2012 International Conference on Machine Vision and Image Processing (MVIP)最新文献

Motion-compensated video compression using variable length Huffman coding 运动补偿视频压缩使用可变长度霍夫曼编码

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428765

K. Vidyavathi, R. S. Sabeenian

引用次数: 3

Iterative image fusion using neuro fuzzy logic and applications 基于神经模糊逻辑的迭代图像融合及其应用

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428775

D. Srinivasa Rao, M. Seetha, M. Hazarath

引用次数: 10

Inspection and error analysis of Geneva gear on machine vision system using Sherlock™ and VB 6.0 Algorithm 基于Sherlock™和VB 6.0算法的机器视觉日内瓦齿轮检测与误差分析

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428793

C. Vigneswaran, M. Madhu, R. Rajamani

{"title":"Inspection and error analysis of Geneva gear on machine vision system using Sherlock™ and VB 6.0 Algorithm","authors":"C. Vigneswaran, M. Madhu, R. Rajamani","doi":"10.1109/MVIP.2012.6428793","DOIUrl":"https://doi.org/10.1109/MVIP.2012.6428793","url":null,"abstract":"Indexing mechanisms find several applications in machineries, equipments and instruments such as, watches, projectors, machine tools, printing and pressing machinery, packaging and automatic machinery, etc., Currently, Geneva mechanism is extensively used for this purpose due to its reduction in shock loading. Therefore, Geneva mechanism should be carefully designed and those attained dimensions inclusive of tolerances must be measured closely to the true value. These measurements need more than one measuring instrument, which cumulatively costs much. Machine Vision is the suitable remedial solution, which acts fast and reduces the cost for large quantity measurement. In this work, an updated version of analyzer is developed by collaborating Machine vision software with Visual Basic 6.0 to make easy and comfortable, even the user is not fully aware of machine vision operations. The Geneva wheel is designed and modeled using CAD software, the dimensional features of the component is compared with the original modeled CAD image, taken as template. For this, a machine Vision software named Sherlock™ is interfaced with Visual Basic 6.0 designed window. This window is customized for user friendly environment to facilitate even an unskilled operator to operate the inspection system without having a deep knowledge about Machine Vision and its software's.","PeriodicalId":170271,"journal":{"name":"2012 International Conference on Machine Vision and Image Processing (MVIP)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130654818","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: 2

Enhanced accuracy of breast cancer detection in digital mammograms using wavelet analysis 小波分析在数字乳房x光检查中提高乳腺癌检测的准确性

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428783

S. Padmanabhan, R. Sundararajan

{"title":"Enhanced accuracy of breast cancer detection in digital mammograms using wavelet analysis","authors":"S. Padmanabhan, R. Sundararajan","doi":"10.1109/MVIP.2012.6428783","DOIUrl":"https://doi.org/10.1109/MVIP.2012.6428783","url":null,"abstract":"About every minute a woman dies out of breast cancer, worldwide. The need for early detection cannot be overstated. Towards this, mammography is a boon for both early detection and screening of breast cancer tumors. It is an imaging system that uses low dose x-rays for examining the breasts, by the electrons reflected from the tissues. The use of screening mammography is associated with the detection of breast cancer at an earlier stage and smaller size, resulting in a reduction in mortality. This study was aimed at enhancing the current accuracy (diagnostic) of digital mammograms using industry standard simulation software tool, MATLAB and the MIAS dataset. The technique involves identification of tumor cells to segment them in terms of different stages of the disease. We consider the process of object detection, recognition and classification of mammograms with the aim of differentiating between normal and abnormal (benign or cancerous) cells. It is reported that dense breasts can make traditional mammograms more difficult to interpret. Although newer digital mammography techniques claim for better detection in dense breast tissues, the availability of such expensive digital mammograms is not widespread. This problem can be minimized by analyzing different breast structures (mammograms) using the MATLAB numerical analysis software for image processing applications. The results indicated up to 91% accuracy, compared to 70% at present. Our proposed solution has proved to be an effective way of detecting breast cancer early in different types of breast tissues.","PeriodicalId":170271,"journal":{"name":"2012 International Conference on Machine Vision and Image Processing (MVIP)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121635755","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: 19

Human action classification in partitioned feature space 基于分区特征空间的人类行为分类

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428751

S. Mohamed, M. Roomi, S. Saranya, S. N. Banu

引用次数: 1

Texture and statistical analysis of mammograms: A novel method to detect tumor in Breast Cells 乳房x光片的纹理和统计分析:一种检测乳腺细胞肿瘤的新方法

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428784

S. Padmanabhan, R. Sundararajan

引用次数: 4

Eye localization towards developing a head postures based interface for wheelchair 眼睛定位开发基于头部姿势的轮椅界面

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428754

M. Carmel Sobia, S. Menora, A. Abudhahir

引用次数: 0

Dental cyst delineation using live wire algorithm 使用带电线算法描绘牙囊肿

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428777

G. Ulaganathan, A. Banumathi, J. Amutha, A. Jeevani Selvabala

引用次数: 7

Graphic-object detection in documents 文档中的图形对象检测

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428755

K. Rajamani, J. Sampathkumar

引用次数: 2

Automatic prediction of Diabetic Retinopathy and Glaucoma through retinal image analysis and data mining techniques 基于视网膜图像分析和数据挖掘技术的糖尿病视网膜病变和青光眼的自动预测

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428782

R. Ramani, L. Balasubramanian, S. Jacob

{"title":"Automatic prediction of Diabetic Retinopathy and Glaucoma through retinal image analysis and data mining techniques","authors":"R. Ramani, L. Balasubramanian, S. Jacob","doi":"10.1109/MVIP.2012.6428782","DOIUrl":"https://doi.org/10.1109/MVIP.2012.6428782","url":null,"abstract":"Application of computational techniques in the field of medicine has been an area of intense research in recent years. Diabetic Retinopathy and Glaucoma are two retinal diseases that are a major cause of blindness. Regular Screening for early disease detection has been a highly labor - and resource- intensive task. Hence automatic detection of these diseases through computational techniques would be a great remedy. In this paper, a novel computational approach for automatic disease detection is proposed that utilizes retinal image analysis and data mining techniques to accurately categorize the retinal images as Normal, Diabetic Retinopathy and Glaucoma affected. Three feature relevance and sixteen classification Algorithms were analyzed and used to identify the contributing features that gave better prediction results. Our results prove that C4.5 and random tree classification techniques generate the maximum multi-class categorization training accuracy of 100% in classifying 45 images from the Gold Standard Database. Moreover the Fisher's Ratio algorithm reveals the most minimal and optimal set of predictive features on the retinal image training data.","PeriodicalId":170271,"journal":{"name":"2012 International Conference on Machine Vision and Image Processing (MVIP)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121550897","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: 40