ICTACT Journal on Image and Video Processing最新文献_第7页

MACHINE LEARNING OF HANDWRITTEN NANDINAGARI CHARACTERS USING VLAD VECTORS 使用vlad向量的手写nandinagari字符机器学习

ICTACT Journal on Image and Video Processing Pub Date : 2017-11-01 DOI: 10.21917/IJIVP.2017.0229

P. Guruprasad, J. Majumdar

{"title":"MACHINE LEARNING OF HANDWRITTEN NANDINAGARI CHARACTERS USING VLAD VECTORS","authors":"P. Guruprasad, J. Majumdar","doi":"10.21917/IJIVP.2017.0229","DOIUrl":"https://doi.org/10.21917/IJIVP.2017.0229","url":null,"abstract":"This paper provides an early attempt to train and retrieve handwritten Nandinagari characters using one of the latest techniques in visual feature detection. The data set consists of over 1600 handwritten Nandinagari characters of different fonts, size, rotation, translation and image formats. In the Learning phase, we subject them to an approach where their recognition is effective by first extracting their key interest points on the images which are invariant to Scale, rotation, translation, illumination and occlusion. The technique used for this phase is Scale Invariant Feature Transform (SIFT). These features are represented in quantized form as visual words in code book generation step. Then the Vector of Locally Aggregated Descriptors (VLAD) is used for encoding each of the Image descriptors in the database. In the recognition phase, for query image, SIFT features are extracted and represented as query vector .Then these features are compared against the visual vocabulary generated by code book to retrieve similar images from the database. The performance is analysed by computing mean average precision .This is a novel scalable approach for recognition of rare handwritten Nandinagari characters with about 98% search accuracy with a good efficiency and relatively low memory usage requirements.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":"8 1","pages":"1633-1638"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43199860","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}

引用次数: 1

DETECTION OF HUMAN FACES IN COLOR IMAGES AND PERFORMANCE ANALYSIS OF DIFFERENT SKIN CHROMINANCE SPACES AND SKIN CHROMINANCE MODELS 彩色图像中人脸的检测及不同肤色空间和肤色模型的性能分析

ICTACT Journal on Image and Video Processing Pub Date : 2017-11-01 DOI: 10.21917/IJIVP.2017.0232

Poorvi Bhatt, Usa Global Solutions

{"title":"DETECTION OF HUMAN FACES IN COLOR IMAGES AND PERFORMANCE ANALYSIS OF DIFFERENT SKIN CHROMINANCE SPACES AND SKIN CHROMINANCE MODELS","authors":"Poorvi Bhatt, Usa Global Solutions","doi":"10.21917/IJIVP.2017.0232","DOIUrl":"https://doi.org/10.21917/IJIVP.2017.0232","url":null,"abstract":"The overall objective of the project is to build a system that detects human faces in a given color image and to compare performance of different chrominance models and chrominance spaces. For face detection, approach is to detect skin color and segment given image into skin and non-skin regions. In a skin segmented image, the region of skin whose height to width ratio falls under well-known Golden ratio = (1 +√5)) / 2  some tolerance, the probability of that region to be considered as face is very high. In this implementation, evaluation of this assumption has been performed. To be able to find out what skin looks like, system has to generate statistical model of skin color. A training set of 54 skin samples (37,020 pixels) and 28 background samples (23,229 pixels) used to generate such models. This paper discusses two types of statistical models Single Gaussian Model and Gaussian Mixture Model. Performance of both these model has been compared and the best fit model for given dataset has been used. Training dataset was collected using University of Stirling’s face database and several images from internet is used. Since dataset comes from different sources, it might result in some unknowns in dataset. One way to eliminate such unknowns is to separate color information from intensity or try to reduce effect of illumination by normalizing color information. To reduce effect of unknowns, this paper uses normalized-rgb (Red, Green, Blue) (illumination is normalized across three color, so effect is reduced), HSV (Hue, Saturation, Value) space (intensity and chromaticity part are independent) and CIE-xyz (Commission Internationale de l'Elcairage) (Machine independent) color spaces. Chrominance models in all three color spaces have been generated and compared to find which color space best suits the selected dataset.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":"8 1","pages":"1651-1659"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48166107","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}

引用次数: 1

AUTHENTICATION, TAMPER LOCALIZATION AND RECTIFICATION ALGORITHM WITH PRIVACY PRESERVATION OF IMAGE FOR THE CLOUD USING HMAC 基于hmac的云图像隐私保护认证、篡改定位和纠偏算法

ICTACT Journal on Image and Video Processing Pub Date : 2017-11-01 DOI: 10.21917/ijivp.2017.0233

Sayyada Fahmeeda Sultana, D. Shubhangi

引用次数: 0

FRACTAL COMPRESSION TECHNIQUE FOR COLOR IMAGES USING VARIABLE BLOCK 基于可变块的彩色图像分形压缩技术

ICTACT Journal on Image and Video Processing Pub Date : 2017-11-01 DOI: 10.21917/IJIVP.2017.0230

Nisar Ahmed, S. A. Sattar

引用次数: 0

BRAIN MR IMAGE SEGMENTATION BY MODIFIED ACTIVE CONTOURS AND CONTOURLET TRANSFORM 基于改进活动轮廓和contourlet变换的脑磁共振图像分割

ICTACT Journal on Image and Video Processing Pub Date : 2017-11-01 DOI: 10.21917/IJIVP.2017.0231

P. Reddy, C. Rao, C. Satyanarayana

{"title":"BRAIN MR IMAGE SEGMENTATION BY MODIFIED ACTIVE CONTOURS AND CONTOURLET TRANSFORM","authors":"P. Reddy, C. Rao, C. Satyanarayana","doi":"10.21917/IJIVP.2017.0231","DOIUrl":"https://doi.org/10.21917/IJIVP.2017.0231","url":null,"abstract":"Multiresolution analysis is often used for image representation and processing because it can represent image at the split resolution and scale space. In this paper, a novel medical image segmentation algorithm is proposed that combines contourlet transform and modified active contour model. This method has a new energy formulation by representing the image with the coefficients of a contourlet transform. This results fast and accurate convergence of the contour towards the object boundary. Medical image segmentation using contourlet transforms has shown significant improvement towards the weak and blurred edges of the Magnetic Resonance Image (MRI). Also, the computational complexity is less compared to using traditional level sets and variational level sets for medical image segmentation. The special property of the contourlet transform is that, the directional information is preserved in each sub-band and is captured by computing its energy. This energy is capable of enhancing weak and complex boundaries in details. Performance of medical image segmentation algorithm using contourlet transform is compared with other deformable models in terms of various performance measures.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":"8 1","pages":"1645-1650"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48884006","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}

引用次数: 9

AN EFFICIENT APPROACH FOR CONTENT BASED IMAGE RETRIEVAL USING HIERARCHICAL PART-TEMPLATE AND TREE MODELING 一种基于内容的高效图像检索方法

ICTACT Journal on Image and Video Processing Pub Date : 2017-11-01 DOI: 10.21917/IJIVP.2017.0226

P. Nikkam, B. E. Reddy

引用次数: 3

CORRELATION COEFFICIENT BASED DETECTION ALGORITHM FOR REMOVAL OF RANDOM VALUED IMPULSE NOISE IN IMAGES 基于相关系数的检测算法去除图像中随机值脉冲噪声

ICTACT Journal on Image and Video Processing Pub Date : 2017-11-01 DOI: 10.21917/ijivp.2017.0227

Neeti Singh, O. Umamaheswari

引用次数: 0

OBJECT DETECTION AND TRACKING IN THERMAL VIDEO USING DIRECTED ACYCLIC GRAPH (DAG) 基于有向无环图的热视频目标检测与跟踪

ICTACT Journal on Image and Video Processing Pub Date : 2017-08-01 DOI: 10.21917/IJIVP.2017.0221

Supriya Mangale, R. Tambe, M. Khambete

{"title":"OBJECT DETECTION AND TRACKING IN THERMAL VIDEO USING DIRECTED ACYCLIC GRAPH (DAG)","authors":"Supriya Mangale, R. Tambe, M. Khambete","doi":"10.21917/IJIVP.2017.0221","DOIUrl":"https://doi.org/10.21917/IJIVP.2017.0221","url":null,"abstract":"This paper suggests an incipient approach to perform target detection as well as tracking for single and multiple moving objects in thermal video sequences. Thermal imaging is complimentary to visible imaging as it has capability to detect object in low light or dark conditions by detecting the infrared radiation of an object and creating an image which contains temperature information. The extracted regions are then used for performing the segmentation of targets in thermal videos. In projected method first, Directed Acyclic Graph (DAG) is used for segmentation in thermal videos. Second, to enlarge the set of target proposals, DAG is initialized with an incremented object proposal set in which, from adjacent frames motion based predictions are used. Last, in this paper for selection of the specific object motion scoring function is used, which is having high optical flow gradient between the edges of the object and background is presented. After segmentation of object, centroid based object tracking is performed to track the objects in thermal videos. The proposed method is evaluated on different thermal videos and found to be robust compared with standard background subtraction method.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":"8 1","pages":"1566-1574"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44710814","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}

引用次数: 8

BETTER FINGERPRINT IMAGE COMPRESSION AT LOWER BIT-RATES: AN APPROACH USING MULTIWAVELETS WITH OPTIMISED PREFILTER COEFFICIENTS 在低比特率下更好的指纹图像压缩:一种使用优化预滤波系数的多小波的方法

ICTACT Journal on Image and Video Processing Pub Date : 2017-08-01 DOI: 10.21917/IJIVP.2017.0224

N. Remac, K. T. Shanavaz, P. Mythili

{"title":"BETTER FINGERPRINT IMAGE COMPRESSION AT LOWER BIT-RATES: AN APPROACH USING MULTIWAVELETS WITH OPTIMISED PREFILTER COEFFICIENTS","authors":"N. Remac, K. T. Shanavaz, P. Mythili","doi":"10.21917/IJIVP.2017.0224","DOIUrl":"https://doi.org/10.21917/IJIVP.2017.0224","url":null,"abstract":"In this paper, a multiwavelet based fingerprint compression technique using set partitioning in hierarchical trees (SPIHT) algorithm with optimised prefilter coefficients is proposed. While wavelet based progressive compression techniques give a blurred image at lower bit rates due to lack of high frequency information, multiwavelets can be used efficiently to represent high frequency information. SA4 (Symmetric Antisymmetric) multiwavelet when combined with SPIHT reduces the number of nodes during initialization to 1/4 compared to SPIHT with wavelet. This reduction in nodes leads to improvement in PSNR at lower bit rates. The PSNR can be further improved by optimizing the prefilter coefficients. In this work genetic algorithm (GA) is used for optimizing prefilter coefficients. Using the proposed technique, there is a considerable improvement in PSNR at lower bit rates, compared to existing techniques in literature. An overall average improvement of 4.23dB and 2.52dB for bit rates in between 0.01 to 1 has been achieved for the images in the databases FVC 2000 DB1 and FVC 2002 DB3 respectively. The quality of the reconstructed image is better even at higher compression ratios like 80:1 and 100:1. The level of decomposition required for a multiwavelet is lesser compared to a wavelet.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":"8 1","pages":"1588-1595"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42208621","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}

引用次数: 1

NEUROIMAGING AND PATTERN RECOGNITION TECHNIQUES FOR AUTOMATIC DETECTION OF ALZHEIMER’S DISEASE: A REVIEW 神经影像学和模式识别技术用于阿尔茨海默病的自动检测:综述

ICTACT Journal on Image and Video Processing Pub Date : 2017-08-01 DOI: 10.21917/IJIVP.2017.0219

R. Kamathe, K. Joshi

引用次数: 0