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

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Fuzzy based diffusion coefficient function in anisotropic diffusion for impulse noise removal 12

Fuzzy based diffusion coefficient function in anisotropic di...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Khan, Nafis Uddin Arya, K.V. Pattanaik, Manisha ABV-Indian Institute of Information Technology and Management Morena Link Road Gwalior-474015 India

ISBN: (纸本)9781450316606

This paper provides the use of rule based fuzzy scheme to define a new diffusion coefficient function in anisotropic diffusion for impulse noise removal with edge preservation. This is achieved by expressing the small, medium and large labels of second order pixel differences in fuzzy format. An aggregated output membership function of percentage of noisiness is then obtained by selecting an optimal linguistic value of second order pixel difference during inference process. The pixels have been classified as homogeneous, edge and noisy pixels based on the degrees of noisiness of the output membership functions. To achieve desired smoothing of the impulse noisy images with homogeneous background, the new diffusion coefficient function in anisotropic diffusion is redefined to vary it in accordance with the degrees of noisiness of the output membership functions. The experimental results have been compared with existing anisotropic diffusion methods as well as advanced median filtering method. It is observed through experimental results that the proposed method works satisfactorily for images having impulsive noise density upto 50%. © 2012 ACM.

关键词： Pixels

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A tweak on k-nearest neighbor decision rule

A tweak on k-nearest neighbor decision rule

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2012 International conference on image processing, computer vision, and Pattern Recognition, IPCV 2012

作者： Basu, Tanmay Murthy, C.A. Chakrabarty, Himadri Machine Intelligence Unit Indian Statistical Institute 203 B. T. Road Kolkata-700108 India Department of Computer Scinece Surendranath College University of Calcutta Kolkata - 700009 India

ISBN: (纸本)9781601322258

The k-nearest neighbor (knn) decision rule puts a point into a particular class if the class has the maximum representation among the k nearest neighbors of the point in the training set. If the difference between the number of points belonging to the two competing classes (i.e., two classes with maximum representation) among the k nearest neighbors is at least one then the point will be assigned to that class. This article presents a tweak on the knn rule to enhance the confidence of the knn voting process. This method proposes a discrimination criterion on the majority voting of knn by a predefined threshold to enhance the rigidity of the voting process. The method does not require the knowledge of neighborhood parameter k to execute knn. The empirical studies show the utility of the proposed method using some synthetic and well known benchmark data sets. It is observed from the experiments that the accuracy of the proposed method is significantly better than the traditional knn method.

关键词： Pattern recognition

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Segmentation of Bangla words in scene images 12

Segmentation of Bangla words in scene images

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Banik, Prakriti Bhattacharya, Ujjwal Parui, Swapan K. Indian Statistical Institute Kolkata 700108 India

ISBN: (纸本)9781450316606

Some studies on extraction of Bangla texts from scene images are available in the literature. Also, OCR of printed Bangla texts has been extensively studied. However, the performance of available Bangla OCR on scene texts is not acceptable. In this article, we present our recent study of segmentation of characters or their parts from Bangla texts extracted from scene images. The proposed approach detects the background and text by a combination of two algorithms: unsupervised learning algorithm K-means clustering and Otsu's threshold selection. We propose a criterion to choose an optimal K value for K-means clustering. The text segmentation is based on region growing and extraction of both headline and baseline of such texts. These two lines divide a Bangla word into three horizontal zones. The present algorithm segments characters or their parts in each individual zone. This zone-based segmentation approach helps to reduce the number of symbols to be handled by the classifier in the next stage of the OCR system. Our algorithm can also detect an image having only numerals, avoiding zone detection in that case. Extracted scene texts are often affected by artifacts and our segmentation algorithm can remove them efficiently. Our algorithm has been tested on 2460 Bangla words extracted from 260 scene images. © 2012 ACM.

关键词： Optical character recognition

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Feature prominence-based weighting scheme for video tracking 12

Feature prominence-based weighting scheme for video tracking

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Candemir, Sema Palaniappan, Kannappan Bunyak, Filiz Rao, Raghuveer M. Seetharaman, Guna Dept. of Computer Science University of Missouri Columbia MO United States Army Research Lab. Adelphi MD United States Air Force Research Lab. Rome NY United States

ISBN: (纸本)9781450316606

This paper introduces a new mechanism called Feature Prominence to combine evidence from multiple feature operators for more reliable target detection and localization during video tracking. Feature prominence is measured using the statistical p-value estimated from a non-parametric local kernel density estimate of the a posteriori feature distribution. More prominent features have lower p-values and this ordering can be used to either discard low prominence features (high p-values) or reduce their weight during the feature fusion process to produce a more reliable fused feature likelihood map for locating the target at a subsequent time during tracking. The proposed feature fusion method is embedded within a test-bed tracking system. Detection and tracking performance of feature prominence as well as three other fusion methods are evaluated using the peak-recall and distance accuracy measures. Experimental results show that feature prominence outperforms these other feature fusion methods. © 2012 ACM.

关键词： Target tracking

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Fragment-based real-time object tracking: A sparse representation approach

Fragment-based real-time object tracking: A sparse represent...

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2012 19th IEEE International conference on image processing, ICIP 2012

作者： Naresh Kumar, M.S. Parate, Priti Venkatesh Babu, R. Supercomputer Education and Research Centre Indian Institute of Science Bangalore - 560012 India

ISBN: (纸本)9781467325332

Real-time object tracking is a critical task in many computer vision applications. Achieving rapid and robust tracking while handling changes in object pose and size, varying illumination and partial occlusion, is a challenging task given the limited amount of computational resources. In this paper we propose a real-time object tracker in l1 framework addressing these issues. In the proposed approach, dictionaries containing templates of overlapping object fragments are created. The candidate fragments are sparsely represented in the dictionary fragment space by solving the l 1 regularized least squares problem. The non zero coefficients indicate the relative motion between the target and candidate fragments along with a fidelity measure. The final object motion is obtained by fusing the reliable motion information. The dictionary is updated based on the object likelihood map. The proposed tracking algorithm is tested on various challenging videos and found to outperform earlier approach. © 2012 IEEE.

关键词： Motion estimation

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Assessment of computational visual attention models on medical images 12

Assessment of computational visual attention models on medic...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Jampani, Varun Ujjwal Sivaswamy, Jayanthi Vaidya, Vivek CVIT IIIT-Hyderabad Hyderabad India Medical Image Analysis Lab. GE Global Research Bangalore India

ISBN: (纸本)9781450316606

Several computational visual saliency models have been proposed in the context of viewing natural scenes. We aim to investigate the relevance of computational saliency models in medical images in the context of abnormality detection. We report on two studies aimed at understanding the role of visual saliency in medical images. Diffuse lesions in Chest X-Ray images, which are characteristic of Pneumoconiosis and high contrast lesions such as 'Hard Exudates' in retinal images were chosen for the study. These approximately correspond to conjunctive and disjunctive targets in a visual search task. Saliency maps were computed using three popular models namely Itti-Koch [7], GBVS [3] and SR [4]. The obtained maps were evaluated against gaze maps and ground truth from medical experts. Our results show that GBVS is seen to perform the best (Mdn. ROC area = 0.77) for chest X-Ray images while SR performs the best (ROC area = 0.73) for retinal images, thus asserting that searching for conjunctive targets calls for a more local examination of an image while disjunctive targets call for a global examination. Based on the results of the above study, we propose extensions for the two best performing models. The first extension makes use of top down knowledge such as lung segmentation. This is shown to improve the performance of GBVS to some extent. In the second case the extension is by way of including multi-scale information. This is shown to significantly (by 28.76%) improve abnormality detection. The key insight from these studies is that bottom saliency continues to play a predominant role in examining medical images. © 2012 ACM.

关键词： Ophthalmology

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Objective evaluation of noisy multimodal medical image fusion using Daubechies complex wavelet transform 12

Objective evaluation of noisy multimodal medical image fusio...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Singh, Rajiv Khare, Ashish Department of Electronics and Communication University of Allahabad Allahabad India

ISBN: (纸本)9781450316606

Medical image fusion needs proper attention as images obtained from medical instruments are of poor contrast and corrupted by blur and noise due to imperfection of image capturing devices. Thus, objective evaluation of medical image fusion techniques has become an important task in noisy domain. Therefore, in the present work, we have proposed maximum selection and energy based fusion rules for the evaluation of noisy multimodal medical image fusion using Daubechies complex wavelet transform (DCxWT). Unlike, traditional real valued wavelet transforms, which suffered from shift sensitivity and did not provide any phase information, DCxWT is shift invariant and provides phase information through its imaginary coefficients. Shift invariance and availability of phase information properties of DCxWT have been found useful for fusion of multimodal medical images. The experiments have been performed over several set of noisy medical images at multiple levels of noise for the proposed fusion scheme. Further, the proposed fusion scheme has been tested up to the maximum level of Gaussian, salt & pepper and speckle noise. Objective evaluation of the proposed fusion scheme is performed with fusion factor, fusion symmetry, entropy, standard deviation and edge information metrics. Results have been shown for two sets of multimodal medical images for the proposed method with maximum and energy based fusion rules, and comparison has been done with Lifting wavelet transform (LWT) and Stationary wavelet transform (SWT) based fusion methods. Comparative analysis of the proposed method with LWT and SWT based fusion methods at different noise levels shows the superiority of the proposed scheme. Moreover, the plots of different fusion metrics against the maximum level of Gaussian, salt & pepper and speckle noise show the robustness of the proposed fusion method against noise. © 2012 ACM.

关键词： image fusion

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Feature selection for classification of remote sensed hyperspectral images: A filter approach using genetic algorithm and cluster validity

Feature selection for classification of remote sensed hypers...

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2012 International conference on image processing, computer vision, and Pattern Recognition, IPCV 2012

作者： Santos, A.B. Celes, C.S.F.D.S. Araújo, A.D.A. Menotti, D. Computer Science Department UFMG - Federal University of Minas Gerais Belo Horizonte MG Brazil Computing Department UFOP - Federal University of Ouro Preto Ouro Preto MG Brazil

ISBN: (纸本)9781601322258

In this paper, we investigate the advantages of using feature selection approaches for classification of remote sensed hyperspectral images. We propose a new filter feature selection approach based on genetic algorithms (GA) and cluster validity measures for finding the best subset of features that maximizes inter-cluster and minimizes intracluster distances, respectivelly. Thus, using the optimal, or sub-optimal, subset of features, classifiers can build decision boundaries in an accurate way. Dunn's index metric, given a subset of features, is used to estimate how good the built clusters are. Experiments were carried out with two wellknown datasets: AVIRIS - indian Pines and ROSIS - Pavia University. Three different classifiers were used to evaluate our proposal: Support Vector Machines (SVM), Multi-layer Perception Neural Networks (MLP) and K-Nearest Neighbor (KNN). Moreover, we compare the performance of our proposal in terms of accuracies to other ones: the traditional Pixelwise, without feature selection/extraction, and the widely used Singular Value Decomposition Band Subset Selection (SVDSS). Experiments show that the classification methods using our feature selection approach produce a small subset of features which easily achieve enough discriminative power and their results are similars to the ones using SVDSS.

关键词： Feature Selection

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Advanced Intelligent Computing Theories and Applications 2012

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丛书名： Lecture Notes in computer Science

2012年

作者： De-Shuang Huang Yong Gan Phalguni Gupta M. Michael Gromiha

ISBN: (数字)9783642259449

ISBN: (纸本)9783642259432

This book constitutes the thoroughly refereed post-conference proceedings of the 7th International conference on Intelligent Computing, ICIC 2011, held in Zhengzhou, China, in August 2011. The 94 revised full papers presented were carefully reviewed and selected from 832 submissions. The papers are organized in topical sections on intelligent computing in scheduling; local feature descriptors for image processing and recognition; combinatorial and numerical optimization; machine learning theory and methods; intelligent control and automation; knowledge representation/reasoning and expert systems; intelligent computing in pattern recognition; intelligent computing in image processing; intelligent computing in computer vision; biometrics with applications to individual security/forensic sciences; modeling, theory, and applications of positive systems; sparse manifold learning methods and applications; advances in intelligent information processing.

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Intelligent Computing Theories and Applications 2012

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丛书名： Lecture Notes in computer Science

2012年

作者： De-Shuang Huang Jianhua Ma Kang-Hyun Jo M. Michael Gromiha

ISBN: (数字)9783642315763

ISBN: (纸本)9783642315756

This book constitutes the refereed proceedings of the 8th International conference on Intelligent Computing, ICIC 2012, held in Huangshan, China, in July 2012. The 85 revised full papers presented were carefully reviewed and selected from 753 submissions. The papers are organized in topical sections on neural networks, evolutionar learning and genetic algorithms, granular computing and rough sets, biology inspired computing and optimization, nature inspired computing and optimization, cognitive science and computational neuroscience, knowledge discovery and data mining, quantum computing, machine learning theory and methods, healthcare informatics theory and methods, biomedical informatics theory and methods, complex systems theory and methods, intelligent computing in signal processing, intelligent computing in image processing, intelligent computing in robotics, intelligent computing in computer vision, intelligent agent and web applications, special session on advances in information security 2012.

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