2012 International Conference on Machine Vision and Image Processing (MVIP)最新文献_第4页

Classification of selected medicinal plants leaf using image processing 基于图像处理的药用植物叶片分类

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428747

A. Gopal, S. Prudhveeswar Reddy, V. Gayatri

{"title":"Classification of selected medicinal plants leaf using image processing","authors":"A. Gopal, S. Prudhveeswar Reddy, V. Gayatri","doi":"10.1109/MVIP.2012.6428747","DOIUrl":"https://doi.org/10.1109/MVIP.2012.6428747","url":null,"abstract":"Plants are an indispensable part of our ecosystem and the dwindling number of plant varieties is a serious concern. To conserve plants, their rapid identification by botanists is a must, thus a tool is needed which could identify plants using easily available information. There is a growing scientific consensus that plant habitats have been altered and species are disappearing at rates never witnessed before. The biodiversity crisis is not just about the perilous state of plant species but also of the specialists who know them This initially requires data about various plant varieties, so that they could be monitored, protected and can be used for future. Plants form the backbone of Ayurveda and today's Modern day medicine and are a great source of revenue. Due to Deforestation and Pollution, lot of medicinal plant leaves have almost become extinct. So, there is an urgent need for us to identify them and regrow them for the use of future generations. Leaf Identification by mechanical means often leads to wrong identification. Due to growing illegal trade and malpractices in the crude drug industry on one hand and lack of sufficient experts on the other hand, an automated and reliable identification and classification mechanism in order to handle the bulk of data and to curb the malpractices is needed. The following paper aims at implementing such system using image processing with images of the plant leaves as a basis of classification. The software returns the closest match to the query. The proposed algorithm is implemented and the efficiency of the system is found by testing it on 10 different plant species. The software is trained with 100 (10 number of each plant species) leaves and tested with 50 (tested with different plant species) leaves. The efficiency of the implementation of the proposed algorithms is found to be 92%.","PeriodicalId":170271,"journal":{"name":"2012 International Conference on Machine Vision and Image Processing (MVIP)","volume":"132 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":"124883546","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: 55

Inspection of faults in textile web materials using wavelets and ANFIS 基于小波和ANFIS的纺织卷材故障检测

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428792

B. Venkatesan, U. S. Ragupathy, P. Vidhyalakshmi, B. Vinoth

{"title":"Inspection of faults in textile web materials using wavelets and ANFIS","authors":"B. Venkatesan, U. S. Ragupathy, P. Vidhyalakshmi, B. Vinoth","doi":"10.1109/MVIP.2012.6428792","DOIUrl":"https://doi.org/10.1109/MVIP.2012.6428792","url":null,"abstract":"Quality is the watchword of any type of business. A product without quality leads to loss and lack of customer satisfaction. This is true in case of textile industries also. Textile manufacturing is a process of converting various types of fibers into yarn, which in turn woven into fabric. Weaving process is used to produce the fabric or cloth by interlacing two distinct set of yarn threads namely warp and weft yarn. In textile industries, quality inspection is one of the major problems for fabric manufacturers. At present, the fault detection is done manually after production of a sufficient amount of fabric. The fabric obtained from the production machine are batched into larger rolls and subjected to the inspection frame. The nature of the work is very dull and repetitive. Due to manual inspection of the manufactured fabric, there is a possibility of human errors with high inspection time, hence it is uneconomical. This paper proposed a PC-based inspection system with benefits of low cost and high detection rate. Both normal and faulty images are processed and features are extracted by using Gray Level Co-occurrence Matrix (GLCM) and classification is done using Adaptive Neuro Fuzzy Inference System (ANFIS). Proposed scheme performs 36.66% better than the existing microcontroller based classification system.","PeriodicalId":170271,"journal":{"name":"2012 International Conference on Machine Vision and Image Processing (MVIP)","volume":"913 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120884244","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: 7

High density impulse noise removal in RGB images using Lone Diagonal Sorting algorithm 基于单对角排序算法的RGB图像高密度脉冲噪声去除

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428758

A. Rajamani, V. Krishnaveni, K. Padmaja, N. Thomas

引用次数: 4

A new no-reference image quality measure to determine the quality of a given image using object separability 一种新的无参考图像质量度量，利用物体可分离性来确定给定图像的质量

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428768

K. De, V. Masilamani

引用次数: 1

A comparative study on mammographic image denoising technique using wavelet, curvelet and contourlet transforms 小波变换、曲线变换和轮廓波变换乳腺x线图像去噪技术的比较研究

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428762

E. Malar, A. Kandaswamy, S. S. Kirthana, D. Nivedhitha

{"title":"A comparative study on mammographic image denoising technique using wavelet, curvelet and contourlet transforms","authors":"E. Malar, A. Kandaswamy, S. S. Kirthana, D. Nivedhitha","doi":"10.1109/MVIP.2012.6428762","DOIUrl":"https://doi.org/10.1109/MVIP.2012.6428762","url":null,"abstract":"This article focuses on comparing the discriminating power of the various multi-resolution based thresholding techniques - wavelet, curvelet, and contourlet for image denoising. Using multiresolution techniques, mammogram images are decomposed into different resolution levels, which are sensitive to different frequency bands. We implement the proposed algorithm on the mammogram images embedded in Random, Salt and Pepper, Poisson, Speckle and Gaussian noises. Curvelet transform employed in the proposed scheme provides sparse decomposition as compared to the wavelet and contourlet transform methods. The curvelet transform has a strong directional character which combines multiscale analysis and ideas of geometry to achieve the optimal rate of convergence by simple thresholding. The proposed algorithm succeeded in providing improved denoising performance to recover the shape of edges and important detailed components. Empirical results proved that the curvelet-based thresholding can obtain a better image estimate than the wavelet- based and contourlet-based restoration methods.","PeriodicalId":170271,"journal":{"name":"2012 International Conference on Machine Vision and Image Processing (MVIP)","volume":"86 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":"131237726","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: 11

Computer vision based approach for Indian Sign Language character recognition 基于计算机视觉的印度手语字符识别方法

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428790

R. K. Shangeetha, V. Valliammai, S. Padmavathi

引用次数: 30

Edge detection in electron microscopy biological images using statistical dispersion 利用统计色散的电子显微镜生物图像边缘检测

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428769

V. S. Bhadouria, D. Ghoshal

{"title":"Edge detection in electron microscopy biological images using statistical dispersion","authors":"V. S. Bhadouria, D. Ghoshal","doi":"10.1109/MVIP.2012.6428769","DOIUrl":"https://doi.org/10.1109/MVIP.2012.6428769","url":null,"abstract":"During the last few decades, there has been a tremendous development in the field of biological sciences. With this, there is an increasing demand for analyzing the molecular or cellular features of the cell in the images, acquired with the electron microscopes (EM). However, despite significant progress in image processing, the efficient detection of features and edges in biological images is still a challenging task due to the presence of minute structures with low intensity variation, compared with the background. In this paper, a novel algorithm for edge detection in electron microscopy biological image is proposed. The edge detector is based on the statistical dispersion of D8 pixels followed by an edge thinning operation. The proposed algorithm has been compared with other state-of-art edge detectors viz. Sobel's and Canny's edge detectors and results suggest that the proposed scheme perform better in detecting the significant edges in Tobacco Mosaic Virus (TMV; scanning-transmission electron microscopy image) and Virus Like Particles (VLPs; transmission electron microscopy image); used as test images in the present study. Experimental results (in terms of Pratt's figure of merit) also suggest that the proposed algorithm is more robust to noise when compared to Sobel's or Canny's edge detector. Consequently, the proposed algorithm can operate efficiently in a noisier environment, compared to Sobel's or Canny's edge detector.","PeriodicalId":170271,"journal":{"name":"2012 International Conference on Machine Vision and Image Processing (MVIP)","volume":"235 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":"124581488","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

Content based image retrieval based on Database revision 基于数据库修订的基于内容的图像检索

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428753

Sreedevi S, Shinto Sebastian

引用次数: 19

Semantic modeling of natural scenes by local binary pattern 基于局部二元模式的自然场景语义建模

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428787

R. Raja, S. Md Mansoor Roomi, D. Kalaiyarasi

引用次数: 5

Image Super Resolution using Fourier-Wavelet transform 图像超分辨率的傅里叶-小波变换

2012 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2012-12-01 DOI: 10.1109/MVIP.2012.6428772

S. Ashwini Devi, A. Vasuki

引用次数: 4