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Nonlinear shape statistics via kernel spaces 23rd

23rd German Association for pattern recognition Symposium, DAGM 2001

作者： Cremers, Daniel Kohlberger, Timo Schnörr, Christoph Computer Vision Graphics and Pattern Recognition Group Department of Mathematics and Computer Science University of Mannheim Mannheim68131 Germany

ISBN: (纸本)3540425969

We present a novel approach for representing shape knowledge in terms of example views of 3D objects. Typically, such data sets exhibit a highly nonlinear structure with distinct clusters in the shape vector space, preventing the usual encoding by linear principal component analysis (PCA). For this reason, we propose a nonlinear Mercer-kernel PCA scheme which takes into account both the projection distanceand the within-subspace distance in a high-dimensional feature space. The comparison of our approach with supervised mixture models indicates that the statistics of example views of distinct 3D objects canfairly well be learned and represented in a completely unsupervised way. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Vector spaces

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Segmentation of co-articulated basic units in isolated Bangla words using multifactorial analysis

Segmentation of co-articulated basic units in isolated Bangl...

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IEEE INDICON 2004 - 1st India Annual Conference

作者： Chattopadhyay, Tanushyam Chaudhuri, B.B. Systems TCS Kolkata Computer Vision and Pattern Recognition Unit Indian Statistical Institute Jawaharlal Nehru

ISBN: (纸本)0780389093

One of the major challenges in speech synthesis and recognition is co-articulated unit segmentation. In this paper we present a novel technique for segmenting the basic co-articulated units using multifactorial analysis based approach. The proposed algorithm is applied on isolated spoken words in Bangla. The results obtained from a considerably large database show the strength of the approach. ©2004 IEEE.

关键词： Speech synthesis

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Distance Metric Learned Collaborative Representation Classifier(DML-CRC)

IEEE Letters of the Computer Society

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IEEE Letters of the computer Society 2020年第2期3卷 34-37页

作者： Chakraborti, Tapabrata McCane, Brendan Mills, Steven Pal, Umapada Department of Computer Science University of Otago Dunedin9016 New Zealand Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India

Any generic deep machine learning algorithm is essentially a function fitting exercise, where the network tunes its weights and parameters to learn discriminatory features by minimizing some cost function. Though the network tries to learn the optimal feature space, it seldom tries to learn an optimal distance metric in the cost function, and hence misses out on an additional layer of abstraction. We present a simple effective way of achieving this by learning a generic Mahalanabis distance in a collaborative loss function in an end-to-end fashion with any standard convolutional network as the feature learner. The proposed method DML-CRC gives state-of-the-art performance on benchmark fine-grained classification datasets CUB Birds, Oxford Flowers and Oxford-IIIT Pets using the VGG-19 deep network. The method is network agnostic and can be used for other similar classification tasks. © 2018 IEEE.

关键词： Learning algorithms

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TSNet: Deep Network for Human Action recognition in Hazy Videos

TSNet: Deep Network for Human Action Recognition in Hazy Vid...

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2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

作者： Chaudhary, Sachin Murala, Subrahmanyam Computer Vision and Pattern Recognition Lab Depatment of Electrical Engineering Indian Institute of Technology Ropar India

ISBN: (纸本)9781538666500

The all-weather intelligent surveillance system is the prime challenge for computer vision researchers. The surveillance is mostly done to analyze the human activity in a particular region. Several extreme weather conditions like rain, snow, haze, fog etc. halts the surveillance process and thus decreases the reliability of the surveillance system. Here, an attempt is made to tackle one of these weather situation i.e. haze in case of surveillance. Haze distorts the quality of images and videos captured by camera. Due to poor quality, it is difficult to analyze the haze degraded video for the human activities using the existing state-of-the-art methods for human action recognition (HAR). Therefore, in this paper, a new two level saliency based end-to-end network (TSNet) for HAR in hazy videos is proposed. De-hazing approaches given in [1]-[9] have certain limitations and therefore we fine-tuned the de-hazing network given in [10] for HAR. The concept of rank pooling given in [11] is further utilized to efficiently represent the temporal saliency of the video. The transmission map information is utilized here to fix the spatial saliency in each frame. As currently, there is no dataset available for HAR in hazy video, here a new dataset of hazy video is generated from two benchmark datasets namely HMDB51 [12] and UCF101 [13] by adding synthetic haze. The existing methods for HAR proposed in [11], [14], [15] are applied and compared with the proposed method on proposed hazy-HMDB51 and hazy-UCF101. The proposed method clearly outperforms the above mentioned methods in terms of average recognition rate (ARR). © 2018 IEEE.

关键词： Monitoring

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Convex relaxations for binary image partitioning and perceptual grouping 23rd

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23rd German Association for pattern recognition Symposium, DAGM 2001

作者： Keuchel, Jens Schellewald, Christian Cremers, Daniel Schnörr, Christoph Computer Vision Graphics and Pattern Recognition Group Department of Mathematics and Computer Science University of Mannheim MannheimD-68131 Germany

ISBN: (纸本)3540425969

We consider approaches to computer vision problems which require the minimization of a global energy functional over binary variables and take into account both local similarity and spatial context. The combinatorial nature of such problems has lead to the design of various approximation algorithms in the past which often involve tuning parameters and tend to get trapped in local minima. In this context, we present a novel approach to the field of computer vision that amounts to solving a convex relaxation of the original problem without introducing any additional parameters. Numerical ground truth experiments reveal a relative error of the convex minimizer with respect to the global optimum of below 2% on the average. We apply our approach by discussing two specific problem instances related to image partitioning and perceptual grouping. Numerical experiments illustrate the quality of the approach which, in the partitioning case, compares favorably with established approaches like the ICM-algorithm. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Approximation algorithms

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On-ine variational estimation of dynamical fluid flows with physics-based spatio-temporal regularization

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28th Symposium of the German Association for pattern recognition, DAGM 2006

作者： Ruhnau, Paul Stahl, Annette Schnörr, Christoph Computer Vision Graphics and Pattern Recognition Group Department of Mathematics and Computer Science University of Mannheim D-68131 Mannheim Germany

ISBN: (纸本)3540444122

We present a variational approach to motion estimation of instationary fluid flows. Our approach extends prior work along two directions;(i) The full incompressible Navier-Stokes equation is employed in order to obtain a physically consistent regularization which does not suppress turbulent flow variations, (ii) Regularization along the time-axis is employed as well, but formulated in a receding horizon manner contrary to previous approaches to spatio-temporal regularization. This allows for a recursive on-line (non-batch) implementation of our estimation framework. Ground-truth evaluations for simulated turbulent flows demonstrate that due to imposing both physical consistency and temporal coherency, the accuracy of flow estimation compares favourably even with optical flow approaches based on higher-order div-curl regularization. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Flow of fluids

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Motion competition: Variational integration of motion segmentation and shape regularization

Motion competition: Variational integration of motion segmen...

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24th German Association for pattern recognition Symposium, DAGM 2002

作者： Cremers, Daniel Schnörr, Christoph Computer Vision Graphics and Pattern Recognition Group Department of Mathematics and Computer Science University of Mannheim MannheimD–68131 Germany

ISBN: (纸本)354044209X

We present a variational method for the segmentation of piecewise affine flow fields. Compared to other approaches to motion segmentation, we minimize a single energy functional both with respect to the affine motion models in the separate regions and with respect to the shape of the separating contour. In the manner of region competition, the evolution of the segmenting contour is driven by a force which aims at maximizing a homogeneity measure with respect to the estimated motion in the adjoining regions. We compare segmentations obtained for the models of piecewise affine motion, piecewise constant motion, and piecewise constant intensity. For objects which cannot be discriminated from the background by their appearance, the desired motion segmentation is obtained, although the corresponding segmentation based on image intensities fails. The region– based formulation facilitates convergence of the contour from its initialization over fairly large distances, and the estimated discontinuous flow field is progressively improved during the gradient descent minimization. By including in the variational method a statistical shape prior, the contour evolution is restricted to a subspace of familiar shapes, such that a robust estimation of irregularly moving shapes becomes feasible. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Flow fields

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Self-Supervised Representation Learning for Knee Injury Diagnosis From Magnetic Resonance Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1613-1623页

作者： Manna, Siladittya Bhattacharya, Saumik Pal, Umapada Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata700108 India Indian Institute of Technology Kharagpur Department of Electronics and Electrical Communication Engineering Kharagpur721302 India

In medical image analysis, the cost of acquiring high-quality data and annotation by experts is a barrier in many medical applications. Most of the techniques used are based on a supervised learning framework and require a large amount of annotated data to achieve satisfactory performance. As an alternative, in this article, we propose a self-supervised learning approach for learning the spatial anatomical representations from the frames of magnetic resonance (MR) video clips for the diagnosis of knee medical conditions. The pretext model learns meaningful context-invariant spatial representations. The downstream task in our article is a class-imbalanced multilabel classification. Different experiments show that the features learned by the pretext model provide competitive performance in the downstream task. Moreover, the efficiency and reliability of the proposed pretext model in learning representations of minority classes without applying any strategy toward imbalance in the dataset can be seen from the results. To the best of our knowledge, this work is the first of its kind in showing the effectiveness and reliability of self-supervised learning algorithms in imbalanced multilabel classification tasks on MR scans. © 2020 IEEE.

关键词： Supervised learning

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A corpus for OCR research on mathematical expressions

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International Journal on Document Analysis and recognition 2005年第4期7卷 241-259页

作者： Garain, Utpal Chaudhuri, B.B. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Calcutta-700 035 203 B.T. Road India

This paper is concerned with research on OCR (optical character recognition) of printed mathematical expressions. Construction of a representative corpus of technical and scientific documents containing expressions is discussed. A statistical investigation of the corpus is presented, and usefulness of this analysis is demonstrated in the related research problems, namely, (i) identification and segmentation of expression zones from the rest of the document, (ii) recognition of expression symbols, (iii) interpretation of expression structures, and (iv) performance evaluation of a mathematical expression recognition system. Moreover, a groundtruthing format has been proposed to facilitate automatic evaluation of expression recognition techniques. © Springer-Verlag Berlin/Heidelberg 2005.

关键词： Database Groundtruthing Mathematical expressions OCR Performance evaluation Statistical learning

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Recent advances in video based document processing: A review

Recent advances in video based document processing: A review

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10th IAPR International Workshop on Document Analysis Systems, DAS 2012

作者： Sharma, Nabin Pal, Umapada Blumenstein, Michael Griffith University QLD Australia Computer Vision and Pattern Recognition Unit Indian Statistical Unit Kolkata India

ISBN: (纸本)9780769546612

Extraction and recognition of text present in video has become a very popular research area in the last decade. Generally, text present in video frames is of different size, orientation, style, etc. with complex backgrounds, noise, low resolution and contrast. These factors make the automatic text extraction and recognition in video frames a challenging task. A large number of techniques have been proposed by various researchers in the recent past to address the problem. This paper presents a review of various state-of-the-art techniques proposed towards different stages (e.g. detection, localization, extraction, etc.) of text information processing in video frames. Looking at the growing popularity and the recent developments in the processing of text in video frames, this review imparts details of current trends and potential directions for further research activities to assist researchers. © 2012 IEEE.

关键词： Extraction

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