2012 International Conference on Machine Vision and Image Processing (MVIP)最新文献_第3页

Involuntary diagnosis of intraductal breast images using gaussian mixture model 利用高斯混合模型对导管内乳腺图像进行非自愿诊断

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428773

M. S. Kumar, E. Dinesh, T. Mohanraj

{"title":"Involuntary diagnosis of intraductal breast images using gaussian mixture model","authors":"M. S. Kumar, E. Dinesh, T. Mohanraj","doi":"10.1109/MVIP.2012.6428773","DOIUrl":"https://doi.org/10.1109/MVIP.2012.6428773","url":null,"abstract":"Intraductal Carcinoma is a noninvasive condition in which abnormal cells are found in the lining of a breast duct. The abnormal cells have not spread outside the duct to other tissues in the breast. During some cases, Intraductal Carcinoma may become persistent cancer. Also spread to other tissues, though it is not known at this time how to predict which lesions will become invasive. Intraductal cancer is the most common type of breast cancer in women. Memory Intraductal includes 3-types of cancer: Usual Ductal Hyperplasia (UDH), Atypical Ductal Hyperplasia (ADH), and Ductal Carcinoma in Situ (DCIS). So the system of detecting the breast microscopic tissue of UDH, ADH, DCIS is proposed. The current standard of care is to perform percutaneous needle biopsies for diagnosis of palpable and image-detected breast abnormalities. UDH is considered benign and patients diagnosed UDH undergo routine follow-up, whereas ADH and DCIS are considered actionable and patients diagnosed with these two subtypes get additional surgical procedures. The systems classify the tissue based on the quantitative feature derived from the images. The statistical features are obtained. The approach makes use of preprocessing, Cell region segmentation, Individual cell segmentation, Feature extraction technique for the detection of cancer.","PeriodicalId":170271,"journal":{"name":"2012 International Conference on Machine Vision and Image Processing (MVIP)","volume":"66 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":"124600114","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: 0

Focus measures for SFF-inspired relative depth estimation sff启发的相对深度估计的焦点度量

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428791

R. Senthilnathan, R. Sivaramakrishnan

{"title":"Focus measures for SFF-inspired relative depth estimation","authors":"R. Senthilnathan, R. Sivaramakrishnan","doi":"10.1109/MVIP.2012.6428791","DOIUrl":"https://doi.org/10.1109/MVIP.2012.6428791","url":null,"abstract":"Shape from Focus (SFF) is a method which recovers the 3D geometry of the scene based on a sequence of images taken from different focus distances between the camera and the object. Generally SFF techniques require parallel projection of the scene on to the image plane so that the corresponding pixels in the set of images taken are easily identified. This can be achieved by using a lens which does parallel projection such as a telecentric lens. Moreover the SFF method is widely applied for extremely small objects due to the limited range of magnification that can be maintained. This again is another manifestation of the fact depth of objects produce perspective shift (generally called as structure-dependent pixel motion) in the image plane. All these facts are applicable for situations which utilizes SFF for complete reconstruction of the scene. Applications involving shape information extracted from focus as a secondary cue need not require a complete dense reconstructed information from SFF. Such applications might allow usage of wide angle lenses where the projection is basically a perspective projection of the scene on to the image plane. The research work utilizes a wide angle lens for SFF based scene reconstruction consisting of a macroscopic object. The paper is an attempt to present 24 different focus measures used for quantifying image focus from which depth is interpolated using a standard function. Since the images in the sequence suffer from changes in magnification, finding the correspondence itself is an issue worth addressing. The pixel motion is tackled by a powerful corner detector and a robust matching algorithm. The knowledge of the right focus measure is very important since it is after all from the focus measure depth of the scene is interpolated. The focus measures presented in the paper are a collection from various applications such as microscopy imaging, auto focussing, holographic reconstructions etc., but applied to an image sequence containing variations in focus and magnification.","PeriodicalId":170271,"journal":{"name":"2012 International Conference on Machine Vision and Image Processing (MVIP)","volume":"58 36","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120888908","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

Orthogonal polynomials based low bit rate image coding 基于正交多项式的低比特率图像编码

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428764

T. Karthikeyan, R. Krishnamoorthy, B. Praburaj

引用次数: 1

Performance analysis of LPG PCA algorithm in medical images LPG PCA算法在医学图像中的性能分析

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428776

R. Hari Kumar, B. Vinoth kumar, S. Gowthami

引用次数: 4

A study on regression spline based local minima approach for gaussian noise reduction in images 基于回归样条的局部最小值法在图像高斯噪声降噪中的研究

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428760

V. S. Bhadouria, D. Ghoshal

引用次数: 0

Ma-Th algorithm for people count in a dense crowd and their behaviour classification Ma-Th算法用于密集人群中的人计数及其行为分类

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428750

B. Yogameena, N. Packiyaraj, S. S. Perumal, P. Saravanan

引用次数: 7

A comparison of FFT and DCT based Phase Correlation function for focused and defocused images 基于FFT和DCT的相位相关函数对聚焦和散焦图像的比较

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428788

A. Raj, S. Majeeth, R. Staunton

引用次数: 3

Robust plant recognition using Graph cut based flower segmentation and PHOG based feature extraction 基于图切花分割和PHOG特征提取的鲁棒植物识别

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428757

K. Deepika, I. Ruth, S. Keerthana, B. Sathya Bama, S. Avvailakshmi, A. Vidhya

引用次数: 0

Spatially adaptive image restoration method using LPG-PCA and JBF 基于LPG-PCA和JBF的空间自适应图像恢复方法

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428759

M. Vijay, S. Subha

引用次数: 6

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428748

R. Venkata Ramana Chary, K. Sunitha, D. Rajya Lakshmi

{"title":"Similar image searching from image database using cluster mean sorting and performance estimation","authors":"R. Venkata Ramana Chary, K. Sunitha, D. Rajya Lakshmi","doi":"10.1109/MVIP.2012.6428748","DOIUrl":"https://doi.org/10.1109/MVIP.2012.6428748","url":null,"abstract":"Computer vision field over the last decades, Content-Based Image Retrieval (CBIR) systems are used in order to search, retrieve and browse image from databases. This accumulation of large collections of digital images has created the need for efficient and intelligent schemes for classifying and retrieval of images. In our proposed method, we are using, Clustering Algorithms for retrieving the images from huge volumes of data with better performance. This requires image processing, feature extraction, classification of images and retrieval steps in order to develop an efficient image retrieval system. In this work, processing is done through the image clustering method [1] which is used for feature extraction which is taken place. For retrieval of images, mean values are calculated between Query image and database images and all clustered mean values are considered as a sorted order. When the comparisons are allowed between the images, in our observation we founded excellent performance and similarities in between images. The main aim of this work is to extract images with good similarity when the images are retrieved based on query image.","PeriodicalId":170271,"journal":{"name":"2012 International Conference on Machine Vision and Image Processing (MVIP)","volume":"24 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":"122552090","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: 3