input face images are compared
with database facial images for identification. The face
recognition results are shown in below in window mode:
6. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 04 Issue: 08 | August-2015, Available @ http://www.ijret.org 348
BIOGRAPHIES
Saurabh Asija He received the B.Tech
Degree in computer science from BGIET,
Sangrur, in 2011.He is currently working
toward the M.Tech degree in the
Department of Computer Engineering,
Punjabi University, Patiala. His research
interests are image processing and
computer vision.
Rakesh Singh, He is Asst. Professor,
Computer Engineering Department,
Punjabi University, Patiala. His area of
interest is image processing and computer
vision.