发布求助
文献互助 智能选刊 最新文献

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

筛选
英文 中文
Classification of mangoes by object features and contour modeling 基于目标特征和轮廓建模的芒果分类
2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428786
S. M. Roomi, R. Priya, S. Bhumesh, P. Monisha
{"title":"Classification of mangoes by object features and contour modeling","authors":"S. M. Roomi, R. Priya, S. Bhumesh, P. Monisha","doi":"10.1109/MVIP.2012.6428786","DOIUrl":"https://doi.org/10.1109/MVIP.2012.6428786","url":null,"abstract":"Object classification plays a vital role in computer vision applications such as decision making in industrial applications, satellite imagery analysis, medical image interpretation etc. Inter and intra classification of fruits in agro industries is one of the repeated and time consuming process. Intra-classification of fruits like mango, banana is being achieved by human experts who are proficient in the trade. Due to the significant growth in agro industries and lack of experts, an automation of fruits intra-classification is required. In order to address this issue, an image processing based solution is proposed for intra-classification of fruits especially mangoes based on shape and region features which are translation and rotation invariant. These features along with Object Contour Model drive Bayes classifier to classify mango varieties.","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":"116581226","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: 16
Hybrid approach for dual image encryption using nibble exchange and Hill-cipher 采用一点点交换和希尔密码的双图像加密混合方法
2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428770
H. Panduranga, H. S. Sharath Kumar, S. N. Naveen Kumar
{"title":"Hybrid approach for dual image encryption using nibble exchange and Hill-cipher","authors":"H. Panduranga, H. S. Sharath Kumar, S. N. Naveen Kumar","doi":"10.1109/MVIP.2012.6428770","DOIUrl":"https://doi.org/10.1109/MVIP.2012.6428770","url":null,"abstract":"Information security is a very important and high priority topic in the fast growing internet technology. Encryption is one of the ways to secure information. This paper presents an approach to improve the entropy of an encrypted image using Hill-cipher technique. Dual image encryption process involves three stages including Hill-cipher technique. In first stage each pixels of both the images are bitwise rotated and reversed to manipulate the pixel value. In second stage, lower nibbles in each pixel of two resultant images are exchanged. Hill-cipher is applied in stage three to obtain the final encrypted images. The resultant encrypted images are found to be more distorted and having higher entropy. This hybrid approach is implemented using LabVIEW. Decryption operation follows the reverse process of encryption, which results in undistorted images.","PeriodicalId":170271,"journal":{"name":"2012 International Conference on Machine Vision and Image Processing (MVIP)","volume":"34 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":"121604832","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: 6
Rotation invariant palmprint recognition: An overview and implementation 旋转不变掌纹识别:概述和实现
2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428781
G. Yashodha, R. Bremananth
{"title":"Rotation invariant palmprint recognition: An overview and implementation","authors":"G. Yashodha, R. Bremananth","doi":"10.1109/MVIP.2012.6428781","DOIUrl":"https://doi.org/10.1109/MVIP.2012.6428781","url":null,"abstract":"Palmprint is one of the relatively new physiological biometrics due to its stable and unique characteristics. Palmprint based biometric approaches possess several advantages over others. Palmprint images can be acquired with low resolution cameras and scanners and still have enough information to achieve good recognition rates. Palm print verification System has been utilized for a long time and it was found that many research activities were carried out. This paper presents a description of state of techniques of palmprint recognition system along with a new method has been proposed for preprocessing combined with OTSU in order to improve the identification. The results obtained in this approach has further enhanced for rotation invariant palmprint recognition.","PeriodicalId":170271,"journal":{"name":"2012 International Conference on Machine Vision and Image Processing (MVIP)","volume":"44 27","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131501275","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
Pixel based feature extraction for ear biometrics 基于像素的耳部生物特征提取
2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428756
P. R. Kumar, S. S. Dhenakaran
{"title":"Pixel based feature extraction for ear biometrics","authors":"P. R. Kumar, S. S. Dhenakaran","doi":"10.1109/MVIP.2012.6428756","DOIUrl":"https://doi.org/10.1109/MVIP.2012.6428756","url":null,"abstract":"Ear Biometrics identification is rapidly growing in current biometric industry because of its unique features, stableness and passive of human involvement. Ear biometric system better suits for automatic identification of individual or human. We propose a pixel based feature identification approach for Ear biometric model.","PeriodicalId":170271,"journal":{"name":"2012 International Conference on Machine Vision and Image Processing (MVIP)","volume":"8 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":"131903174","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
Automatic image mosaic system using steerable Harris corner detector 采用可操纵哈里斯角探测器的自动图像拼接系统
2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428767
Mahesh, M. Subramanyam
{"title":"Automatic image mosaic system using steerable Harris corner detector","authors":"Mahesh, M. Subramanyam","doi":"10.1109/MVIP.2012.6428767","DOIUrl":"https://doi.org/10.1109/MVIP.2012.6428767","url":null,"abstract":"Image mosaic is to be combine several views of a scene in to single wide angle view. This paper proposes the image mosaic based on feature based approach. The steps in image mosaic include feature point detection, feature point descriptor extraction and feature point matching. A RANSAC algorithm is applied to eliminate number of mismatches and obtain transformation matrix between the images. The input image is transformed with the correct mapping model for image stitching and same is estimated. In this paper, feature points are detected using steerable Harris and compared with traditional Harris, SUSAN corner detectors.","PeriodicalId":170271,"journal":{"name":"2012 International Conference on Machine Vision and Image Processing (MVIP)","volume":"23 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":"115603871","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
Automatic noise identification in images using moments and neural network 基于矩量和神经网络的图像噪声自动识别
2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428761
P. Vasuki, S. Mohamed Mansoor Roomi, C. Bhavana, E. L. Deebikaa
{"title":"Automatic noise identification in images using moments and neural network","authors":"P. Vasuki, S. Mohamed Mansoor Roomi, C. Bhavana, E. L. Deebikaa","doi":"10.1109/MVIP.2012.6428761","DOIUrl":"https://doi.org/10.1109/MVIP.2012.6428761","url":null,"abstract":"Identifying noise from the original image is still a challenging research in image processing and is essential in order to counter the effects of unnecessary filtering process. Noise gets added to an image during image capture, transmission, or processing and degrades the performance of any image processing algorithms. Prior to de-noising step, the image should be tested for the identification of noise. Though Several approaches have been introduced in literature earlier for noise identification, each has its own assumption, advantages are not generic. This paper proposes a novel method based on statistical features with neural network classifier to identify the different types of noises such as Additive white Gaussian Noise, Salt & pepper Noise, Speckle Noise in the image without the human intervention. Extensive simulations on variety of images show that the proposed method effectively identifies the noise in a given image.","PeriodicalId":170271,"journal":{"name":"2012 International Conference on Machine Vision and Image Processing (MVIP)","volume":"11 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":"123856928","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: 13
A wavelet based morphological mass detection and classification in mammograms 基于小波的乳房x线影像形态学肿块检测与分类
2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428752
J. Anitha, J. Peter
{"title":"A wavelet based morphological mass detection and classification in mammograms","authors":"J. Anitha, J. Peter","doi":"10.1109/MVIP.2012.6428752","DOIUrl":"https://doi.org/10.1109/MVIP.2012.6428752","url":null,"abstract":"This paper presents an efficient mass detection and classification in mammogram images with the use of features extracted from the mass regions obtained by the automatic morphological based segmentation method. In this approach, the mammogram images are preprocessed to extract the breast profile and improve the contrast. The segmentation is done with combination of various morphological operations. In this approach, the wavelet features are extracted from the detected mass regions and is compared with feature extracted using Gray Level Co-occurrence Matrix (GLCM) to differentiate the TP and FP regions. Classifications of the mass regions are carried out through the Support Vector Machine (SVM) to separate the segmented regions into masses and non-masses based on the features. The methodology achieves 95% of accuracy.","PeriodicalId":170271,"journal":{"name":"2012 International Conference on Machine Vision and Image Processing (MVIP)","volume":"35 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":"125819229","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: 20
首页 上一页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信