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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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Shape recognition based on shape-signature identification and condensibility: Application to underwater imagery

Shape recognition based on shape-signature identification an...

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Jeet Banerjee Ranjit Ray Siva Ram Krishna Vadali Ritwik Kumar Layek Sankar Nath Shome Electronics and Electrical Communication Dept Indian Institute of Technology Kharagpur Kharagpur West Bengal Robotics and Automation Div CSIR-Central Mechanical Engineering Research Institute Durgapur West Bengal Central Mechanical Engineering Research Institute CSIR Durgapur West Bengal IN

In this paper, a shape recognition method is proposed for a few common geometrical shapes including straight line, circle, ellipse, triangle, quadrilateral, pentagon and hexagon. In the present work, two indices namely Unique Shape Signature (USS) and Condensibility (C) are employed for shape recognition of an object. Using the USS index, all the above mentioned non-circular shapes are neatly recognized, whereas, the C index recognized the circular objects. An added advantage of the proposed method is that it can further differentiate triangles, quadrilaterals and both symmetric and non-symmetric shapes of pentagon and hexagon using distance variance (V ar(d si )) parameter calculated from USS. Applying the proposed method on above mentioned shapes, an overall recognition rate of 98.80% is achieved on several simulated and real objects of different shapes. Proposed method has also been compared with two existing methods, presents better result. Performance of the proposed method is illustrated by applying it on underwater images and it is observed to perform satisfactory on all the images under test.

关键词： Shape image edge detection Object recognition image color analysis Indexes Standards

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Summarization of Neonatal Video EEG for Seizure and Artifact Detection

Summarization of Neonatal Video EEG for Seizure and Artifact...

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Sourya Bhattacharyya Aditi Roy Debi Prosad Dogra Arunava Biswas Jayanta Mukherjee Arun Kumar Majumdar Bandana Majumdar Suchandra Mukherjee Arun Kumar Singh Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal India School of Information Technology Indian Institute of Technology Kharagpur West Bengal India Department of Neonatology IPGMER SSKM Hospital Kolkata West Bengal India

Monitoring neonatal EEG signal is useful in identifying neonatal convulsions or seizures. For neonates, seizures can be electrographic, electro clinical, or both simultaneously. Electrographic seizure is identified via recorded EEG signal, while electro clinical seizures exhibit clinical manifestations. Sometimes neonates can exhibit silent seizures which may be clinically invisible but identifiable in recorded EEG, or vice versa. Thus, simultaneous monitoring of video and recorded EEG determines the correlation between the electrographic and electro clinical seizures. Furthermore, analyzing the movements of the neonates can identify movement artifacts easily, thus preventing false seizure detection. However, storage of high quality video recordings require large storage space. As neonates do not commonly exhibit movements, summarizing the video for storing only patient movements along with corresponding timestamps, can be useful. In this paper, a video summarization method is proposed for efficient browsing of video-EEG. Identification and analysis of the patterns of interest is possible via summarized information, thus reducing effective analysis time. In addition, quantitative demonstration of electrographic and electro clinical seizures is presented to analyze the utility of video-EEG.

关键词： Electroencephalography Pediatrics Monitoring Electrodes Correlation Hospitals

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A Novel Framework for Robust Fingerprint Representations using Deep Convolution Network with Attention Mechanism 23

A Novel Framework for Robust Fingerprint Representations usi...

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

作者： Alka Ranjan Nishkal Prakash Santhoshkumar Peddi Debasis Samanta Computer Science and Engineering Department Indian Institute of Technology Kharagpur India

ISBN: (纸本)9798400716256

Fingerprint recognition systems are highly dependent on the quality and accuracy of the fingerprint representation. Traditional fingerprint recognition algorithms often employ handcrafted features designed manually based on domain knowledge and heuristics. However, these methods may struggle to capture the intricate patterns and minute details present in fingerprints, leading to limited discriminatory power and vulnerability to noise and variations in imaging conditions. This paper presents a novel approach for learning robust fingerprint representation by leveraging the power of deep convolution networks (DCN) along with attention mechanisms. The proposed method consists of a dual-module framework. The first module extracts local features, i.e., minutiae-based features, and the second module focuses on extracting global features, i.e., texture-based features. The extracted local and global features are concatenated into a single fixed-length feature vector. It aims to capture fine-grained details and complex patterns present in fingerprint images, thereby enhancing the accuracy and robustness of fingerprint recognition systems. Experimental results show that the proposed method outperforms state-of-the-art (SOTA) fingerprint recognition performances across a variety of benchmark datasets, containing fingerprints from various sensors and having different resolutions and sizes. It harnesses the finer features of fingerprints, as well as efficiently boosts the performance of the framework to progress SOTA.

关键词： Attention Mechanism Deep Convolutional Networks Fingerprint Embedding Fingerprint Matching. Fingerprint Recognition

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MMAG: Mutually Motivated Attention Gates for Simultaneous Extraction of Contextual and Spatial Information from a Monocular image 23

MMAG: Mutually Motivated Attention Gates for Simultaneous Ex...

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

作者： Aditya Singh Saloni Mohan Vandana Kushwaha G C Nandi IIIT Allahabad IN

ISBN: (纸本)9798400716256

In order to effectively interact with its environment, an agent must possess the ability to comprehend the ’what’ and the ’where.’ Two vision-based approaches, namely semantic segmentation and depth estimation, can be employed to provide this information. This paper introduces a unified model that combines these two tasks and utilizes a shared latent space. The chosen approach for the model involves an encoder-decoder architecture accompanied by a novel attention gate mechanism called MMAG, which has been proven to be highly effective. Furthermore, a common skip connection method, in conjunction with the proposed attention gate, is included to emphasize crucial features for both prediction purposes. By leveraging the complementary information within the shared representations, the model is capable of generating more accurate predictions compared to other joint-prediction models, all while utilizing fewer network parameters. To enhance performance, dilated layers are incorporated, allowing for focused attention through different receptive fields. The model’s performance has been evaluated on the NYU Depth v2 and Camvid datasets, demonstrating improved results on both tasks when compared to other state-of-the-art models. With its reduced parameter count, this model is particularly suitable for low-cost robots and has undergone successful testing for inference on the LoCoBot robot.

关键词： attention gate mechanism encoder-decoder monocular depth estimation semantic segmentation

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Dual Stage Semantic Information Based Generative Adversarial Network For image Super-Resolution✱ 23

Dual Stage Semantic Information Based Generative Adversarial...

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

作者： Shailza Sharma Abhinav Dhall Dr. Vinay Kumar Vivek Singh Thapar Institute of Engineering and technology India Indian Institute of Technology Ropar India Oxford Brookes University UK

ISBN: (纸本)9798400716256

Deep learning methods for the super-resolution problem are showing great performance compared to other traditional techniques. However, these methods are unable to learn complex spatial structures and high frequency details; which leads to over-smooth results. In the present paper, a novel Generative Adversarial Network based architecture named as Residue and Semantic feature based Dual Subpixel Generative Adversarial Network has been proposed for generator and discriminator networks to solve super-resolution problem. The generator network is residue and semantic feature based dual subpixel generative architecture. This architecture is divided into two stages: premier residual stage and deuxieme residual stage. These two stages are concatenated together to form a two stage upsamping process, which enhances the feature learning capability of our model. Inter and intra residual connections are made within these two stages; helping us to sustain the high texture details of images. Semantic based information is implanted in generator to enhance the quality of objects in an image. For embedding semantic information in generator, feature maps extracted from pre-trained model are merged with the input image. To stabilize the training process, we introduced spectral normalization in the discriminator. Visual perception and mean opinion score shows that proposed method outperforms the other state-of-the-art methods.

关键词： Convolutional Neural Networks Generative Adversarial Networks Residual learning Spectral normalization Super-Resolution

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MoMSGAN: Mode Collapse based Degradation Agnostic Multi-Scale Super-Resolution of Medical images 23

MoMSGAN: Mode Collapse based Degradation Agnostic Multi-Scal...

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

作者： Samiran Dey Tapabrata Chakraborti Partha Basuchowdhuri Debasis Mitra Robin Augustine Sanjoy Kumar Saha Indian Association for the Cultivation of Science India The Alan Turing Institute and University College London UK and Linacre College University of Oxford United Kingdom Indian Institute of Engineering Science and Technology Shibpur India Uppsala University Sweden Jadavpur University India

ISBN: (纸本)9798400716256

Existing super-resolution (SR) models require separate training for different scales of SR because of the fixed upsampling levels in their architecture. We propose a novel one-time training approach for multi-scale SR models with a fixed upsampling unit, that can effectively super-resolve images to various scales. Another persisting problem relevant to medical image SR is that the models are biased to the level of degradation present in the low-resolution (LR) images and generate variations in anatomical structures and pathologies when applied to LR images corresponding to the same high-resolution (HR) image, varying only in degradation. But regardless of the degradation in the LR images, the anatomical structures and clinical findings in the SR images should remain consistent and unique to the patient’s specific anatomy and health conditions. We thus propose a novel approach for GANs to blindly constrain the variations in super-resolved medical images. Further, we introduce an ensemble loss function to gain details and accurate textures, while enabling multi-scale SR and making the model unbiased towards degradation in LR images. Experimental results on lung CT scans demonstrate that our model, MoMSGAN, is an efficient one-time training approach for multi-scale SR and is also capable of reducing the degradation bias in reproducing accurate information in medical images, thus establishing state-of-the-art.

关键词： GAN Medical image Mode Collapse Multi-Scale Super Resolution

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6th International Symposium on Signal processing and Intelligent Recognition Systems, SIRS 2020

6th International Symposium on Signal Processing and Intelli...

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6th International Symposium on Advances in Signal processing and Intelligent Recognition Systems, SIRS 2020

ISBN: (纸本)9789811604249

The proceedings contain 27 papers. The special focus in this conference is on Signal processing and Intelligent Recognition Systems. The topics include: Improved Long-Short Term Memory U-Net for image Segmentation;Extraction of Parcel Boundary from UAV images Using Deep Learning Techniques;Grad-CAM-Based Classification of Chest X-Ray images of Pneumonia Patients;periocular Recognition Under Unconstrained image Capture Distances;acoustic Prediction of Elephants for Localization and Movement Tracking Using Sensors and Distance Metrics;community Detection Algorithms in Complex Networks: A Survey;a Systematic Review on the Influence of User Personality in Rumor and Misinformation Propagation Through Social Networks;HRIDAI: A Tale of Two Categories of ECGs;channel-Aware Decision Fusion with Rao Test for Multisensor Fusion;detection of Breast Cancer from Mammogram images Using Deep Transfer Learning;Comprehending the Dynamics of EEG Generated Under Various Odorant Stimulation on the Brain;Robust Beamforming Against DOA Mismatch with Null Widening for Moving Interferences;6G Ultra-Low Latency Communication in Future Mobile XR Applications;applying Neural Style Transfer to Spectrograms of Environmental Audio;identification of indian English by Speakers of Multiple Native Languages;Robust and Imperceptible Digital image Watermarking Based on DWT-DCT-Schur;comparative Study of Maturation Profiles of Neural Cells in Different Species with the Help of computer vision and Deep Learning;deep Learning Algorithms to Detect and Localize Acute Intracranial Hemorrhages;Explainable NLP: A Novel Methodology to Generate Human-Interpretable Explanation for Semantic Text Similarity;a Leak Detection in Water Pipelines Using Discrete Wavelet Decomposition and Artificial Neural Network;supervised Feature Learning for Music Recommendation.

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A Secure and Light Weight User Authentication System Based on Online Signature Verification for Resource Constrained Mobile Networks 4th

A Secure and Light Weight User Authentication System Based o...

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

作者： Vorugunti, Chandra Sekhar Guru, D.S. Pulabaigari, Viswanath Indian Institute of Information Technology-SriCity Chittoor India University of Mysore Mysore India

ISBN: (纸本)9789811393600

The rapid advances in mobile and networking technologies results in usage of mobiles for critical applications like m-commerce, m-payments etc. Even though mobile based services offer many benefits, authenticating the user logging into the system is a big challenge. To mitigate this concern, secure mobile applications based on user online signature verification (OSV) has been proposed. Unfortunately, these models would intensify the substantial computational overhead on thin and resource-constrained mobile devices. This summarizes for a critical need of OSV models which are computationally efficient and achieves higher classification accuracy. Recently, several OSV models have been defined in the literature. However, these models are not computationally effective for resource-constrained mobile devices, because the proposed verification models ought to require not only higher feature dimension but also heavy weight writer specific parameter fixation logic. In this manuscript, we propose an efficient and light weight OSV model for resource-constrained mobile devices. Our approach employs dimensionality reduction based on DBSCAN clustering technique and user specific parameter selection. Thorough experimental analysis are conducted on benchmarking online signature datasets MCYT-100 (DB1) and MCYT-330 (DB2) datasets which confirms the efficiency of proposed model with latest OSV models. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Mobile security

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Evolution of Bagbazar Street Through Visibility Graph Analysis (1746–2020) 12th

Evolution of Bagbazar Street Through Visibility Graph Analys...

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

作者： Chakraborty, Shilpi Ji, Shiva Department of Design Indian Institute of Technology Hyderabad Telangana Sangareddy Kandi502284 India

ISBN: (纸本)9789811941351

Activities intended at increasing the accessibility of offers connected with urban growth and efforts and pragmatic practices targeted at boosting community contact through the usage of common areas contribute to cultural integration in the urban context. Bag Bazar Street is one of Kolkata’s three main streets, known as the grandmother road in ekantoporibar (joint family), making it an excellent case study site. By developing and assessing ideas regarding urban mobility characteristics and linkages across time, syntactic space analyses aid in the examination of diverse historical sources to build a coherent narrative of urban topology configuration. In a quantitative study of urban space and space syntax, enticing prospects as an exciting development of spaces use the city plan as the initial stage for investigating the historical relationship between the urban setting and urban life. The significant features of the axial line are with utilizing the argumentation of extrinsic spatial features in space syntax. DepthMap, open-source software for studying the visibility of urban core, was used in this study. The utilization of an axial map with a connection map is to frame basic facts in the study. Any strategy to revival or smart city development necessitates a full grasp of the spatial factors and stages that characterize our urban communities, as well as the link between the two. Consequently, this research uses axial analysis and visibility graph analysis to validate the degree of functionality of Kolkata’s Bag Bazar street over time as an input for digital preservation. The paper identifies the period having transformation for each attribute. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Urban growth

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Variational Augmentation for Enhancing Historical Document image Binarization✱ 22

Variational Augmentation for Enhancing Historical Document I...

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

作者： Avirup Dey Prof. Mita Nasipuri Nibaran Das Jadavpur University India

ISBN: (纸本)9781450398220

Historical Document image Binarization is a well-known seg- mentation problem in image processing. Despite ubiquity, traditional thresholding algorithms achieved limited success on severely degraded document images. With the advent of deep learning, several segmentation models were proposed that made significant progress in the field but were limited by the unavailability of large training datasets. To mitigate this problem, we have proposed a novel two-stage framework- the first of which comprises a generator that generates degraded samples using variational inference and the second being a CNN-based binarization network that trains on the generated data. We evaluated our framework on a range of DIBCO datasets, where it achieved competitive results against previous state-of-the-art methods.

关键词： Binarization

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