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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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Depth estimation using Stereo Light Field Camera✱ 22

Depth estimation using Stereo Light Field Camera✱

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proceedings of the Thirteenth indian conference on computer vision, graphics and image processing

作者： Suresh Nehra Jayanta Laha J. Mukhopadhyay P. K. Biswas Indian Institute of Technology Kharagpur India Indian Space Research Organisation India

ISBN: (纸本)9781450398220

Light field imaging has emerged as a new modality, enabling to capture the angular and spatial information of a scene. This additional angular information is used to estimate the depth of a 3-D scene. The continuum of virtual view-points in light field data efficiently handles occlusion and provides a robust depth estimate for smaller distances. However, a narrow baseline in a light field camera limits the depth estimation for larger distances. To have an efficient occlusion handling and increase the operating distances, we proposed a novel disparity based stereo light field depth estimation method. First, segments are obtained in central sub-aperture of left view and then estimate the disparity vector of these segments using left camera sub-aperture images. This handles occlusion efficiently. Then stereo disparity at boundaries of these segments exploiting the epipolar geometry inherent in a light field data. Finally this stereo disparity at boundaries is propagated to other pixels and normalized. We provided a synthetic stereo light field data-set having inherent characteristic of a light field. We have tested our approach on a variety of real-world scenes captured with Lytro Illum camera and also on synthetic images. The proposed method outperforms several state-of-the-art algorithms.

关键词： stereo light field stereo depth estimation. Lytro Illum Light Field Imaging

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Masked Student Dataset of Expressions 22

Masked Student Dataset of Expressions

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proceedings of the Thirteenth indian conference on computer vision, graphics and image processing

作者： Sridhar Sola Darshan Gera Sri Sathya Sai Institute of Higher Learning India

ISBN: (纸本)9781450398220

Facial expression recognition (FER) algorithms work well in constrained environments with little or no occlusion of the face. However, real-world face occlusion is prevalent, most notably with the need to use a face mask in the current Covid-19 scenario. While there are works on the problem of occlusion in FER, little has been done before on the particular face mask scenario. Moreover, the few works in this area largely use synthetically created masked FER datasets. Motivated by these challenges posed by the pandemic to FER, we present a novel dataset, the Masked Student Dataset of Expressions or MSD-E, consisting of 1,960 real-world non-masked and masked facial expression images collected from 142 individuals. Along with the issue of obfuscated facial features, we illustrate how other subtler issues in masked FER are represented in our dataset. We then provide baseline results using ResNet-18, finding that its performance dips in the non-masked case when trained for FER in the presence of masks. To tackle this, we test two training paradigms: contrastive learning and knowledge distillation, and find that they increase the model’s performance in the masked scenario while maintaining its non-masked performance. We further visualise our results using t-SNE plots and Grad-CAM, demonstrating that these paradigms capitalise on the limited features available in the masked scenario. Finally, we benchmark SOTA methods on MSD-E. The dataset is available at https://***/SridharSola/MSD-E.

关键词： Human-computer interaction computer vision Covid-19 Facial expression recognition

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G-PReDICT: Generalizable Person Re-ID using Domain Invariant Contrastive Techniques✱ 22

G-PReDICT: Generalizable Person Re-ID using Domain Invariant...

引用

proceedings of the Thirteenth indian conference on computer vision, graphics and image processing

作者： Chaitra Jambigi Umar Masud Anirban Chakraborty Department of Computational and Data Sciences Indian Institute of Science IN Jamia Millia Islamia IN and Department of Computational and Data Sciences Indian Institute of Science India

ISBN: (纸本)9781450398220

Learning identity-aware, domain-invariant representations is crucial in solving domain generalizable person ReID (DG-ReID). Existing methods commonly use augmentation techniques either in feature space by mixing instance and batch normalization layers or in pixel space by adversarially generating pseudo domains. However, neither of these techniques guarantee identity preservation. Apart from increasing training data diversity, the augmented positive pairs also encode rich semantic relations which have not been fully explored. To address the above issues, we propose a novel framework for Generalizable Person Re-identification using Domain Invariant Contrastive Techniques (G-PReDICT). Specifically, we use simple yet effective perturbation strategies to hallucinate positive samples across domains by realistically modelling domain variations while preserving the target identities. We harness rich sample-sample relations between the hallucinated positive-negative pairs to learn domain-invariant representations using supervised contrastive learning. We also use a domain independent auxiliary task, i.e. attribute prediction to learn robust representations and introduce attribute annotations for two large scale public benchmarks i.e. CUHK-03 and MSMT17. Extensive experiments on standard benchmarks demonstrate the effectiveness of the proposed method.

关键词： Deep learning

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Nrityantar: Pose oblivious indian classical dance sequence classification system 18

Nrityantar: Pose oblivious Indian classical dance sequence c...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Vinay Kaushik Prerana Mukherjee Brejesh Lall Dept. of Electrical Engineering IIT Delhi

ISBN: (纸本)9781450366151

In this paper, we attempt to advance the research work done in human action recognition to a rather specialized application namely indian Classical Dance (ICD) classification. The variation in such dance forms in terms of hand and body postures, facial expressions or emotions and head orientation makes pose estimation an extremely challenging task. To circumvent this problem, we construct a pose-oblivious shape signature which is fed to a sequence learning framework. The pose signature representation is done in two-fold process. First, we represent person-pose in first frame of a dance video using symmetric Spatial Transformer Networks (STN) to extract good person object proposals and CNN-based parallel single person pose estimator (SPPE). Next, the pose basis are converted to pose flows by assigning a similarity score between successive poses followed by non-maximal suppression. Instead of feeding a simple chain of joints in the sequence learner which generally hinders the network performance we constitute a feature vector of the normalized distance vectors, flow, angles between anchor joints which captures the adjacency configuration in the skeletal pattern. Thus, the kinematic relationship amongst the body joints across the frames using pose estimation helps in better establishing the spatio-temporal dependencies. We present an exhaustive empirical evaluation of state-of-the-art deep network based methods for dance classification on ICD dataset.

关键词： Action Recognition

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Scale and Rotation Corrected CNNs (SRC-CNNs) for Scale and Rotation Invariant Character Recognition: SRC-CNN for Scale and Rotation Invariant Character Recognition 2018

Scale and Rotation Corrected CNNs (SRC-CNNs) for Scale and R...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Swetha V. C. Deepak Mishra Sai Subrahmanyam Gorthi Indian Institute of Space Science and Technology Thiruvananthapuram Kerala Indian Institution of Technology Tirupati Andra Pradesh

ISBN: (纸本)9781450366151

Last decade has witnessed rapid growth for the popularity of Convolutional Neural Networks (CNNs), in detecting and classifying objects. The self trainable nature of CNNs makes them the strongest candidate as a classifier and a feature extractor. However, many of the existing CNN architectures fail recognizing texts or objects under input rotation and scaling. This paper introduces an elegant approach, 'Scale and Rotation Corrected CNN (SRC-CNN)' for scale and rotation invariant text recognition, exploiting the concept of principal component of characters. Prior to training and testing with baseline CNN, 'SRC-CNN' maps each character image to a reference orientation and scale, which is again derived from the character image itself. SRC-CNN is capable of recognizing characters in a document, even though they differ in orientation and scale greatly. The proposed method does not demand any training with samples which are scaled or rotated. The performance of proposed approach is validated on different character data sets like MNIST, MNIST_rot_12k and English alphabets and compared with state of the art rotation invariant classification networks. SRC-CNN is a generalized approach and can be extended for rotation and scale invariant classification of many other datasets as well, choosing any appropriate baseline CNN. Here we have demonstrated the generality of the proposed SRC-CNN on MNIST Fashion data set and found to perform well in rotation and scale invariant classification of objects as well. This paper demonstrates how the basic PCA based rotation and scale invariant image recognition can be integrated to CNN for achieving better rotational and scale invariances in classification.

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Unsupervised Detection of Surface Mine Sites using Sentinel Multi-spectral imagery and Dual-polarimetric SAR Data 18

Unsupervised Detection of Surface Mine Sites using Sentinel ...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Shashaank M. Aswatha Vishnu Saini Jayanta Mukherjee Prabir K. Biswas Subhas Aikat Arundhati Misra Indian Institute of Technology Kharagpur West Bengal India Indian Institute of Technology Kharagpur India Space Applications Centre Indian Space Research Organization Ahmedabad India

ISBN: (纸本)9781450366151

Detecting surface mine activities has been one of the major challenges of remote sensing community to monitor ecological balance in the vicinity of mine area. Towards addressing this issue, we propose an unsupervised mine-site detection technique using multi-modal remotely sensed data from Sentinel missions. We use Sentinel-1's dual polarized SAR data, and Sentinel-2's multi-spectral data for this purpose. An initial set of seed pixels is sampled using spectral slopes based rules on multi-spectral images, which are tailored to capture mining area pixels that exhibit common characteristics. Then, we generate features using multi-spectral image indices and dual polarized SAR data Stokes' parameters. These features over locations sampled by slope based rules are used to train a one-class support vector machine to detect the land anomalies (mine activities). As observed from the experiments, multi-spectral features are efficient in separating mine region and built-up region from the rest of the land surface, and SAR image features discriminate built-up region from the rest of the image. We fuse both of these imagery features to encash their advantage in segregating mine sites in the considered study area. We demonstrate the efficiency of the proposed technique in detecting active coal mines over two study sites, Asansol region (in West bengal, India) and Jharia region (in Jharkhand, India), both of which exhibit common geographical characteristics. The average detection accuracy in our results is found to be around 94.8%.

关键词： Multi-spectral imagery

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A free viewpoint 3DTV system based on parameterized variety model

A free viewpoint 3DTV system based on parameterized variety ...

引用

International conference on Pattern Recognition

作者： Mansi Sharma Santanu Chaudhury Brejesh Lall Department of Electrical Engineering Indian Institute of Technology Delhi India

ISBN: (纸本)9781467322164

This paper presents a novel parameterized variety based architecture for interactive 3DTV and Free viewpoint TV (FTV) applications. The proposed signal representation scheme allows to render free viewpoint images, taking only few sample images of the scene acquired by arbitrary, uncalibrated cameras. We extend the Parameterized image Variety approach for rendering proposed earlier by Genc and Ponce [1] to handle full perspective cameras. A fast and efficient algebraic framework is proposed for the parameterized representation of 3D scene, in terms of image pixel positions corresponding to only three reference scene points. The key aspects of the novel FTV architecture based on this variety model are i) interactive stereoscopic view synthesis from arbitrary viewpoint ii) intuitive interface for content based virtual view specification iii) facilitation to add special effects like 3D scene augmentation.

关键词： Cameras Rendering (computer graphics) Calibration Equations Mathematical model Data mining Stereo image processing

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Intelligent Computing and Applications: proceedings of the International conference on ICA, 22-24 December 2014 1

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丛书名： Advances in Intelligent Systems and Computing 343

2015年

作者： Durbadal Mandal, Rajib Kar, Swagatam Das, Bijaya Ketan Panigrahi (eds.)

ISBN: (数字)9788132222682

ISBN: (纸本)9788132222675;9788132222682

The idea of the 1st International conference on Intelligent Computing and Applications (ICICA 2014) is to bring the Research Engineers, Scientists, Industrialists, Scholars and Students together from in and around the globe to present the on-going research activities and hence to encourage research interactions between universities and industries. The conference provides opportunities for the delegates to exchange new ideas, applications and experiences, to establish research relations and to find global partners for future collaboration. The proceedings covers latest progresses in the cutting-edge research on various research areas of image, Language processing, computer vision and Pattern Recognition, Machine Learning, Data Mining and Computational Life Sciences, Management of Data including Big Data and Analytics, Distributed and Mobile Systems including Grid and Cloud infrastructure, Information Security and Privacy, VLSI, Electronic Circuits, Power Systems, Antenna, Computational fluid dynamics & Heat transfer, Intelligent Manufacturing, Signal processing, Intelligent Computing, Soft Computing, Bio-informatics, Bio Computing, Web Security, Privacy and E-Commerce, E-governance, Service Orient Architecture, Data Engineering, Open Systems, Optimization, Communications, Smart wireless and sensor Networks, Smart Antennae, Networking and Information security, Machine Learning, Mobile Computing and Applications, Industrial Automation and MES, Cloud Computing, Green IT, IT for Rural Engineering, Business Computing, Business Intelligence, ICT for Education for solving hard problems, and finally to create awareness about these domains to a wider audience of practitioners.

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Leveraging information from imperfect examples: Common action sequence mining from a mix of incorrect performances 18

Leveraging information from imperfect examples: Common actio...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Hiteshi Jain Gaurav Harit Indian Institute of Technology Jodhpur Jodhpur India

ISBN: (纸本)9781450366151

As much as good representation and theory are needed to explain human actions, so are the action videos used for learning good segmentation techniques. To accurately model complex actions such as diving, figure skating, and yoga practices, videos depicting action by human experts are required. Lack of experts in any domain leads to reduced number of videos and hence an improper learning. In this work we attempt to utilize imperfect amateur performances to get more confident representations of human action sequences. We introduce a novel Community Detection based unsupervised framework that provides mechanisms to interpret video data and address its limitations to produce better action representation. Human actions are composed of distinguishable key poses which form dense communities in graph structures. Anomalous poses performed for a longer duration can also form such dense communities but can be identified based on their rare occurrence across action videos and be rejected. Further, we propose a technique to learn the temporal order of these key poses from these imperfect videos, where the inter community links help reduce the search space of many possible pose sequences. Our framework is seen to improve the segmentation performance of complex human actions with the help of some imperfect performances. The efficacy of our approach has been illustrated over two complex action datasets - Sun Salutation and Warm-up exercise, that have been developed using random executions from amateur performers.

关键词： Unsupervised Anomaly Detection

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Anomaly Detection using Context Dependent Optical Flow 2018

Anomaly Detection using Context Dependent Optical Flow

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Ranjan Mondal Bhabatosh Chanda Indian Statistical Institute Kolkata India

ISBN: (纸本)9781450366151

Anomaly detection is a major task in crowd management through video surveillance. It refers to the events which are deviated from normal events. We have introduced an unsupervised method to detect motion anomaly in surveillance video. In this work we have considered only optical flow as feature. First, for each frame we compute magnitude of optical flow of motion using flownet2 [10]. Then, mean of magnitude of flow due to regular normal motion (given in training data) is computed at each pixel where such motion exists in the training video frames. Our strategy is to compare the motion under consideration against this mean flow magnitude, and we expect that the anomalous motion would differ significantly from normal motion. An autoencoder type network is trained to detect this anomaly. Training data patch is constructed by Interleaving the columns of mean optical flow patch and the corresponding flow patch from each frame. This interleaving is done to incorporate context dependency. The autoencoder is trained to minimize mean-square reconstruction error between input column wise interleaved patch and output (i.e., reconstructed patch) of the autoencoder. During testing, a patch is declared anomalous if the reconstruction error is high compared to the training error. Experiments have been carried out on UCSD and UMN dataset and are compared with other methods. Our method gives comparable results with other state-of-the-art methods.

关键词： Motion Representation Anomaly Detection Activity Recognition Unsupervised Method

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