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

共 1497 条记录，以下是131-140 订阅

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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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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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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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REF-SHARP: REFined face and geometry reconstruction of people in loose clothingg± 13

REF-SHARP: REFined face and geometry reconstruction of peopl...

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

作者： Routhu, Snehith Goud Sagar, Sai Sharma, Avinash IIIT Hyderabad India

ISBN: (纸本)9781450398237

In this paper, we address the problem of monocular 3D human reconstruction with an acute focus on the challenge of recovering person-specific facial geometry as well as suppressing surface noise, specifically addressing the issue of false geometrical variations caused by textural edges. Most of the existing state-of-the-art methods in this domain fail to address these challenges. More specifically, we propose to integrate facial and wrinkle map priors in a learning-based framework to improve the quality of full-body 3D reconstruction from monocular images. By incorporating facial prior, we recover person-specific identity unlike many of the existing methods which rely on parametric shape models. Similarly, the wrinkle map prior enables our network to alleviate the challenge of false geometrical variations caused by high-frequency textural details present in the input image. We evaluate our method on publicly available datasets & in-the-wild internet images with loose clothing and report superior performance both qualitatively and quantitatively when compared with SOTA methods. © 2022 ACM.

关键词： image reconstruction

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G-PReDICT: Generalizable Person Re-ID using Domain Invariant Contrastive Techniquesg± 13

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

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

作者： Jambigi, Chaitra Masud, Umar Chakraborty, Anirban Dept. of Computational and Data Sciences Indian Institute of Science Karnataka Bengaluru India Dept. of Electronics and Communications Jamia Millia Islamia New Delhi India

ISBN: (纸本)9781450398237

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. © 2022 ACM.

关键词： computer vision

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Dense captioning for Text-image ReID 23

Dense captioning for Text-Image ReID

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

作者： A V Subramanyam Vibhu Dubey Niranjan Sundararajan Brejesh Lall Department of Electronics & Communications Engineering Indraprastha Institute of Information Technology Delhi (IIIT-Delhi) IN Department of Computer Science and Engineering Indraprastha Institute of Information Technology Delhi (IIIT-Delhi) IN Department of Electrical Engineering IIT Delhi India

ISBN: (纸本)9798400716256

Text-to-image (T2I) ReID has attracted a lot of attention in the recent past. CUHK-PEDES, RSTPReid and ICFG-PEDES are the three available benchmarks to evaluate T2I ReID methods. RSTPReid and ICFG-PEDES comprise of identities from MSMT17 but due to limited number of unique persons, the diversity is limited. On the other hand, CUHK-PEDES comprises of 13,003 identities but has relatively shorter text description on average. Further, these datasets are captured in a restricted environment with limited number of cameras. In order to further diversify the identities and provide dense captions, we propose a novel dataset. Our dataset comprises of 20,000 unique identities captured in the wild and provides a rich dataset for text-to-image ReID. With a minimum of 26 words for a description, each image is densely captioned. We further synthetically generate images and fine-grained captions using Stable-diffusion and BLIP models trained on our dataset. We perform elaborate experiments using state-of-art text-to-image ReID models and vision-language pre-trained models and present a comprehensive analysis of the dataset. Our experiments also reveal that synthetically generated data leads to a substantial performance improvement in both same dataset as well as cross dataset settings. We will release the code and dataset.

关键词： Benchmark Synthetic data Text-to-image ReID

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Controllable image Synthesis via Feature Mask Coupling using Implicit Neural Representation✱ 22

Controllable Image Synthesis via Feature Mask Coupling using...

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

作者： Sonam Gupta Arti Keshari Sukhendu Das Indian Institute of Technology Madras India

ISBN: (纸本)9781450398220

Implicit neural representation (INR) has emanated as a powerful paradigm for 2D image representation. Recent works like INR-GAN have successfully adopted INR for 2D image synthesis. However, these lack explicit control on the generated images as achieved by their 3D-aware image synthesis counterparts like GIRAFFE. Our work investigates INRs for the task of controllable image synthesis. We propose a novel framework that allows for manipulation of foreground, background and their shape and appearance in the latent space. To achieve effective control over these attributes, we introduce a novel feature mask coupling technique that leverages the foreground and background masks for mutual learning. Extensive quantitative and qualitative analysis shows that our model can disentangle the latent space successfully and allows to change the foreground and/or background’s shape and appearance. We further demonstrate that our network takes lesser training time than other INR-based image synthesis methods.

关键词： Generative adversarial networks

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