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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91-100 订阅

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Unique Identity Generation with Global Features from Multimodal Biometric Data 14

Unique Identity Generation with Global Features from Multimo...

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

作者： Dash, Priyabrata Samanta, Debasis Sarma, Monalisa Indian Institute of Technology Kharagpur West Bengal Kharagpur India

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. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Authentication

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Fine-grain Cluster Estimation of Land Cover Classes using Landsat 8 Multi-spectral images 14

Fine-grain Cluster Estimation of Land Cover Classes using La...

引用

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

作者： Pal, Shankho Subhra Chatterjee, Ankita Mukhopadhyay, Jayanta Sarkar, Sudeshna Das, Partha Pratim Indian Institute of Technology Kharagpur West Bengal Kharagpur India Ashoka University Haryana Sonipat India

ISBN: (纸本)9798400716256

Earth observation satellites provide us with ample amount of raw data for land cover analysis. However, annotating these data is a cumbersome process, subjected to human error which compel us to shift from supervised to unsupervised techniques. Although clustering methods are being widely used for the past few years in the field of remote sensing, identifying the number of fine-grain classes present in a region remain a challenging problem. Therefore, we propose a rule-based and neural-network learning technique that can divide the pixels into three standard classes, water bodies, vegetation and vegetation-void. These classes are easier to identify and is not region-specific. Later we apply fine-grain clustering on each of these classes to segregate them into finer groups. Our clustering method identifies the appropriate number of fine-grain classes present in a specific region. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Landsat

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Knowledge Distillation with Ensemble Calibration 14

Knowledge Distillation with Ensemble Calibration

引用

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

作者： Mishra, Ishan Jain, Riyanshu Viradiya, Dhruv Patel, Divyam Mishra, Deepak Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798400716256

Knowledge Distillation is a transfer learning and compression technique that aims to transfer hidden knowledge from a teacher model to a student model. However, this transfer often leads to poor calibration in the student model. This can be problematic for high-risk applications that require well-calibrated models to capture prediction uncertainty. To address this issue, we propose a simple and novel technique that enhances the calibration of the student network by using an ensemble of well-calibrated teacher models. We train multiple teacher models using various data-augmentation techniques such as cutout, mixup, CutMix, and AugMix and use their ensemble for knowledge distillation. We evaluate our approach on different teacher-student combinations using CIFAR-10 and CIFAR-100 datasets. Our results demonstrate that our technique improves calibration metrics (such as expected calibration and overconfidence errors) while also increasing the accuracy of the student network. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Distillation

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EDCGAN: Encoder Decoder based Conditional GAN for SAR to Optical image translation ✱ 22

EDCGAN: Encoder Decoder based Conditional GAN for SAR to Opt...

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

作者： Avinash Chouhan Nitesh Jindal Arijit Sur Dibyajyoti Chutia Shiv Prasad Aggarwal North Eastern Space Applications Centre IN and Indian Institute of Technology Guwahati India Indian Institute of Technology Guwahati IN North Eastern Space Applications Centre IN

ISBN: (纸本)9781450398220

High-resolution optical images are heavily utilized in various remote sensing applications. The optical images cannot reflect the actual ground information in cloudy conditions. SAR images are used to solve this for their ability to see through clouds. But SAR images are usually available with coarser resolutions. So, there is a need to produce an optical image from a SAR image to overcome bad weather and poor resolution in a single go. In this paper, a novel deep learning architecture named EDCGAN is proposed. The proposed architecture is an encoder-decoder-based conditional GAN that uses multi-scale attentive discrimination to get accurate SAR to RGB image translation. In addition, we have used residual connections, spatial & channel-wise attention for better feature representation. A set of extensive experimentations show that this architecture outperforms the existing state-of-the-art method in terms of PSNR, SSIM, and FSIM_c values for the WHU-SENCity dataset.

关键词： Conditional GAN

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TransDocUNet: A Transformer-based UNet Architecture for Degraded Document image Binarization 14

TransDocUNet: A Transformer-based UNet Architecture for Degr...

引用

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

作者： Biswas, Risab Sarkhel, Soumik Roy, Swalpa Kumar Pal, Umapada Optiks Innovations Pvt. Ltd. Maharashtra Mumbai India Alipurduar Government Engineering and Management College West Bengal India Indian Statistical Institute West Bengal Kolkata India

ISBN: (纸本)9798400716256

The enhancement of historical document images is critical for improving the quality and legibility of scanned or captured document images. Convolutional-based techniques previously generated competitive results for document image binarization, however, due to their inherent locality, these models are often limited in explicitly expressing long-range dependency. Transformers (ViT) have evolved as an alternative design with a global self-attention mechanism to tackle this issue, however, they can result in restricted localization capabilities due to a lack of low-level details. To address this problem, we propose TransDocUNet, a CNN-Transformer hybrid UNet architecture for document image binarization that merits both attention and convolution capabilities in a U-Net architecture and serves as a strong alternative to the existing solutions. The experimental results, obtained using the DIBCO/H-DIBCO datasets, highlight that our proposed method outperforms all the existing competing methods in terms of both objective quality metrics and visual quality assessment, achieving state-of-the-art performance in document image binarization. In addition, we undertake an ablation study to understand the role of dilation in the CNN to capture feature dependencies while reducing the computational cost as well. The findings helped us arrive at the final model and provide valuable insights into the importance of acquiring both global and local contextual information for tasks like enhancing document images. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： image enhancement

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TARSNet: Topology Aware Root Segmentation Network for plant phenotyping 13

TARSNet: Topology Aware Root Segmentation Network for plant ...

引用

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

作者： Bhugra, Swati Mukherjee, Prerana Kaushik, Vinay Jha, Ritika Lall, Brejesh Chaudhury, Santanu Department of Electrical Engineering Indian Institute of Technology Delhi India School of Engineering Jawaharlal Nehru University India Department of Computer Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9781450398237

Root morphological traits are key to monitoring plant growth and development. Traditionally, plant biologists relied on manual or semi-Automatic approaches to accurately estimate these traits. With high-Throughput acquisition of root image data, the computation of these root traits is currently achieved with automatic image analysis, and in this context, root segmentation is an important pre-processing step. However, this is a challenging task because of (1) diverse root characteristics i.e orientation, size and shape, (2) complex image background, (3) low contrast and (4) varying degrees of self-occlusion. Deep learning methods proposed for root segmentation have mainly focused on conventional pixel-wise losses. In addition, they neglected the relationship between deep features which is crucial for segmentation of thin root structures in the presence of complex backgrounds such as water droplets and leaves. In this paper, we propose a novel attention based framework that combines the strength of feature attention, topological loss and residual learning for root segmentation. The proposed framework has reached state-of-The-Art performance on Arabidopsis Root Segmentation Challenge 2021 dataset from computer vision in Plant Phenotyping and Agriculture (CVPPA). An ablation study has also been conducted to evaluate the contribution of each module to the proposed framework. © 2022 ACM.

关键词： Semantic Segmentation

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A Novel Multi-Scale Residual Dense Dehazing Network (MSRDNet) for Single image Dehazingg 13

A Novel Multi-Scale Residual Dense Dehazing Network (MSRDNet...

引用

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

作者： Manu, Chippy M. Sreeni, K.G. Computer Vision Lab Dept.Electronics and Communication College of Engineering Affiliated to APJA K.T.U Kerala Trivandrum India

ISBN: (纸本)9781450398237

Dehazing is a difficult process because of the damage caused by the non-uniform fog and haze distribution in images. To address these issues, a Multi-Scale Residual dense Dehazing Network (MSRDNet) is proposed in this paper. A Contextual feature extraction module (CFM) for extracting multi-scale features and an Adaptive Residual Dense Module (ARDN) are used as sub-modules of MSRDNet. Moreover, all the hierarchical features extracted by each ARDN are fused, which helps to detect hazy maps of varying lengths with multi-scale features. This framework outperforms the state-of-the-art dehazing methods in removing haze while maintaining and restoring image detail in real-world and synthetic images captured under various scenarios. © 2022 ACM.

关键词： image reconstruction

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Fine-grain Cluster Estimation of Land Cover Classes using Landsat 8 Multispectral images 23

Fine-grain Cluster Estimation of Land Cover Classes using La...

引用

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

作者： Shankho Subhra Pal Ankita Chatterjee Jayanta Mukhopadhyay Sudeshna Sarkar Partha Pratim Das Indian Institute of Technology Kharagpur India

ISBN: (纸本)9798400716256

关键词： Clustering computer vision Hierarchy image processing Land Cover Classification Landsat Multi-spectral image Remote Sensing

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EEG2image: image Reconstruction from EEG Brain Signals 48

EEG2IMAGE: Image Reconstruction from EEG Brain Signals

引用

48th IEEE International conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Singh, Prajwal Pandey, Pankaj Miyapuram, Krishna Raman, Shanmuganathan Indian Institute of Technology Cvig Lab Gandhinagar India Indian Institute of Technology Brain Lab Gandhinagar India

ISBN: (纸本)9781728163277

Reconstructing images using brain signals of imagined visuals may provide an augmented vision to the disabled, leading to the advancement of Brain-computer Interface (BCI) technology. The recent progress in deep learning has boosted the study area of synthesizing images from brain signals using Generative Adversarial Networks (GAN). In this work, we have proposed a framework for synthesizing the images from the brain activity recorded by an electroencephalogram (EEG) using small-size EEG datasets. This brain activity is recorded from the subject's head scalp using EEG when they ask to visualize certain classes of Objects and English characters. We use a contrastive learning method in the proposed framework to extract features from EEG signals and synthesize the images from extracted features using conditional GAN. We modify the loss function to train the GAN, which enables it to synthesize 128 × 128 images using a small number of images. Further, we conduct ablation studies and experiments to show the effectiveness of our proposed framework over other state-of-the-art methods using the small EEG dataset. © 2023 IEEE.

关键词： Electroencephalography

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indian Regional Sign Language Recognition 14

Indian Regional Sign Language Recognition

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

作者： Sujatha, C. Patil, Madhura K. Umadevi, F.M. Jain, Neha Badni, Akshata Angadi, Ranjeeta Gangishetty, Shankar KLE Technological University Hubli India IIIT Hyderabad India

ISBN: (纸本)9798400716256

Sign Language is a medium of communication in the Deaf and Hard of Hearing community (DHH community). According to WHO, there are approximately 63 million people in India, including 3.3 million from the state of Karnataka, with 0.3 million children belonging to DHH community. The sign language gestures vary globally, making communication difficult between the DHH community and others. Hence, Sign Language Recognition has gained significant attention in recent years. There is a limited publicly available dataset on indian Sign Language. In addition, the dataset for regional languages especially the Kannada dataset, is unavailable for the sign recognition tasks. Hence initially, in our work, we built a dataset of indian Regional Kannada Sign Language (IRKSL) for different levels of data at character, word, and sentence levels. We propose a method for recognizing the indian sign language and translating it into Kannada regional language. We demonstrate the proposed method on the IRKSL dataset and obtained an accuracy of 81.69% and 73.87% with and without feature augmentation, respectively, on the IRKSL dataset. We also evaluated our work on the INCLUDE 50 dataset and obtained a better accuracy of 93.42% as compared to the state-of-art method. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Audition

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