Restoration techniques of degraded image is still a challenging task, in spite of the sophistication of the recently proposed methods. All show an outstanding performance when the image model corresponds to the algorithm assumptions but fail to retain the edges and fine structure. In this paper, a novel approach for image restoration has been developed. To show the analysis of performance of this noel restoration procedure GUI has been developed. It shows the Restoration of Degraded image on various noises by different Filters. In implementation of the approach first, image is degraded by adding different types of noises in sample images and then convolving images with different kinds of filters (Mean Filters, Min and Max Filters). Proposed image restoration method's analysis on performances of denoising techniques Graphical User Interface has been developed as a part of this research. In this paper true colour sample images are degraded with different noise and then is restored back. The performance analyis of the present approach with state of art techniques are in terms of mean square error, peak signal-to-noise ratio, and normalized absolute error is also provided. In comparisons with other state of art methods, present approach yields better to optimization, and shows to be applicable to a much wider range of noises.
{"title":"Novel Restoration Process for Degraded Image","authors":"Tripty Singh","doi":"10.1109/ICSIP.2014.18","DOIUrl":"https://doi.org/10.1109/ICSIP.2014.18","url":null,"abstract":"Restoration techniques of degraded image is still a challenging task, in spite of the sophistication of the recently proposed methods. All show an outstanding performance when the image model corresponds to the algorithm assumptions but fail to retain the edges and fine structure. In this paper, a novel approach for image restoration has been developed. To show the analysis of performance of this noel restoration procedure GUI has been developed. It shows the Restoration of Degraded image on various noises by different Filters. In implementation of the approach first, image is degraded by adding different types of noises in sample images and then convolving images with different kinds of filters (Mean Filters, Min and Max Filters). Proposed image restoration method's analysis on performances of denoising techniques Graphical User Interface has been developed as a part of this research. In this paper true colour sample images are degraded with different noise and then is restored back. The performance analyis of the present approach with state of art techniques are in terms of mean square error, peak signal-to-noise ratio, and normalized absolute error is also provided. In comparisons with other state of art methods, present approach yields better to optimization, and shows to be applicable to a much wider range of noises.","PeriodicalId":111591,"journal":{"name":"2014 Fifth International Conference on Signal and Image Processing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128152908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A Braille document image is a collection of dots. The position of the dot and relative-ness of the dot with other dots gives different Braille characters. It is challenging, to separate the character lines, words and characters from a Braille document. This paper presents an Optical Braille character recognition system for both machine punched and hand punched Kannada Braille text documents [21]. Standard spacing between the characters and lines are used to segregate the dots. Dot mesh is created and character box is identified. Once character box is identified an efficient look up method is designed to identify the equivalent normal Kannada character. A unique value for the Braille character is generated and the Braille character is matched to the corresponding normal Kannada character in one shot. A Braille character is made of 6 dots combination and hence only 26=64 different combinations are possible. Recognized character is classified into one of the 64 possible classes. Identifying the dot position inside a character box is done using the dot mesh and by computing the centre position of all the objects inside the character box.
{"title":"A Novel and Efficient Algorithm to Recognize Any Universally Accepted Braille Characters: A Case with Kannada Language","authors":"C. N. R. Kumar, S. Srinath","doi":"10.1109/ICSIP.2014.52","DOIUrl":"https://doi.org/10.1109/ICSIP.2014.52","url":null,"abstract":"A Braille document image is a collection of dots. The position of the dot and relative-ness of the dot with other dots gives different Braille characters. It is challenging, to separate the character lines, words and characters from a Braille document. This paper presents an Optical Braille character recognition system for both machine punched and hand punched Kannada Braille text documents [21]. Standard spacing between the characters and lines are used to segregate the dots. Dot mesh is created and character box is identified. Once character box is identified an efficient look up method is designed to identify the equivalent normal Kannada character. A unique value for the Braille character is generated and the Braille character is matched to the corresponding normal Kannada character in one shot. A Braille character is made of 6 dots combination and hence only 26=64 different combinations are possible. Recognized character is classified into one of the 64 possible classes. Identifying the dot position inside a character box is done using the dot mesh and by computing the centre position of all the objects inside the character box.","PeriodicalId":111591,"journal":{"name":"2014 Fifth International Conference on Signal and Image Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128960928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In computer vision extracting an object from an image automatically is too hard. Towards addressing this issue a comprehensive analysis of most of the Object detection through different Segmentations is performed taken from the major recent publications covering various aspects of the research in this area. We identify the following methods of the state-of-the-art techniques in which an object can be detected: (1) Mean Shift Segmentation With Region Merging, (2) Boundary Structure Segmentation With Region Grouping, (3) Watershed Segmentation With Region Merging. All these are semi automatic detection of an object through segmentation and contour based shape descriptor. The results tabulated prove that the Mean Shift Segmentation with Region Merging Process yields the best result over the other two methods in detection the Object Of Interest.
{"title":"Comprehensive Analysis of Object Detection through Segmentation","authors":"P. Nikkam, N. Hegde, Eswar Reddy","doi":"10.1109/ICSIP.2014.32","DOIUrl":"https://doi.org/10.1109/ICSIP.2014.32","url":null,"abstract":"In computer vision extracting an object from an image automatically is too hard. Towards addressing this issue a comprehensive analysis of most of the Object detection through different Segmentations is performed taken from the major recent publications covering various aspects of the research in this area. We identify the following methods of the state-of-the-art techniques in which an object can be detected: (1) Mean Shift Segmentation With Region Merging, (2) Boundary Structure Segmentation With Region Grouping, (3) Watershed Segmentation With Region Merging. All these are semi automatic detection of an object through segmentation and contour based shape descriptor. The results tabulated prove that the Mean Shift Segmentation with Region Merging Process yields the best result over the other two methods in detection the Object Of Interest.","PeriodicalId":111591,"journal":{"name":"2014 Fifth International Conference on Signal and Image Processing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132082585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper aims at developing a support vector machine for identity verification of offline signature based on the feature values in the database. A set of signature samples are collected from individuals and these signature samples are scanned in a gray scale scanner. These scanned signature images are then subjected to a number of image enhancement operations like binarization, complementation, filtering, thinning and edge detection. From these pre-processed signatures, features such as centroid, centre of gravity, calculation of number of loops, horizontal and vertical profile and normalized area are extracted and stored in a database separately. The values from the database are fed to the support vector machine which draws a hyper plane and classifies the signature into original or forged based on a particular feature value. The developed SVM is successfully tested against 336 signature samples and the classification error rate is less than 7.16% and this is found to be convincing.
{"title":"Offline Signature Verification Using Support Vector Machine","authors":"Kruthi C, Deepika C Shet","doi":"10.1109/ICSIP.2014.5","DOIUrl":"https://doi.org/10.1109/ICSIP.2014.5","url":null,"abstract":"This paper aims at developing a support vector machine for identity verification of offline signature based on the feature values in the database. A set of signature samples are collected from individuals and these signature samples are scanned in a gray scale scanner. These scanned signature images are then subjected to a number of image enhancement operations like binarization, complementation, filtering, thinning and edge detection. From these pre-processed signatures, features such as centroid, centre of gravity, calculation of number of loops, horizontal and vertical profile and normalized area are extracted and stored in a database separately. The values from the database are fed to the support vector machine which draws a hyper plane and classifies the signature into original or forged based on a particular feature value. The developed SVM is successfully tested against 336 signature samples and the classification error rate is less than 7.16% and this is found to be convincing.","PeriodicalId":111591,"journal":{"name":"2014 Fifth International Conference on Signal and Image Processing","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132144363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Modern mobile devices incorporate rich collection of sensing and communication capabilities allowing the design of diverse range of interactive context aware applications. Intensive use of these resources comes at a cost, typically in the form of reduced battery life. Therefore, limited battery power is one of the major drawbacks of mobile communication and hence managing battery life is an important research issue. Managing battery life has two viewpoints: (i) harvesting and (ii) managing. Harvesting energy from the surrounding environment is very interesting and a promising research direction, but this scavenging provides very limited amount of energy. Managing the available energy in mobile devices efficiently to extend the battery life and also to maximize the usage of the enhanced features of the modern mobiles is another research direction. In this paper, we give an overview of the ambient energy harvesting and energy consumed by mobiles.
{"title":"A Tour into Ambient Energy Resources and Battery Optimization","authors":"Ruchi Sharma, Shalini Prasad, S. Balaji","doi":"10.1109/ICSIP.2014.60","DOIUrl":"https://doi.org/10.1109/ICSIP.2014.60","url":null,"abstract":"Modern mobile devices incorporate rich collection of sensing and communication capabilities allowing the design of diverse range of interactive context aware applications. Intensive use of these resources comes at a cost, typically in the form of reduced battery life. Therefore, limited battery power is one of the major drawbacks of mobile communication and hence managing battery life is an important research issue. Managing battery life has two viewpoints: (i) harvesting and (ii) managing. Harvesting energy from the surrounding environment is very interesting and a promising research direction, but this scavenging provides very limited amount of energy. Managing the available energy in mobile devices efficiently to extend the battery life and also to maximize the usage of the enhanced features of the modern mobiles is another research direction. In this paper, we give an overview of the ambient energy harvesting and energy consumed by mobiles.","PeriodicalId":111591,"journal":{"name":"2014 Fifth International Conference on Signal and Image Processing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130604099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Kavitha, M. Sudhamani, S. Omar, G. Ismail, A. S. Ghanem
Content based image retrieval (CBIR) is a promising way to address image retrieval based on the visual features of an image like color, texture and shape. Every visual feature will address a specific property of the image, so the state of the art focuses on combination of multiple visual features for content based image retrieval. In this paper we have devised a content based image retrieval system based on the combination of local and global features. The local features used are Bidirectional Empirical Mode Decomposition (BEMD) technique for edge detection and Harris corner detector to detect the corner points of an image. The global feature used is HSV colorfeature. For the experimental purpose the COIL-100 database has been used. The result show significant improvement in the retrieval accuracy when compared to the existing systems.
{"title":"Object Based Image Retrieval from Database Using Combined Features","authors":"H. Kavitha, M. Sudhamani, S. Omar, G. Ismail, A. S. Ghanem","doi":"10.1109/ICSIP.2014.31","DOIUrl":"https://doi.org/10.1109/ICSIP.2014.31","url":null,"abstract":"Content based image retrieval (CBIR) is a promising way to address image retrieval based on the visual features of an image like color, texture and shape. Every visual feature will address a specific property of the image, so the state of the art focuses on combination of multiple visual features for content based image retrieval. In this paper we have devised a content based image retrieval system based on the combination of local and global features. The local features used are Bidirectional Empirical Mode Decomposition (BEMD) technique for edge detection and Harris corner detector to detect the corner points of an image. The global feature used is HSV colorfeature. For the experimental purpose the COIL-100 database has been used. The result show significant improvement in the retrieval accuracy when compared to the existing systems.","PeriodicalId":111591,"journal":{"name":"2014 Fifth International Conference on Signal and Image Processing","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133708587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：