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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 1469 条记录，以下是111-120 订阅

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Variational Augmentation for Enhancing Historical Document image Binarizationg± 13

Variational Augmentation for Enhancing Historical Document I...

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

作者： Dey, Avirup Nasipuri, Prof. Mita Das, Nibaran Jadavpur University West Bengal Kolkata India

ISBN: (纸本)9781450398237

Historical Document image Binarization is a well-known seg-mentation problem in image processing.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. © 2022 ACM.

关键词： image enhancement

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Single View Homography Estimation for an Inclined Textured Planar Surface: Overcoming the Inverse and Ill-Posed Challenge! 23

Single View Homography Estimation for an Inclined Textured P...

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

作者： Pooja Kumari Sukhendu Das IIT Madras IN Indian Institute of Technology Madras IN

ISBN: (纸本)9798400716256

Homography estimation is a crucial step in many computer vision problems involving the planar transformation of an image from one view to another. Generally, from two image views of a scene with a planar patch, one can compute the homography matrix (H) using Direct Linear Transformation (DLT) or its variants. In this study, we work on the inverse scenario to solve an ill-posed problem, where given only a single image (Math 1) of an inclined textured planar surface, we attempt to estimate both H and the orthogonal view (Math 2) of the planar surface. We propose to solve this problem of homography Estimation using an optimization framework. To the best of our knowledge, there has barely been any work done on this inverse scenario of homography estimation from only a single image view. The research presented in this paper successfully achieves image rectification and homography computation by assuming a perspective transformation on an upright planar patch when viewed by a camera placed exactly in front of it. A cost function with suitable constraints is utilized to refine homography estimation. Experimental results reveal the efficiency of the proposed approach in the case of images of checkerboard and other textured planar surfaces (real-world images with lines and other prominent textures).

关键词： DLT Homography LM image perspective optimization texture

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Low-light image enhancement based on variational Retinex model 13

Low-light image enhancement based on variational Retinex mod...

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13th International conference on Signal processing Systems, ICSPS 2021

作者： Li, Xuesong Zhang, Hongyi Pan, Jinfeng Li, Qilei Zou, Guofeng Gao, Mingliang School of Electrical and Electronic Engineering Shandong University of Technology Zibo255000 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

ISBN: (数字)9781510653184

ISBN: (纸本)9781510653177

The low-light image enhancement plays a crucial role in computer vision and multimedia applications. However, it is still a challenging task, as the degraded images reduce the visual naturalness and visibility. To address this problem, we build a novel variational Retinex model to accurately estimate the illumination and reflectance components. The illumination and reflectance are jointly updated by alternating optimization algorithm. Experimental results on several public datasets demonstrate that the proposed method outperforms the state-of-the-art methods in Retinex decomposition and illumination adjustment. © COPYRIGHT SPIE.

关键词： image enhancement

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I’m GROOT: a multi head multi GRaph netwOrk recognizing surgical actiOn Triplets✱ 22

I’m GROOT: a multi head multi GRaph netwOrk recognizing sur...

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

作者： Raviteja Sista Rachana Sathish Riya Agrawal Utpal De Partha Chakrabarti Debdoot Sheet Indian Institute of Technology Kharagpur IN Nrs IN NRS Medical College IN

ISBN: (纸本)9781450398220

Laparoscopic cholecystectomy is a widely performed minimally invasive surgical procedure that imposes many challenges to the operating surgeon. While we strive to understand and automate such surgeries, the key is to identify the actions involved in it. An action involves a set of tools and a target anatomy, together forming the action triplets. However, the relations between the triplets and their constituents are sparse, making it challenging to learn their relations. In this paper, we propose a graph neural network based approach to exploit these underlying sparse relations in the data. We portray the proposed method’s ability to uniformly learn multiple tasks and classify triplets with an mAP of 0.261. In addition, we experimentally show the inability of fully connected and convolution layers to learn these sparse relations when trained on 40 laparoscopic videos and validated using five videos. Codes will be available at : https://***/iitkliv/groot.

关键词： Sparse relations Graph neural networks Multi task learning Surgical action triplets Laparoscopic cholecystectomy

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ContextCLIP: Contextual Alignment of image-Text pairs on CLIP visual representations 22

ContextCLIP: Contextual Alignment of Image-Text pairs on CLI...

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

作者： Chanda Grover Indra Deep Mastan Debayan Gupta Ashoka University India The LNM Institute of Information Technology Jaipur India

ISBN: (纸本)9781450398220

State-of-the-art empirical work has shown that visual representations learned by deep neural networks are robust in nature and capable of performing classification tasks on diverse datasets. For example, CLIP demonstrated zero-shot transfer performance on multiple datasets for classification tasks in a joint embedding space of image and text pairs. However, it showed negative transfer performance on standard datasets, e.g., BirdsNAP, RESISC45, and MNIST. In this paper, we propose ContextCLIP, a contextual and contrastive learning framework for the contextual alignment of image-text pairs by learning robust visual representations on Conceptual Captions dataset. Our framework was observed to improve the image-text alignment by aligning text and image representations contextually in the joint embedding space. ContextCLIP showed good qualitative performance for text-to-image retrieval tasks and enhanced classification accuracy. We evaluated our model quantitatively with zero-shot transfer and fine-tuning experiments on CIFAR-10, CIFAR-100, Birdsnap, RESISC45, and MNIST datasets for classification task.

关键词： Alignment

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ViLP: Knowledge Exploration using vision, Language, and Pose Embeddings for Video Action Recognition 23

ViLP: Knowledge Exploration using Vision, Language, and Pose...

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

作者： Soumyabrata Chaudhuri Saumik Bhattacharya IIT Bhubaneswar IN E&ECE Indian Institute of Technology Kharagpur IN

ISBN: (纸本)9798400716256

Video Action Recognition (VAR) is a challenging task due to its inherent complexities. Though different approaches have been explored in the literature, designing a unified framework to recognize a large number of human actions is still a challenging problem. Recently, Multi-Modal Learning (MML) has demonstrated promising results in this domain. In literature, 2D skeleton or pose modality has often been used for this task, either independently or in conjunction with the visual information (RGB modality) present in videos. However, the combination of pose, visual information, and text attributes has not been explored yet, though text and pose attributes independently have been proven to be effective in numerous computer vision tasks. In this paper, we present the first pose augmented vision-language model (VLM) for VAR. Notably, our scheme achieves an accuracy of 92.81% and 73.02% on two popular human video action recognition benchmark datasets, UCF-101 and HMDB-51, respectively, even without any video data pre-training, and an accuracy of 96.11% and 75.75% after kinetics pre-training.

关键词： Action recognition vision-language model multimodal training

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MCDNet: Multi Context Dense Network for multi-frame super resolution of satellite images 23

MCDNet: Multi Context Dense Network for multi-frame super re...

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

作者： Avinash Chouhan Harsh Motwani 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)9798400716256

Satellite image super resolution is an important task that generates high resolution satellite images from low resolution inputs. Multi-frame super resolution utilizes multiple low-resolution images to generate a single high-resolution image. Multi-frame super resolution methods face difficulty in handling spatial and temporal dependencies of pixels. In this work, we proposed a novel architecture named Multi-context Dense Network (MCDNet) to handle spatial and temporal pixel dependencies using multiple approaches of global average pooling, multiple size kernels, and self-attention. The proposed approach improved the PSNR values by 0.29 % and 0.001 % for super resolution of NIR and RED bands on the benchmark PROBA-V dataset.

关键词： Multi-context features

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MMAG: Mutually Motivated Attention Gates for Simultaneous Extraction of Contextual and Spatial Information from a Monocular image 14

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

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

作者： Singh, Aditya Mohan, Saloni Kushwaha, Vandana Nandi, G.C. Center of Intelligent Robotics Indian Institute of Information Technology Allahabad Uttar Pradesh Prayagraj India

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

关键词： Semantic Segmentation

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Convolutional Ensembling based Few-Shot Defect Detection Technique 22

Convolutional Ensembling based Few-Shot Defect Detection Tec...

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

作者： Soumyajit Karmakar Abeer Banerjee Prashant Gidde Sumeet Saurav Sanjay Singh Computer Science and Engineering Indian Institute of Information Technology Guwahati IN Advanced Information Technologies Group CSIR-CEERI IN Advanced Information Technologies Group CSIR-CEERI Pilani India

ISBN: (纸本)9781450398220

Over the past few years, there has been a significant improvement in the domain of few-shot learning. This learning paradigm has shown promising results for the challenging problem of anomaly detection, where the general task is to deal with heavy class imbalance. Our paper presents a new approach to few-shot classification, where we employ the knowledge base of multiple pre-trained convolutional models that act as the backbone for our proposed few-shot framework. Our framework uses a novel ensembling technique for boosting the accuracy while drastically decreasing the total parameter count, thus paving the way for real-time implementation. We perform an extensive hyperparameter search using a power-line defect detection dataset and obtain an accuracy of 92.30% for the 5-way 5-shot task. Without further tuning, we evaluate our model on competing standards with the existing state-of-the-art methods and outperform them.

关键词： Few-Shot Classification Anomaly Detection Ensemling Strategy

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MoMSGAN: Mode Collapse based Degradation Agnostic Multi-Scale Super-Resolution of Medical images 14

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

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

作者： Dey, Samiran Chakraborti, Tapabrata Basuchowdhuri, Partha Mitra, Debasis Augustine, Robin Saha, Sanjoy Kumar Indian Association for the Cultivation of Science India The Alan Turing Institute University College London United Kingdom Indian Institute of Engineering Science and Technology 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. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： computerized tomography

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