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3D face recognition using kinect 14

3D face recognition using kinect

引用

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

作者： Ajmera, Rahul Nigam, Aditya Gupta, Phalguni Department of Computer Science and Engineering Indian Institute of Technology Kanpur India School of Computing and Electrical Engineering Indian Institute of Technology Mandi India

ISBN: (纸本)1595930361

2D Face recognition systems bound to fail on images with varying pose angles and occlusions. Many pose invariant methods are proposed in recent years but they are still not able to achieve very good accuracies. So in order to achieve a better accuracy we need to extend algorithms over 3D faces. Due to the high cost involved in acquisition of 3D faces we developed our approach for low-cost and low-quality Microsoft Kinect Sensor and propose an algorithm to produce better results than existing 2D Face recognition techniques even after compromising on the quality of the images from the sensor. Our proposed algorithm is based on modified SURF descriptors on RGB images combined with various enhancements on automatically generated training images using Depth and Color images. We compare our results obtained with State Of the Art Techniques obtained on publicly available RGB-D Face databases. Our System obtained recognition rate of 98.07% on 30° CurtinFace Database, 89.28% on EURECOM Database, 98.00% on 15° Internal Database and 81.00% on 30° Internal Database.

关键词： Face recognition

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Domain adaptation on correlated subspace for unseen multiview headpose classification 14

Domain adaptation on correlated subspace for unseen multivie...

引用

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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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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Parametric dictionaries and feature augmentation for continuous domain adaptation 14

Parametric dictionaries and feature augmentation for continu...

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

作者： Shekhar, Sumit Shroff, Nitesh Chellappa, Rama Adobe Research Bangalore India Light Paolo Alto United States University of Maryland College Park United States

ISBN: (纸本)1595930361

In this paper, we study methods for learning classifiers for the case when there is a variation introduced by an underlying continuous parameter θ representing transformations like blur, pose, time, etc. First, we consider the task of learning dictionary-based representation for such cases. Sparse representations driven by data-derived dictionaries have produced state-of-the-art results in various image restoration and classification tasks. While significant advances have been made in this direction, most techniques have focused on learning a single dictionary to represent all variations in the data. In this paper, we show that dictionary learning can be significantly improved by explicitly parameterizing the dictionaries for θ. We develop an optimization framework to learn parametric dictionaries that vary smoothly with θ. We propose two optimization approaches, (a) least squares approach, and (b) the regularized K-SVD approach. Furthermore, we analyze the variations in data induced by θ from a different yet related perspective of feature augmentation. Specifically, we extend the feature augmentation technique proposed for adaptation of discretely separable domains to continuously varying domains, and propose a Mercer kernel to account for such changes. We present experimental validation of the proposed techniques using both synthetic and real datasets. Copyright 2014 ACM.

关键词： image reconstruction

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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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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...

引用

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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Crowd abnormality detection and localization - A matrix decomposition approach 14

Crowd abnormality detection and localization - A matrix deco...

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

作者： Gupta, Vikas Biswas, Soma Department of Electrical Engineering Indian Institute of Science Bangalore India

ISBN: (纸本)1595930361

Abnormality detection in crowded scenes plays a very important role in automatic monitoring of surveillance feeds. Here we present a novel framework for abnormality detection in crowd videos. the key idea of the approach is that rarely or sparsely occurring events correspond to abnormal activities while the commonly occurring events correspond to the normal activities. Given an input video, multiple feature matrices are computed which are decomposed into their low-rank and sparse components, out of which the sparse components correspond to the abnormal activities. the approach does not require any explicit modeling of crowd behavior or training. Localization of the anomalies is obtained as a by-product of the proposed approach by doing an inverse mapping between the entries of the matrix and the pixels in the video frames. the method is very general and can be applied for both sparsely crowded as well as densely crowded scenes and it can be used to detect both global and local abnormalities. Experimental evaluation on two widely used datasets as well as some dense crowd videos downloaded from the web shows the effectiveness of the proposed approach. Comparison with several state-of-the-art crowd abnormality detection approaches show that the proposed method compares well as compared to the other approaches. Copyright is held by the authors.

关键词： Matrix algebra

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Super pixel clustering via Kernel Density Estimation 14

Super pixel clustering via Kernel Density Estimation

引用

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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Multi-criteria energy minimization with boundedness, edge-density and rarity, for object saliency in natural images 14

Multi-criteria energy minimization with boundedness, edge-de...

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

作者： Roy, Sudeshna Das, Sukhendu Indian Institute Technology Madras India

Recent methods of bottom-up salient object detection have attempted to either: (i) obtain a probability map with a 9;contrast rarity9; based functional, formed using low level cues;or (ii) Minimize an objective ... 详细信息

ISBN: (纸本)1595930361

Recent methods of bottom-up salient object detection have attempted to either: (i) obtain a probability map with a 'contrast rarity' based functional, formed using low level cues;or (ii) Minimize an objective function, to detect the object. Most of these methods fail for complex, natural scenes, such as the PASCAL-VOC challenge dataset which contains images with diverse appearances, illumination conditions, multiple distracting objects and varying scene environments. We thus formulate a novel multi-criteria objective function which captures many dependencies and the scene structure for correct spatial propagation of low-level priors to perform salient object segmentation, in such cases. Our proposed formulation is based on CRF modeling where the minimization is performed using graph cut and the optimal parameters of the objective function are learned using a max-margin framework from the training set, without the use of class labels. Hence the method proposed is unsu-pervised, and works efficiently when compared to the very recent state-of-the art methods of saliency map detection and object proposals. Results, compared using F-measure and intersection-over-union scores, show that the proposed method exhibits superior performance in case of the complex PASCAL-VOC 2012 object segmentation dataset as well as the traditional MSRA-B saliency dataset. Copyright 2014 ACM.

关键词： Object detection

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

Techniques for improving the performance of image retrieval ...

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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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