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检索条件"任意字段=Proceedings of the Thirteenth Indian Conference on Computer Vision, Graphics and Image Processing"

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TASOCNN: A topology adaptive self-organizing circular neural network and its application to color segmentation 10

TASOCNN: A topology adaptive self-organizing circular neural...

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proceedings of the Seventh indian conference on computer vision, graphics and image processing

作者： Roy, Anandarup Parui, Swapan Kumar Roy, Utpal CVPR Unit Indian Statistical Institute 203 B. T. Road Kolkata 700108 India DCSS Visva-Bharati University Santiniketan Birbhum 731235 India

ISBN: (纸本)9781450300605

Topology adaptive neural networks are popular because of its capability to adopt the underlaying topology of data. In this paper we develop a topology adaptive self organizing circular neural network (TASOCNN) model for circular-linear data sampled from an unit disk. The basic framework uses the TASONN procedure of Datta et. al. [8]. The update rules and the distance measure are reformulated with the inclusion of directional information. In the iterative TASOCNN process, we create/update edges between any two winner processors. These edges are weighted, hence finally a weighted processor graph is created concerning processors as vertices. By removing possible inter-cluster edges from the connected components of this processor graph, a set of subgraphs can be obtained. For this purpose we use a cost function based on edge length and strength. Many of these subgraphs become close to one another. These close subgraphs are merged. Finally, each subgraph represents a cluster in the data. We apply TASOCNN for color based image segmentation. TASOCNN is constructed in the huesaturation space. The Berkeley segmentation dataset is used to present the results. An evaluation is made by means of probabilistic rand index. Our experiments reveals satisfactory outcome of TASOCNN. © 2010 ACM.

关键词： Topology

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image based PTM synthesis for realistic rendering of low resolution 3D models 10

Image based PTM synthesis for realistic rendering of low res...

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proceedings of the Seventh indian conference on computer vision, graphics and image processing

作者： Rajiv, Pradeep Namboodiri, Anoop M. Center For Visual Information Technology International Institute of Information Technology Hyderabad India

ISBN: (纸本)9781450300605

Capturing the shape and texture of large structures such as monuments and statues at very high resolution is extremely expensive, both in terms of time as well as storage space. In many cases the finer details are generated by surface properties of the material, and the appearance is statistically uniform. In this paper, we present an approach to add surface details to a coarse 3D model of an object based on two additional information: a set of images of the object and a high resolution model of the material that the object is made of. The material model that we employ is the Polynomial Texture Map (PTM), which captures the appearance of a surface under various illumination conditions. We use the observed images of the object as constraints to synthesize texture samples for each triangle of the object under any given illumination. The primary challenge is to synthesize a polynomial model of the texture, where the constraints arise in the image domain. We use the knowledge of object illumination to map the texture models into image space and compute the optimal patch. The texture transfer then happens as a complete 3D texturemodel. We also consider the problems of pose, scale, reflectance and smoothness of surface while carrying out the texture transfer. We synthesize the texture of an object at a per-triangle basis while carrying out operations such as normalization and blending to take care of discontinuities at the edges. © 2010 ACM.

关键词： image texture

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Face recognition using principal component analysis and multi-class support vector machine

Face recognition using principal component analysis and mult...

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2010 International conference on image processing, computer vision, and Pattern Recognition, IPCV 2010

作者： Chawla, Karishma Bansal, Veena Arya, K.V. Shukla, Anupam Department of Information Technology Indian Institute of Information Technology and Management Gwalior Gwalior Madhya Pradesh India

ISBN: (纸本)9781601321541

In this paper, Support Vector Machines (SVMs) are used to tackle the face recognition problem, using Principal Component Analysis (PCA) for feature extraction and Support Vector Machine (SVM) for multi-class classification. It is found that SVM is invariant under PCA transform. which qualifies PCA to be a desirable dimension reduction method for SVM. This proposed method is tested on the Cambridge ORL face database. It is compared with the standard eigenface approach and experimental results have shown that the proposed method lends itself to higher recognition rate of 97.5%.

关键词： Principal component analysis

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Decision fusion for urine particle classification in multispectral images 10

Decision fusion for urine particle classification in multisp...

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proceedings of the Seventh indian conference on computer vision, graphics and image processing

作者： Hans, Charu Merchant, Fatima A. Shah, Shishir K. Quantitative Imaging Laboratory University of Houston Dept. of Computer Science Houston TX United States University of Houston Dept. of Engineering Technology Houston TX United States

ISBN: (纸本)9781450300605

The microscopic analysis of the urinary sediment is important in making diagnoses for a variety of diseases, including urinary tract infection, urinary tract tumors, occult glomerulonephritis, and interstitial nephritis. A typical automated system acquires images of urinary sediment by employing a CCD camera, and then detects and recognizes the distinct particles automatically from these images. Automated recognition of these particles represents a significant challenge due to poor image resolution, strong variability of particle shape and size, and challenges associated with detection of particles in the presence of noisy backgrounds. In this paper, we present a novel method for urine particle classification based on the use of local descriptors coupled with regression based decision fusion. Specifically, DAISY descriptors have been used to capture the textural characteristics of each particle and subjected to dimensionality reduction across three linear subspaces to increase the diversity in decision making along with lowering the "curse of dimensionality". Classification in each subspace is based on computing a similarity score, which is then fused through support vector regression to obtain a final classification. The approach is applied to both brightfield and multispectral data to ascertain the benefits of multispectral imaging for urine analysis. Urine particles analyzed included crystals, casts and blood cells, and the results obtained show an average classification accuracy of 92.6% for 6 classes of urinary particles. © 2010 ACM.

关键词： CCD cameras

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Handwritten Gujarati numeral optical character recognition using hybrid feature extraction technique

Handwritten Gujarati numeral optical character recognition u...

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2010 International conference on image processing, computer vision, and Pattern Recognition, IPCV 2010

作者： Desai, Apurva A. Department of Computer Science Veer Narmad South Gujarat University Udhna Magdalla Road Surat - 395007 Gujarat India

ISBN: (纸本)9781601321541

Lot of work has been done for Optical Character Recognition (OCR) for various indian languages. But for Gujarati, a language belonging to Devnagari family of languages and spoken in the western state of Gujarat in India, hardly any work can be traced especially for handwritten characters. In this work I have addressed the problem of handwritten Gujarati numerals. Here pre-process techniques like global threshold, erosion and dilation, skew correction etc, are used. A novel hybrid feature extraction technique is suggested and used which is constituted by a structural approach and statistical approach of feature extraction. image is subdivided and then the pixel information is used as a structural approach whereas the aspect ratio of the number is considered as a statistical approach. For classification kNN classifier has been used. This model gives overall accuracy of 96.99% for the handwritten Gujarati numerals.

关键词： Optical character recognition

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Inpainting large missing regions in range images

Inpainting large missing regions in range images

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2010 20th International conference on Pattern Recognition, ICPR 2010

作者： Bhavsar, Arnav V. Rajagopalan, A.N. Image Processing and Computer Vision Lab. Indian Institute of Technology Madras Chennai India

ISBN: (纸本)9780769541099

We propose a technique to inpaint large missing regions in range images. Such a technique can be used to restore degraded/occluded range maps. It can also serve to reconstruct dense depth maps from sparse measurements which can speed up the acquisition. Our method uses the visual cue from segmentation of an intensity image registered to the range image. Our approach enforces that pixels in the same segment should have similar range. Our simple strategy involves plane-fitting and local medians over segments to compute local energies for labeling unknown pixels. Our results exhibit high quality inpainting with very low errors. © 2010 IEEE.

关键词： Pixels

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Cell volume estimation from a sparse collection of noisy confocal image slices 10

Cell volume estimation from a sparse collection of noisy con...

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proceedings of the Seventh indian conference on computer vision, graphics and image processing

作者： Chakraborty, Anirban Liu, Min Mkrtchyan, Katya Reddy, G. Venugopala Roy-Chowdhury, Amit Department of Electrical Engineering University of California Riverside Riverside CA 92521 United States Department of Computer Science and Engineering University of California Riverside Riverside CA 92521 United States Department of Botany and Plant Sciences University of California Riverside Riverside CA 92521 United States

ISBN: (纸本)9781450300605

Measurement and quantification of the volume of cells are very useful for analyzing and understanding cellular behavior, such as cell growth and differentiation. One popular practice is to use Confocal Laser Scanning Microscopy(CLSM) to image cell slices and then reconstruct the 3-D volume of the cells from these serial optical slices. However, all of the current methods of volume estimation using CLSM imaging require large number of cell slices (i.e. high volume of image data). But in many practical situations, especially in case of CLSM based live cell imaging, such high depth resolution is not feasible in order to avoid photodynamic damage to cells from prolonged exposure to laser. In this work, we have addressed this problem of finding out the volume of a plant cell using CLSM when the amount of data is as limited as two to three slices per cell. We assume an ellipsoid model for a cell and estimate the minimum volume ellipsoid that encloses all three cell slices in the 3D stack. The level set based segmentation often tends to produce cell slice areas less than the actual due to poor signal to noise ratio in images and that is the reason why we choose a Minimum Volume Enclosing Ellipsoid over mean or best-fit ellipsoids. We tested the proposed computational method on time-lapse CLSM images of Shoot Apical Meristem (SAM) cells of model plant Arabidopsis Thaliana. The volume estimates of individual cells are reasonably close to what is expected from biological point of view. © 2010 ACM.

关键词： Cells

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Classification of cell cycle phases in 3D confocal microscopy using PCNA and chromocenter features 10

Classification of cell cycle phases in 3D confocal microscop...

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proceedings of the Seventh indian conference on computer vision, graphics and image processing

作者： Jaeger, Stefan Palaniappan, Kannappan Casas-Delucchi, Corella S. Cardoso, M. Cristina Department of Computer Science University of Missouri Columbia MO 65211 United States Department of Biology Technische Universität Darmstadt 64287 Darmstadt Germany

ISBN: (纸本)9781450300605

Cell cycle progression studies using subcellular markers offer important insight into cellular mechanisms of disease and therapeutic drug development. Due to the large volumes of microscopy data involved in such studies, a manual approach to extracting quantitative information is not only prohibitive but error prone. We present an automatic cell cycle phase identification algorithm applied to 3D spinning disk confocal microscopy imagery of mouse embryonic fibroblast cells. In our training dataset, each 3D image stack depicts a single cell in a manually identified cell phase, and is recorded via two channels showing the fluorescently marked protein PCNA and the chromocenters, respectively. We use a 3D k-means approach to segment each volume and extract a set of shape and curvature features to characterize the subcel-lular foci patterns associated with cell cycle phases for each channel. Radial features are used to describe the spatial distribution of PCNA over the course of the cell cycle. A support vector machine (SVM) classifier using 234 features was trained and achieved a recognition rate of 83% for the chromocenter and 86% for the PCNA channels separately on the testing data. A combined SVM classifier using both channels and 468 features further improved the accuracy to nearly 92% for five phases (G1, SE, SM, SL, G2) and shows promising scalability. © 2010 ACM.

关键词： Confocal microscopy

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Color feature based approach for determining ink age in printed documents

Color feature based approach for determining ink age in prin...

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International conference on Pattern Recognition

作者： Halder, Biswajit Garain, Utpal Dept. of Information Technology Mallabhum Institute of Technology Bisnupur WB India Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T. Road Kolkata 700108 India

ISBN: (纸本)9780769541099

Answering to a query like when a particular document was printed is quite helpful in practice especially forensic purposes. This study attempts to develop a general framework that makes use of image processing and pattern recognition principles for ink age determination in printed documents. The approach, at first, computationally extracts a set of suitable color features and then analyzes them to properly associate them with ink age. Finally, a neural net is designed and trained to determine ages of unknown samples. The dataset used for the present experiment consists of the cover pages of LIFE magazines published in between 1930's and 70's (five decades). Test results show that a viable framework for involving machines in assisting human experts for determining age of printed documents. © 2010 IEEE.

关键词： image processing

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An efficient document image reconstruction and binarization method using Compressed Sensing

An efficient document image reconstruction and binarization ...

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IADIS International conference computer graphics, Visualization, computer vision and image processing 2009, CGVCVIP 2009. Part of the IADIS Multi conference on computer Science and Information Systems, MCCSIS 2009

作者： Satyanarayana, J.V. Ramakrishnan, A.G. Indian Institute of Science Bangalore India

ISBN: (纸本)9789728924843

In this paper, we present a novel approach of document image acquisition with reduced number of measurements, requiring lesser number of sensing elements. Reduction in the number of sensors also directly implies faster acquisition. Our approach is built around the Compressed Sensing paradigm, in which signals which are sparse can be captured and reconstructed with considerably lesser number of measurements than required by Nyquist sensing. Signal reconstruction algorithms used in Compressed Sensing are computationally intensive. A brief survey of some of the popular reconstruction algorithms is also given in the paper. In the algorithm proposed in this work, the reconstruction step associated with recovering the binary image from the Compressed Sensing measurements is pruned to take advantage of the inherent requirement of the binarization of the acquired image. Binarization of captured documents which is an essential step for post processing is built into the proposed scheme. © 2009 IADIS.

关键词： Compressed sensing

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