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检索条件"任意字段=Proceedings of the Thirteenth Indian Conference on Computer Vision, Graphics and Image Processing"

共 1468 条记录，以下是161-170 订阅

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A Fine-Grained Vehicle Detection (FGVD) Dataset for Unconstrained Roads✱ 22

A Fine-Grained Vehicle Detection (FGVD) Dataset for Unconstr...

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

作者： Prafful Kumar Khoba Chirag Parikh C.V. Jawahar Ravi Kiran Sarvadevabhatla Rohit Saluja Indian Institute of Technology Delhi IN IIIT Hyderabad IN IIT Mandi IN

ISBN: (纸本)9781450398220

The previous fine-grained datasets mainly focus on classification and are often captured in a controlled setup, with the camera focusing on the objects. We introduce the first Fine-Grained Vehicle Detection (FGVD) dataset in the wild, captured from a moving camera mounted on a car. It contains 5502 scene images with 210 unique fine-grained labels of multiple vehicle types organized in a three-level hierarchy. While previous classification datasets also include makes for different kinds of cars, the FGVD dataset introduces new class labels for categorizing two-wheelers, autorickshaws, and trucks. The FGVD dataset is challenging as it has vehicles in complex traffic scenarios with intra-class and inter-class variations in types, scale, pose, occlusion, and lighting conditions. The current object detectors like yolov5 and faster RCNN perform poorly on our dataset due to a lack of hierarchical modeling. Along with providing baseline results for existing object detectors on FGVD Dataset, we also present the results of a combination of an existing detector and the recent Hierarchical Residual Network (HRN) classifier for the FGVD task. Finally, we show that FGVD vehicle images are the most challenging to classify among the fine-grained datasets. [GitHub]

关键词： dataset detection gradcam. Fine-grained unconstrained roads

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Unique Identity Generation with Global Features from Multimodal Biometric Data 23

Unique Identity Generation with Global Features from Multimo...

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

作者： Priyabrata Dash Debasis Samanta Monalisa Sarma Indian Institute of Technology Kharagpur IN Indian Institute of Technology Kharagpur IN

ISBN: (纸本)9798400716256

The patterns in biometric data, such as fingerprints, iris, etc., are random and distinct from one individual to another, making them ideal for generating unique identities suitable for many applications. This work proposes creating users’ unique identities based on their fingerprint and iris biometric data. The global feature descriptor, which is invariant to scale, rotation, and translation, is considered in this work. The global feature descriptors are obtained from fingerprint and iris images and fused to create a single feature vector. Next, feature selection based on the Laplacian score is applied to remove irrelevant features. Further, a metric-learning-based mechanism is introduced to enhance the feature vectors’ intra-class similarity and inter-class separability. Finally, a novel quantization mechanism is used to generate a unique identity. Experiments with multimodal datasets show that the generated identities are distinct, random, revocable, and unlinkable. This identity generation scheme can be utilized in storage security, digital signature schemes, remote authentication protocols, blockchain security, etc.

关键词： Crypto-biometric system Global feature descriptor. Multimodal biometric data Pattern recognition Unique identity generation

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MATIC: Memory-Guided Adaptive Transformer for image Captioning 12th

MATIC: Memory-Guided Adaptive Transformer for Image Captioni...

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12th indian conference on computer vision, graphics, and image processing, ICVGIP 2021

作者： Gajhiye, Gaurav O. Nandedkar, Abhijeet V. Nanded India

ISBN: (纸本)9789811941351

Transforming the visual knowledge into linguistic description is a demanding and essential task known as image captioning, which necessitates a vision-language-based system to co-relate the multi-modal information. Transformer architecture has recently become cutting-edge in addressing the image captioning-related problems. In this paper, the utility of the transformer is explored by bridging single layer memory-guided encoder and multi-layer adaptive attention decoder framework entitled as Memory-guided Adaptive Transformer for image Captioning. The memory-based encoder is used to capture the relative spatial information from CNN-based visual features and transfer it to the decoder, which then learns the inter-modality affinity between visual and linguistic features. The decoder with multi-headed adaptive attention reconciles the visual and linguistic information to describe the visual contents in a comprehensible way by balancing visual and semantic patterns through adaptive gate channeling. The performance of the proposed method is evaluated on widely used captioning datasets viz. Flickr8k and Flickr30k. The proposed method performs remarkable and earns a superior caption generation score on both datasets as compared to state-of-the-art. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decoding

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Interpreting Intrinsic image Decomposition using Concept Activations 22

Interpreting Intrinsic Image Decomposition using Concept Act...

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

作者： Avani Gupta Saurabh Saini P. J. Narayanan CVIT KCIS International Institute of Information Technology IN

ISBN: (纸本)9781450398220

Evaluation of ill-posed problems like Intrinsic image Decomposition (IID) is challenging. IID involves decomposing an image into its constituent illumination-invariant Reflectance (R) and albedo-invariant Shading (S) components. Contemporary IID methods use Deep Learning models and require large datasets for training. The evaluation of IID is carried out on either synthetic Ground Truth images or sparsely annotated natural images. A scene can be split into reflectance and shading in multiple, valid ways. Comparison with one specific decomposition in the ground-truth images used by current IID evaluation metrics like LMSE, MSE, DSSIM, WHDR, SAW AP%, etc., is inadequate. Measuring R-S disentanglement is a better way to evaluate the quality of IID. Inspired by ML interpretability methods, we propose Concept Sensitivity Metrics (CSM) that directly measure disentanglement using sensitivity to relevant concepts. Activation vectors for albedo invariance and illumination invariance concepts are used for the IID problem. We evaluate and interpret three recent IID methods on our synthetic benchmark of controlled albedo and illumination invariance sets. We also compare our disentanglement score with existing IID evaluation metrics on both natural and synthetic scenes and report our observations. Our code and data are publicly available for reproducibility 1.

关键词： ML interpretability evaluation techniques ill-posed problems Intrinsic image Decomposition Disentanglement TCAV

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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...

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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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Detecting Coronavirus (COVID -19) Disease Cues from Chest Radiography images 13

Detecting Coronavirus (COVID -19) Disease Cues from Chest Ra...

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13th indian conference on computer vision, graphics, and image processing, ICVGIP 2022

作者： Vinod, Athira Shilna Sreeni, K.G. Purushothaman, Anurenjan Jeena, R.S. Computer Vision Lab Dept. of Electronics and Communication College of Engineering Trivandrum Kerala Trivandrum India Government Engineering College Kerala Idukki India Rajiv Gandhi Institute of Technology Kerala Kottayam India Affiliated to APJ Abdul Kalam Technological University Kerala Trivandrum India

ISBN: (纸本)9781450398237

This paper proposes a deep learning-based approach to detect COVID-19 infections in lung tissues from chest Computed Tomography (CT) images. A two-stage classification model is designed to identify the infection from CT scans of COVID-19 and Community Acquired Pneumonia (CAP) patients. The proposed neural model named, Residual C-NiN uses a modified convolutional neural network (CNN) with residual connections and a Network-in-Network (NiN) architecture for COVID-19 and CAP detection. The model is trained with the Signal processing Grand Challenge (SPGC) 2021 COVID dataset. The proposed neural model achieves a slice-level classification accuracy of 93.54% on chest CT images and patient-level classification accuracy of 86.59% with class-wise sensitivity of 92.72%, 55.55%, and 95.83% for COVID-19, CAP, and Normal classes, respectively. Experimental results show the benefit of adding NiN and residual connections in the proposed neural architecture. Experiments conducted on the dataset show significant improvement over the existing state-of-the-art methods reported in the literature. © 2022 ACM.

关键词： COVID-19

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An Efficient Motor imagery Classification Framework using Sparse Brain Connectivity and Class-consistent Dictionary Learning from Electroencephalogram Signals 14

An Efficient Motor Imagery Classification Framework using Sp...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Pain, Subrata Samanta, Debasis Sarma, Monalisa Advanced Technology Development Center IIT Kharagpur West Bengal Kharagpur India Department of Computer Science and Engineering IIT Kharagpur West Bengal Kharagpur India Subir Chowdhury School of Quality and Reliability IIT Kharagpur West Bengal Kharagpur India

ISBN: (纸本)9798400716256

Identifying patterns in high-dimensional and noisy neuro-imaging data such as Electroencephalogram (EEG) has always been challenging. This fact hampers the performance of the real-time Brain-computer Interfaces (BCI) controlled by EEG recordings of brain signals, such as imagined motor activity (MI-EEG). Traditionally, the MI-EEG signals are classified by mainly two approaches, firstly, using the Common Spatial Patterns (CSPs) extracted from MI-EEG as features for Machine Learning (ML) classifiers, and secondly, using various Deep Learning (DL)-based approaches. But these methods are computationally heavy and ignore the dynamic topological interactions between the brain regions. Recently, due to high performance and low testing time, sparse representation-based classifiers have been utilized for MI-EEG classifications with higher real-life applicability. But these methods consider a fixed training dictionary consisting of inherently noisy training features. This study proposes an efficient MI-EEG classification framework utilizing a sparse representation of brain connectivity features and a dictionary learned from the training features. Here, the dynamic synchrony of co-activating brain regions is captured by suitable connectivity measures, which are further used as features. Further, sparse representations of the connectivity features are obtained through sparsity and dictionary learning. As this dictionary learning method preserves the class information during dictionary updation, this method is best suited for classification tasks. The dictionary learning method simultaneously learns a reduced dictionary and a linear classifier further used for classification. The proposed framework is validated using a publicly available MI dataset, and corresponding classification performances are observed. The proposed framework outperforms the other sparsity-based classifiers in mixed-, intra-, and cross-subject performances, making it suitable for real-life BCI applications.

关键词： Electroencephalography

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Designing a Sequential Hybrid BCI Speller on Fusion of Motor imagery and Steady-State Visually Evoked Potentials EEG Data 23

Designing a Sequential Hybrid BCI Speller on Fusion of Motor...

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

作者： Tutan Nama Debasis Samanta DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING INDIAN INSTITUTE OF TECHNOLOGY KHARAGPUR IN

ISBN: (纸本)9798400716256

Brain-computer interfaces (BCIs) enable direct communication between the human brain and external devices, interpreting signals like Electroencephalogram (EEG) to translate user intentions into commands. While EEG-based BCIs have potential, they face accuracy and user training challenges. Hybrid BCIs (hBCIs) integrate various input modalities to enhance robustness of BCI applications. Based on the operation mode hBCIs are two types, sequential and simultaneous. In simultaneous hBCI two or more input modalities work together to achieve a single goal. Sequential hBCIs, where modalities work in sequence, offer intuitive interactions. This work introduces a hybrid BCI speller combining MI and SSVEP in a sequential operation mode of hybrid BCI. Two-class MI EEG signals navigate through character groups at the initial stage and initiate SSVEP-based spelling. The interface integrates letters, punctuation, space, error correction operators, and digits. Promising results show an average entry rate of 8.28 char/min with 92.35% accuracy among six participants. This study showcases the potential of hybrid BCI spellers, enhancing communication for individuals with motor impairments.

关键词： BCI Speller

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Quaternion Factorized Simulated Exposure Fusion 22

Quaternion Factorized Simulated Exposure Fusion

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

作者： Saurabh Saini P. J. Narayanan CVIT IIIT-Hyderabad India

ISBN: (纸本)9781450398220

image Fusion maximizes the visual information at each pixel location by merging content from multiple images in order to produce an enhanced image. Exposure Fusion, specifically, fuses a bracketed exposure stack of poorly lit images to generate a properly illuminated image. Given a single input image, exposure fusion can still be employed on a ‘simulated’ exposure stack, leading to direct single image contrast and low-light enhancement. In this work, we present a novel ‘Quaternion Factorized Simulated Exposure Fusion’ (QFSEF) method by factorizing an input image into multiple illumination consistent layers. To this end, we use an iterative sparse matrix factorization scheme by representing the image as a two-dimensional pure quaternion matrix. Theoretically, our representation is based on the dichromatic reflection model and accounts for the two scene illumination characteristics by factorizing each progressively generated image into separate specular and diffuse components. We empirically prove the advantages of our factorization scheme over other exposure simulation methods by using it for the low-light image enhancement task. Furthermore, we provide three exposure fusion strategies which can be used with our simulated stack and provide a comprehensive performance analysis. Finally, in order to validate our claims, we show extensive qualitative and quantitative comparisons against relevant state-of-the-art solutions on multiple standard datasets along with relevant ablation analysis to support our proposition. Our code and data are publicly available for easy reproducibility and reference. 1

关键词： Exposure Fusion Quaternion image Representation Low Light image Enhancement Robust Principal Component Analysis

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Spectrum-inspired Low-light image Translation for Saliency Detection 13

Spectrum-inspired Low-light Image Translation for Saliency D...

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13th indian conference on computer vision, graphics, and image processing, ICVGIP 2022

作者： Varghese, Kitty Rajagopalan, Sudarshan Lamba, Mohit Mitra, Kaushik Indian Institute of Technology Madras India

ISBN: (纸本)9781450398237

Saliency detection methods are central to several real-world applications such as robot navigation and satellite imagery. However, the performance of existing methods deteriorate under low-light conditions because training datasets mostly comprise of well-lit images. One possible solution is to collect a new dataset for low-light conditions. This involves pixel-level annotations, which is not only tedious and time-consuming but also infeasible if a huge training corpus is required. We propose a technique that performs classical band-pass filtering in the Fourier space to transform well-lit images to low-light images and use them as a proxy for real low-light images. Unlike popular deep learning approaches which require learning thousands of parameters and enormous amounts of training data, the proposed transformation is fast and simple and easy to extend to other tasks such as low-light depth estimation. Our experiments show that the state-of-The-Art saliency detection and depth estimation networks trained on our proxy low-light images perform significantly better on real low-light images than networks trained using existing strategies. © 2022 ACM.

关键词： Satellite imagery

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