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检索条件"任意字段=15th Indian Conference on Computer Vision Graphics and Image Processing"

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Total cluster: A person agnostic clustering method for broadcast videos 14

Total cluster: A person agnostic clustering method for broad...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Tapaswi, Makarand Parkhi, Omkar M. Rahtu, Esa Sommerlade, Eric Stiefelhagen, Rainer Zisserman, Andrew Computer Vision for Human Computer Interaction Karlsruhe Institute of Technology Germany Visual Geometry Group Department of Engineering Science University of Oxford United Kingdom Center for Machine Vision Research University of Oulu Finland

ISBN: (纸本)1595930361

the goal of this paper is unsupervised face clustering in edited video material - where face tracks arising from different people are assigned to separate clusters, with one cluster for each person. In particular we explore the extent to which faces can be clustered automatically without making an error. this is a very challenging problem given the variation in pose, lighting and expressions that can occur, and the similarities between different people. the novelty we bring is three fold: first, we show that a form of weak supervision is available from the editing structure of the material - the shots, threads and scenes that are standard in edited video;second, we show that by first clustering within scenes the number of face tracks can be significantly reduced with almost no errors;third, we propose an extension of the clustering method to entire episodes using exemplar SVMs based on the negative training data automatically harvested from the editing structure. the method is demonstrated on multiple episodes from two very different TV series, Scrubs and Buffy. For both series it is shown that we move towards our goal, and also outperform a number of baselines from previous works. Copyright is held by the authors.

关键词： Cluster analysis

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Particle coding for meshfree cutting of deformable assets 14

Particle coding for meshfree cutting of deformable assets

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Shrivastava, Prateek Das, Sukhendu VPLab. CSE IIT Madras India

ISBN: (纸本)1595930361

In this paper we propose a novel algorithm for cutting deformable (soft) assets, modeled using the meshfree method of Smoothed Particle Hydrodynamics (SPH). the key idea of the algorithm is to label all particles during the virtual cut operation to obtain particle codes. Since the traditional SPH formulations ignore particle separation due to cuts, we had to account for the change in topology due to virtual cuts in the SPH formulation. Virtual cut causes disruption in internal forces between separated particles. To avoid separated particles (i.e., particles belonging to different regions) affecting each other's dynamics, the generated particle codes are used for filtering the neighbours before SPH dynamics are computed. Assignment of particle codes eliminates the need for an external grid like structure for detecting newly generated cut surfaces. We exploit the region information extracted from the particle codes to obtain a color field for surface generation. We show that our algorithm can easily be integrated with existing SPH techniques. Our proposed method generates minimum overhead in scenarios where multiple cutting operations are performed on a deformable asset. Particle coding is applied across all particles in parallel and is hence computationally efficient when implemented on a GPU. Copyright 2014 ACM.

关键词： graphics processing unit

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Blind 3D video watermarking based on 3D-HEVC encoder using depth 14

Blind 3D video watermarking based on 3D-HEVC encoder using d...

引用

9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Rana, Shuvendu Sur, Arijit Department of Computer Science and Engineering Indian Institute of Technology Guwahati Assam781039 India

ISBN: (纸本)1595930361

Due to enormous advancement of internet technology and display devices, 3D video becomes popular in recent times. To ensure secure media transmission, efficient authentication scheme for such 3D video sequence is a requirement. In recent past, watermarking is being regarded as a popular DRM tool for video authentication. It has been observed that video watermarking becomes a challenging task in the presence of advanced auto-stereoscopic display devices and MVD (Multi-view Video plus Depth) based encoding technique in case of 3D video. In this paper, depth image based rendering technique is proposed for blind 3D video watermarking. In this scheme, rendering technique is used to find the Z-axis of the stereo videos (left and right video). the connected regions of the Z-axis of a Group of Picture (GOP) have been filtered using the motion prediction of the video. Block DCT coefficients are used to embed the watermark signal with the selected Z-axis regions of the each video (left and right separately). A comprehensive set of experiments have been done to justify the robustness of the proposed scheme over existing schemes with respect to compression of the 3D-HEVC video codec. Copyright 2014 ACM.

关键词： Watermarking

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Techniques for improving the performance of image retrieval using relevance feedback 14

Techniques for improving the performance of image retrieval ...

引用

9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Nayak, Shrikant Sekhar, C. Chandra Department of Computer Science and Engineering Indian Institute of Technology Madras India

ISBN: (纸本)1595930361

Relevance feedback is important in bridging the semantic gap between the low-level visual features and the high-level semantic concepts during the image retrieval. In this work, the image retrieval using relevance feedback involves four phases. In the first phase, an initial retrieval is carried out for a given query image and each of the retrieved images is rated as relevant or irrelevant. In the second phase, the rated images are clustered. In the third phase, a score is computed for each image in the repository to measure the degree of its relevance to the query using the images in the clusters. We propose a method to compute the score using the local relevance feedback and the global relevance feedback. the local relevance feedback based component of the score is computed using the instance-based feedback approach. In this approach, the degree of relevance is measured using the feedback from the current iteration only. the global relevance feedback based component of the score is computed using the query-point movement approach. In this approach, the degree of relevance is measured using the feedback from different iterations. the scores for the images in the repository are used to retrieve the images. Each of the newly retrieved images is rated as relevant or irrelevant. In the fourth phase, each of the newly rated images is assigned to an existing cluster of rated images. We propose to represent an irregularly shaped cluster using multiple representatives. these representatives are used to assign each of the newly rated images to a cluster. the third and fourth phases are repeated until there is a convergence of the process of retrieval. the image retrieval performance for the proposed methods is compared with that of the existing methods on Wang and Corel image datasets. Results of these studies demonstrate the effectiveness of the proposed methods in improving the retrieval performance. Copyright 2014 ACM.

关键词： image retrieval

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Knowledge sharing and cooperation based adaptive boosting for robust eye detection 14

Knowledge sharing and cooperation based adaptive boosting fo...

引用

9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Lahiri, Avisek Biswas, Prabir Indian Institute of Technology Kharagpur West721302 India

ISBN: (纸本)1595930361

this paper proposes knowledge sharing and cooperation based Adaptive Boosting (KSC-AdaBoost) for supervised collaborative learning in presence of two different feature spaces (views) representing a training example. In such a binary learner space, two learner agents are trained on the two feature spaces. Difficulty of a training example is ascertained not only by classification performance of an individual learner but also by overall group performance on that training example. Group learning is enhanced by a novel algorithm for assigning weight to training set data. three different models of KSC-AdaBoost are proposed for agglomerating decisions of the two learners. KSC-AdaBoost out-performs traditional AdaBoost and some recent variants of AdaBoost in terms of convergence rate of training set error and generalization accuracy. the paper then presents KSC-AdaBoost based hierarchical model for accurate eye region localization followed by fuzzy rule based system for robust eye center detection. Exhaustive experiments on five publicly available popular datasets reveal the viability of the learning models and superior eye detection accuracy over recent state-of-the-art algorithms. Copyright 2014 ACM.

关键词： Fuzzy rules

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Domain adaptation on correlated subspace for unseen multiview headpose classification 14

Domain adaptation on correlated subspace for unseen multivie...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Rajagopal, Anoop K. Ramakrishnan, K.R. Dept. of Electrical Engineering Indian Institute of Science Bengaluru India

ISBN: (纸本)1595930361

Head pose classification from images acquired from far field cameras is a challenging problem because of the low resolution, blur and noise due to subject movements. Further there exists a domain shift in head pose classification between training (source) and testing (target) images. Also more often the head poses in the target set may not exist in the source set and acquiring sufficient samples for training is quite expensive. In this paper, we propose a novel framework to address multi-view unseen head pose classification where the target set belongs to a different domain and has more classes than in the source. A correlated subspace is first derived using Canonical Correlation Analysis (CCA) between corresponding head poses in the source (stationary subjects) and target set (moving subjects). A distance based Domain Adaptation technique is then used in the correlation subspace for classification of unseen head pose in the target set. Experimental results confirm the effectiveness of our approach in improving the classification performance over the state-of-art. Copyright is held by the authors.

关键词： CCA

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Face recognition using entropy based face segregation as a pre-processing technique and conservative BPSO based feature selection 14

Face recognition using entropy based face segregation as a p...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Babu, S. Hitesh Birajdhar, Sachin A. Tambad, Samarth Dept. of Electronics and Communication Engg. M.S. Ramaiah Inst. of Tech. Bangalore560054 India

ISBN: (纸本)1595930361

the statistical appearance of the face can vary due to various factors such as pose, occlusion, expression and back-ground which makes it a challenging task to have an efficient Face Recognition (FR) system. this paper proposes 4 novel techniques viz., Entropy based Face Segregation (EFS) as pre-processing technique, Double Wavelet Noise Removal (DWNR) as pre-processing technique, 1D Stationary Wavelet Transform (SWT) as Feature Extractor and Conservative Binary Particle Optimization (CBPSO) as Feature Selector to enhance the performance of the system. EFS is used to segregate the facial region, thus removing the cluttered background. DWNR has unique combination of 2D Discrete Wavelet Transform (DWT), Wiener Filter and 2D SWT for image denoising and contrast enhancement. the pre-processed image is then fed to unique combination of 1D DWT, 1D SWT and 1D Discrete Cosine Transform (DCT) to extract essential features. CBPSO is used to select very optimum feature subset and significantly reduce the computation time. the proposed algorithm is experimented on four benchmark databases viz., Color FERET, CMU PIE, Pointing Head Pose and Georgia Tech. Copyright 2014 ACM.

关键词： Face recognition

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Table extraction from document images using fixed point model 14

Table extraction from document images using fixed point mode...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Bansal, Anukriti Harit, Gaurav Dutta Roy, Sumantra IIT Delhi India IIT Jodhpur India

ISBN: (纸本)1595930361

the paper presents a novel learning-based framework to identify tables from scanned document images. the approach is designed as a structured labeling problem, which learns the layout of the document and labels its various entities as table header, table trailer, table cell and non-table region. We develop features which encode the foreground block characteristics and the contextual information. these features are provided to a fixed point model which learns the inter-relationship between the blocks. the fixed point model attains a contraction mapping and provides a unique label to each block. We compare the results with Condition Random Fields(CRFs). Unlike CRFs, the fixed point model captures the context information in terms of the neighbourhood layout more efficiently. Experiments on the images picked from UW-III (University of Washington) dataset, UNLV dataset and our dataset consisting of document images with multi-column page layout, show the applicability of our algorithm in layout analysis and table detection. Copyright 2014 ACM.

关键词： image analysis

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Revealing what to extract from where, for object-centric content based image retrieval (CBIR) 14

Revealing what to extract from where, for object-centric con...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Gupta, Nitin Das, Sukhendu Chakraborti, Sutanu Dept. of CS and E IIT Madras India

ISBN: (纸本)1595930361

Content Based image Retrieval (CBIR) techniques retrieve similar digital images from a large database. As the user often does not provide any clue (indication) of the region of interest in a query image, most methods of CBIR rely on a representation of the global content of the image. the desired content in an image is often localized (e.g. car appearing salient in a street) instead of being holistic, demanding the need for an object-centric CBIR. We propose a biologically inspired framework WOW ("What"Object is "Where") for this purpose. Design of WOW framework is motivated by the cognitive model of human visual perception and feature integration theory (FIT). the key contributions in the proposed approach are: (i) Feedback mechanism between Recognition ("What") and Localization ("Where") modules (both supervised), for a cohesive decision based on mutual consensus;(ii) Hierarchy of visual features (based on FIT) for an efficient recognition task. Integration of information from the two channels ("What" and "Where") in an iterative feedback mechanism, helps to filter erroneous contents in the outputs of individual modules. Finally, using a similarity criteria based on HOG features (spatially localized by WOW) for matching, our system effectively retrieves a set of rank-ordered samples from the gallery. Experimentation done on various real-life datasets (including PASCAL) exhibits the superior performance of the proposed method. Copyright 2014 ACM.

关键词： Iterative methods

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Super pixel clustering via Kernel Density Estimation 14

Super pixel clustering via Kernel Density Estimation

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Sheth, Chintak Venkatesh Babu, R. BITS Pilani K. K. Birla Goa Campus Bypass Road National Highway 17B Zuarinagar Goa India Supercomputer Education and Research Centre Indian Institute of Science Bangalore560012 India

ISBN: (纸本)1595930361

Super pixels, which are a result of over-segmentation provide a reasonable compromise between working at pixel level versus with few optimally segmented regions. One fundamental challenge is that of defining the search space for merging. A naive approach of performing iterative clustering on the local neighborhood would be prone to under segmentation. In this paper, we develop a framework for generating non-compact super pixels by performing clustering on compact super pixels. We define the optimal search space by generating both over-segmented and under-segmented clustering of compact super pixels. Using this spatial information of the under-segmented scale, we look to improve the over-segmented scale. Our work is based on performing Kernel Density Estimation in 1D and further refining it using angular quantization. In all we propose three angular quantization formulations to generate the three scales of segmentation. Our results and comparison with the state-of-the-art super pixel algorithms show that merging a large number of super pixels with our algorithm is able to provide better results than using the underlying super pixel algorithm to obtain a smaller number of super pixels. Copyright 2014 ACM.

关键词： image segmentation

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