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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Deopujari, Kalyani Velmurugan, Rajbabu Tiwari, Kanchan MES Coll Engn Pune Maharashtra India Indian Inst Technol Bombay Maharashtra India

ISBN: (纸本)9781450347532

this paper proposes a simple method to handle illumination variation in a video. the proposed method is based on generative mean shift tracker, which uses energy compaction property of discrete Cosine transform (DCT) to handle illumination variation within and across frames. the proposed method uses spatial and temporal DCT coefficient based approach to assign weights to target and candidate histograms in mean shift. the proposed weighing factor takes care of changes in illumination within a frame i.e., illumination change of the target with respect to background and also across the frames i.e., varying illumination between the consecutive time instances. the algorithm was tested using VOT2015 challenge dataset and also on sequences from OTB and CAVIAR datasets. the proposed method was also tested rigorously for illumination attribute. the qualitative and quantitative evaluation process of the proposed method was twofold. First, the tracker was compared with existing DCT coefficient based method and showed improved results. Secondly, the proposed algorithm was compared with other state of the art trackers. the results show that the proposed algorithm outperformed some state-of-the-art trackers while with others it showed comparable performance.

关键词： Mean Shift Discrete Cosine Transform DC Coefficient Illumination Variation

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Kumar, Sunil Bhuyan, M. K. Chakraborty, Biplab Ketan Indian Inst Technol Guwahati Gauhati 781039 India

ISBN: (纸本)9781450347532

Recently several multi-view learning-based methods have been proposed, and they are found to be more efficient in many real world applications. However, existing multi-view learning-based methods are not suitable for finding discriminative directions if the data is multi-modal. In such cases, Locality Preserving Projection (LPP) and/or Local Fisher Discriminant Analysis (LFDA) are found to be more appropriate to capture discriminative directions. Furthermore, existing methods show that imposing uncorrelated constraint onto the common space improves classification accuracy of the system. Hence inspired from the above findings, we propose an Uncorrelated Multi-view Discriminant Locality Preserving Projection (UMvDLPP)-based approach. the proposed method searches a common uncorrelated discriminative space for multiple observable spaces. Moreover, the proposed method can also handle the multi-modal characteristic, which is inherently embedded in multi-view facial expression recognition (FER) data. Hence, the proposed method is effectively more efficient for multi-view FER problem. Experimental results show that the proposed method outperforms state-of-the-art multi-view learning-based methods.

关键词： Multi-view learning facial expression recognition multi-view discriminant analysis locality preserving projection and uncorrelated discriminant analysis

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Local ternary patterns and maximum bipartite matching for face recognition

Local ternary patterns and maximum bipartite matching for fa...

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2011 3rd National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2011

作者： Patnaik, Renuka Gupta, Raj Mittal, Anurag Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai 600036 India

ISBN: (纸本)9780769545998

Local Binary Pattern (LBP) has been the successful feature descriptor used for face recognition. the basic idea in this method is to convert from an intensity space to an order space where the order of neighboring pixels is used to create a monotonic change illumination-invariant code for each point in the image. A drawback for this method, however, is that, in homogenous regions, the order of the pixel with respect to its neighbors is quite noisy. In this paper, we propose to use a third value which indicates if two pixels are similar in value. We also propose to match these patterns using maximum bipartite matching rather than histogram matching. Significant performance boost was found when compared to LBP and other standard methods like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). We present our results on Extended Yale and FERET datasets. © 2011 IEEE.

关键词： Local binary pattern

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Activity Recognition in Egocentric Videos Using Bag of Key Action Units 11

Activity Recognition in Egocentric Videos Using Bag of Key A...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Suma, K. Sai Aditya, G. Mukherjee, Snehasis Indian Inst Informat Technol Sricity Chittoor Andhra Pradesh India

ISBN: (纸本)9781450366151

In this paper we present a novel methodology for recognizing human activity in Egocentric video based on the Bag of Visual Features. the proposed technique is based on the assumption that, only a portion of the whole video can be sufficient to identify an activity. Rather, we argue that, for activity recognition in egocentric videos, the proposed approach performs better than any deep learning based method. Because, in egocentric videos, often the person wiring the sensor, becomes static for long time, or moves his head frequently. In both the cases, it becomes difficult to learn the spatio-temporal pattern of the video during action. the proposed approach divides the video into smaller video segments called Video Units. Spatio-temporal features extracted from the units, are clustered to construct the dictionary of Action Units (AU). the AUs are ranked based upon their score of likeliness. the scores are obtained by constructing a weighted graph with the AUs as vertices and edge weights calculated based on the frequencies of occurrences of the AUs during the activity. the less significant AUs are pruned out from the dictionary, and the revised dictionary of key AUs are used for activity *** test our approach on benchmark egocentric dataset and achieve a good accuracy.

关键词： Deep learning

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image retrieval based on indexing and relevance feedback

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PATTERN RECOGNITION LETTERS 2007年第3期28卷 357-366页

作者： Saha, Sanjoy K. Das, Amit K. Chanda, Bhabatosh Jadavpur Univ CSE Dept Kolkata 700032 W Bengal India BE Coll DU CST Dept Howrah 711103 India Indian Stat Inst ECS Unit Kolkata 700035 W Bengal India

In content based image retrieval (CBIR) system, search engine retrieves the images similar to the query image according to a similarity measure. It should be fast enough and must have a high precision of retrieval. Indexing scheme is used to achieve a fast response and relevance feedback helps in improving the retrieval precision. In this paper, a human perception based similarity measure is presented and based on it a simple yet novel indexing scheme with relevance feedback is discussed. the indexing scheme is designed based on the primary and secondary keys which are selected by analysing the entropy of features. A relevance feedback method is proposed based on Mann-Whitney test. the test is used to identify the discriminating features from the relevant and irrelevant images in a retrieved set. then emphasis of the discriminating features are updated to improve the retrieval performance. the relevance feedback scheme is implemented for two different similarity measure (Euclidean distance based and human perception based). the experiment justifies the effectiveness of the proposed methodologies. Finally, the indexing scheme and relevance feedback mechanism are combined to build up the search engine. (c) 2006 Elsevier B.V. All rights reserved.

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Deep Automatic Licence Plate Recognition system 10

Deep Automatic Licence Plate Recognition system

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Jain, Vishal Sasindran, Zitha Rajagopal, Anoop Biswas, Soma Bharadwaj, Harish S. Ramakrishnan, K. R. Indian Inst Sci Bengaluru Karnataka India Myntra Designs Bengaluru Karnataka India

ISBN: (纸本)9781450347532

Automatic License Plate Recognition (ALPR.) has important applications in traffic surveillance. It is a challenging problem especially in countries like in India where the license plates have varying sizes, number of lines, fonts etc. the difficulty is all the more accentuated in traffic videos as the cameras are placed high and most plates appear skewed. this work aims to address ALPR. using Deep CNN methods for real-time traffic videos. We first extract license plate candidates from each frame using edge information and geometrical properties, ensuring high recall. these proposals are fed to a CNN classifier for License Plate detection obtaining high precision. We then use a CNN classifier trained for individual characters along with a spatial transformer network (STN) for character recognition. Our system is evaluated on several traffic videos with vehicles having different license plate formats in terms of tilt, distances, colors, illumination, character size, thickness etc. Results demonstrate robustness to such variations and impressive performance in both the localization and recognition. We also make available the dataset for further research on this topic.

关键词： Convolution Neural Network Spatial Transformer Network image Transformation Pursuit Automatic License Plate Recognition

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DP-GAN: Dual Pathway Generative Adversarial Network for Face Recognition in Degraded Scenarios 11

DP-GAN: Dual Pathway Generative Adversarial Network for Face...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Bhattacharjee, Avishek Banerjee, Samik Das, Sukhendu Indian Inst Technol Madras Chennai 600036 Tamil Nadu India

ISBN: (纸本)9781450366151

Face Recognition (FR) using Convolutional Neural Network (CNN) based models have achieved considerable success in constrained environments. they however fail to perform well in unconstrained scenarios, especially when the images are captured using surveillance cameras. these probe samples suffer from degradations such as noise, poor illumination, low resolution, blur as well as aliasing, when compared to the rich training (gallery) set, comprising mostly of mugshot images captured in laboratory settings. these images in the training (gallery) set are crisp and have high contrast, compared to the probe samples. To cope with this scenario, we propose a novel dual-pathway generative adversarial network (DP-GAN) which maps low resolution images captured using surveillance camera into their corresponding high resolution images, which are gallery-like, using a novel combination of multi-scale reconstruction and Jensen-Shannon divergence based loss. these images thus obtained are then used to train a deep domain adaptation (deep-DA) network to perform the task of FR. the proposed network achieves superior results (>90%) on four benchmark surveillance face datasets, evident from the rank-1 recognition rates when compared with recent state-of-the-art CNN-based techniques.

关键词： Face recognition

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Robust Registration of Mouse Brain Slices with Severe Histological Artifacts 16

Robust Registration of Mouse Brain Slices with Severe Histol...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Agarwal, Nitin Xu, Xiangmin Gopi, M. Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA Univ Calif Irvine Dept Anat Neurobiol Irvine CA USA

ISBN: (纸本)9781450347532

Brain mapping research is facilitated by first aligning digital images of mouse brain slices to standardized atlas framework such as the Allen Reference Atlas (ARA). However, conventional processing of these brain slices introduces many histological artifacts such as tears and missing regions in the tissue, which make the automatic alignment process extremely challenging. We present an end-to-end fully automatic registration pipeline for alignment of digital images of mouse brain slices that may have histological artifacts, to a standardized atlas space. We use a geometric approach where we first align the bounding box of convex hulls of brain slice contours and atlas template contours, which are extracted using a variant of Canny edge detector. We then detect the artifacts using Constrained Delaunay Triangulation (CDT) and remove them from the contours before performing global alignment of points using iterative closest point (ICP). this is followed by a final non-linear registration by solving the Laplace's equation with Dirichlet boundary conditions. We tested our algorithm on 200 mouse brain slice images including slices acquired from conventional processing techniques having major histological artifacts, and from serial two-photon tomography (STPT) with no major artifacts. We show significant improvement over other registration techniques, both qualitatively and quantitatively, in all slices especially on slices with significant histological artifacts.

关键词： Non-Linear Registration image Artifacts Histology Mouse Brain Allen Reference Atlas Point Correspondences

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Subjective performance of texture based algorithm for face verification: the role of databases

Subjective performance of texture based algorithm for face v...

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6th indian conference on computer vision, graphics and image processing, ICVGIP 2008

作者： Raghavendra, R. Rao, Ashok Kumar.G, Hemantha Department of studies in Computer Science University of Mysore Mysore-570 006 India Dept of Computer Science J.S.S. College of Arts Commerce and Science Mysore-570 025 India

ISBN: (纸本)9780769534763

this paper address new face verification scheme based on Log-Gabor filter (texture based) and Gaussian Mixture Model. the proposed method consists of three parts. the first part is a Log-Gabor filtering on facial image. the second part is to model the Log-Gabor filter response using Gaussian Mixture Model to obtain more than one set of features. the third part is transforming the set of features using subspace methods such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA). thus, in this paper two methods namely Log-Gabor Mixture Model (LGMM) based on PCA and Log-Gabor Mixture Model based (LGMM) on ICA is proposed. Proposed methods are evaluated for its performance by conducting series of experiments on three image databases: ORL, AR, YALE face database. the nature of type & size of databases chosen on the performance of these algorithms are also studied. the experimental results indicate the efficacy of the proposed methods and varied nature of results based on these algorithms. © 2008 IEEE.

关键词： Principal component analysis

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Sparse Signal Recovery for Multiple Measurement Vectors with Temporally Correlated Entries: A Bayesian Perspective 11

Sparse Signal Recovery for Multiple Measurement Vectors with...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Sharma, Shruti Chaudhury, Santanu Jayadeva, Jayadeva Bhagat, Snigdha Indian Inst Technol Delhi New Delhi India

ISBN: (纸本)9781450366151

Bayesian Sparse Signal Recovery (SSR) for Multiple Measurement Vectors, when elements of each row of solution matrix are correlated, is addressed in the paper. We propose a standard linear Gaussian observation model and a three-level hierarchical estimation framework, based on Gaussian Scale Mixture (GSM) model with some random and deterministic parameters, to model each row of the unknown solution matrix. this hierarchical model induces heavy-tailed marginal distribution over each row which encompasses several choices of distributions viz. Laplace distribution, Student's t distribution and Jeffery prior. Automatic Relevance Determination (ARD) phenomenon introduces sparsity in the model. It is interesting to see that Block Sparse Bayesian Learning framework is a special case of the proposed framework when induced marginal is Jeffrey prior. Experimental results for synthetic signals are provided to demonstrate its effectiveness. We also explore the possibility of using Multiple Measurement Vectors to model Dynamic Hand Posture Database which consists of sequence of temporally correlated hand posture sequence. It can be seen that by exploiting temporal correlation information present in the successive image samples, the proposed framework can reconstruct the data with less linear random measurements with high fidelity.

关键词： Matrix algebra

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