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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111-120 订阅

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Low Resource Degraded Quality Document image Binarization - Domain Adaptation is the Way 13

Low Resource Degraded Quality Document Image Binarization - ...

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

作者： Kundu, Ahana Bhattacharya, Ujjwal Computer Vision and Pattern Recognition Indian Statistical Institute West Bengal Kolkata India

ISBN: (纸本)9781450398237

Usually, image binarization plays a crucial role in automatic analysis of degraded documents from their captured images. However, this binarization task is often difficult due to a number of reasons including the high similarity between noisy background and faded foreground pixels. The study presented here is particularly focused on binarization of images of low-resource degraded quality documents based on a set of recently collected image samples of several rare, ancient and severely degraded quality printed documents of Bangla, the 2nd and 5th most popular script of India and the world respectively. This new collection of degraded document image samples will henceforth be referred as 'ISIDDI2' and it consists of 139 images of Bangla old document pages. Samples of 'ISIDDI', another existing database of degraded Bangla document image samples, have also been used in the present study. A novel deep architecture based on attention UNET++ with dilated convolution operation is proposed for this binarization task. The model is optimized using human vision perceptible distance reciprocal distortion (DRD) loss. Since the binarization ground truth of samples of both 'ISIDDI2' and 'ISIDDI' are not available, the proposed network has been trained using samples of DIBCO and H-DIBCO datasets and an unsupervised domain adaptation (DA) module is employed for adaptation of the proposed architecture to the degradation patterns of 'ISIDDI2' or 'ISIDDI' samples. The proposed binarization strategy includes certain post-processing operation based on a modified k-neighbourhood based approach for recovery of broken characters. Results of our extensive experimentation show that the proposed binarization strategy has improved the binarization output of state-of-the-art methods on both ISIDDI2 and ISIDDI datasets. Also, its performance on well-known DIBCO samples is satisfactory. © 2022 ACM.

关键词： Network architecture

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Controllable image Synthesis via Feature Mask Coupling using Implicit Neural Representationg± 13

Controllable Image Synthesis via Feature Mask Coupling using...

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

作者： Gupta, Sonam Keshari, Arti Das, Sukhendu Indian Institute of Technology Madras Tamil Nadu Chennai India

ISBN: (纸本)9781450398237

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. © 2022 ACM.

关键词： Generative adversarial networks

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TransDocUNet: A Transformer-based UNet Architecture for Degraded Document image Binarization 23

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

引用

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

作者： Risab Biswas Soumik Sarkhel Swalpa Kumar Roy Umapada Pal Artificial Intelligence Group Optiks Innovations Pvt. Ltd. (P360) India Department of Computer Science and Engineering Alipurduar Government Engineering and Management College India Computer Vision and Pattern Recognition Unit Indian Statistical Institute 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.

关键词： Binarization CNN Deep Learning Document image Enhancement TransUNet UNet vision Transformer

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C-SAW: Self-Supervised Prompt Learning for image Generalization in Remote Sensing 23

C-SAW: Self-Supervised Prompt Learning for Image Generalizat...

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

作者： Avigyan Bhattacharya Mainak Singha Ankit Jha Biplab Banerjee Indian Institute of Technology Bombay IN

ISBN: (纸本)9798400716256

We focus on domain and class generalization problems in analyzing optical remote sensing images, using the large-scale pre-trained vision-language model (VLM), CLIP. While contrastively trained VLMs show impressive zero-shot generalization performance, their effectiveness is limited when dealing with diverse domains during training and testing. Existing prompt learning techniques overlook the importance of incorporating domain and content information into the prompts, which results in a drop in performance while dealing with such multi-domain data. To address these challenges, we propose a solution that ensures domain-invariant prompt learning while enhancing the expressiveness of visual features. We observe that CLIP’s vision encoder struggles to identify contextual image information, particularly when image patches are jumbled up. This issue is especially severe in optical remote sensing images, where land-cover classes exhibit well-defined contextual appearances. To this end, we introduce C-SAW, a method that complements CLIP with a self-supervised loss in the visual space and a novel prompt learning technique that emphasizes both visual domain and content-specific features. We keep the CLIP backbone frozen and introduce a small set of projectors for both the CLIP encoders to train C-SAW contrastively. Experimental results demonstrate the superiority of C-SAW across multiple remote sensing benchmarks and different generalization tasks.

关键词： vision-language models

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A Novel Framework for Robust Fingerprint Representations using Deep Convolution Network with Attention Mechanism 14

A Novel Framework for Robust Fingerprint Representations usi...

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

作者： Ranjan, Alka Prakash, Nishkal Peddi, Santhoshkumar Samanta, Debasis Indian Institute of Technology Kharagpur West Bengal 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. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Convolution

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A Globally-Connected and Trainable Hierarchical Fine-Attention Generative Adversarial Network based Adversarial Defense 13

A Globally-Connected and Trainable Hierarchical Fine-Attenti...

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

作者： Kumar, Wahengbam Kanan Paidimarri, Manoj Sur, Arijit Department of Computer Science and Engineering Indian Institute of Technology Guwahati Assam Guwahati India

ISBN: (纸本)9781450398237

Deep Neural Network (DNN) inferences have been proven highly susceptible to carefully engineered adversarial perturbations, presenting a pivotal hindrance to real-world computer vision tasks. Most of the existing defenses have poor generalization ability due to their dependence on relatively limited Adversarial Examples (AE). Furthermore, the existing adversarial training necessitates continually retraining a target network with the sort of attack required to be repelled. The defense strategies that are primarily based on processing the perturbed image eventually fall short when pitted against constantly developing threats. Protection of DNN against adversarial attacks remains a difficult challenge on challenging datasets such as Fashion MNIST and CIFAR10. This paper proposes a GAN-based two-stage adversarial training model named Globally Connected and Trainable Hierarchical Fine Attention (GCTHFA). The first stage of the proposed GCTHFA GAN is to create a reconstructed image that is a purified version of an adversarial example. The proposed approach has used a trainable and globally linked attention map to teach the Generator about the different types of representations an image might have in different convolutional layers located at different levels in a network. The discriminator's reliance on feature vectors produced by transfer learning eliminates the traditional dependency on standard image pixels. The second step involves adversarial training of a target classifier to provide resistance to such attacks. Extensive testing on the MNIST, Fashion MNIST, and CIFAR10 datasets with different classifiers and attacks show that the proposed model can handle adversarial attack settings for various target models. The proposed model uses only one type of adversarial training, with no requirement for retraining based on the type of attack. © 2022 ACM.

关键词： Generative adversarial networks

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A Novel Approach for Neuromorphic vision Data Compression based on Deep Belief Network 23

A Novel Approach for Neuromorphic Vision Data Compression ba...

引用

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

作者： Sally Khaidem Abhipraay Nevatia Mansi Sharma Department of Electrical Engineering Indian Institute of Technology Madras IN Department of Mechanical Engineering Indian Institute of Technology Madras IN

ISBN: (纸本)9798400716256

A neuromorphic camera is an image sensor that emulates the human eyes capturing only changes in local brightness levels. They are widely known as event cameras, silicon retinas or dynamic vision sensors (DVS). DVS records asynchronous per-pixel brightness changes, resulting in a stream of events that encode the time, location, and polarity of brightness change. DVS consumes little power and can capture a wider dynamic range with no motion blur and higher temporal resolution than conventional frame-based cameras. Despite yielding a lower bit rate compared to conventional video capture, the present approach of event capture demonstrates enhanced compressibility. Hence, we introduce a novel deep learning-based compression methodology tailored for event data. The proposed technique employs a deep belief network (DBN) to condense the high-dimensional event data into a latent representation, which is subsequently encoded utilising an entropy-based coding method. Notably, our proposed scheme represents one of the initial endeavours to integrate deep learning methodologies for event compression. It achieves a high compression ratio while maintaining good reconstruction quality outperforming state-of-the-art event data coders and other lossless benchmark techniques.

关键词： Entropy coding deep belief network dynamic vision sensor. neuromorphic computing

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Knowledge Distillation with Ensemble Calibration 23

Knowledge Distillation with Ensemble Calibration

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

作者： Ishan Mishra Riyanshu Jain Dhruv Viradiya Divyam Patel Deepak Mishra Computer Science and Engineering Indian Institute of Technology Jodhpur India Computer Science and Engineering Indian Institute of Technology Jodhpur India Electrical Engineering 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.

关键词： Augmentation Confidence Calibration Ensemble Knowledge Distillation

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EDCGAN: Encoder Decoder based Conditional GAN for SAR to Optical image translation g ± 13

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

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

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

ISBN: (纸本)9781450398237

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 and 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. © 2022 ACM.

关键词： Synthetic aperture radar

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Mandala as computational art: Vectorization and beyond 23

Mandala as computational art: Vectorization and beyond

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

作者： Tusita Sarkar Partha Bhowmick Department of Computer Science and Engineering IIT Kharagpur India

ISBN: (纸本)9798400716256

This paper introduces a novel technique of computational art with mandala—an iconic heritage of indian folk art. Its novelty lies in several fundamental steps. The first one is fixing the asymmetries and the imperfections in a hand-drawn piece of art based on the notion of a primitive map. The primitive map is described using a novel concept of geometric salience—a set of well-defined salient points on the frontier polygon of a primitive—characterizing the concavities and convexities present in the primitive. The primitive map is also used for the vectorization of a mandala and its succinct representation as a mandala sector graph (MSG), which eventually results in efficient graph operations on an existing artwork to create a new piece of art. The use of frontier polygons in different steps of the algorithm makes it robust and efficient. Experimental results on various datasets demonstrate the potential and versatility of the proposed technique.

关键词： Art and culture alpana art and psychology computational art digital art mandala vectorization.

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