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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211-220 订阅

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7th International conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

7th International Conference on Information and Communicatio...

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7th International conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

ISBN: (纸本)9789819940394

The proceedings contain 45 papers. The special focus in this conference is on . The topics include: Skin Cancer Recognition Using CNN, VGG16 and VGG19;diagnosis of Cardiovascular Disease Using Machine Learning Algorithms and Feature Selection Method for Class Imbalance Problem;similarity Based Answer Evaluation in Academic Questions Using Natural Language processing Techniques;fake News Detection Using Machine Learning and Deep Learning Classifiers;survey on Pre-Owned Car Price Prediction Using Random Forest Algorithm;sentiment Analysis of Youtube Comment Section in indian News Channels;deep Learning Framework for Speaker Verification Under Multi Sensor, Multi Lingual and Multi Session Conditions;DLLACC: Design of an Efficient Deep Learning Model for Identification of Lung Air Capacity in COPD Affected Patients;Content Based Document image Retrieval Using computer vision and AI Techniques;drowsiness Detection System;Monitor the Effectiveness of Cardiovascular Disease Illness Diagnostics Utilizing AI and Supervised Machine Learning Classifiers;architecture Based Classification for Intrusion Detection System Using Artificial Intelligence and Machine Learning Classifiers;a Novel Privacy-Centric Training Routine for Maintaining Accuracy in Traditional Machine Learning Systems;outside the Closed World: On Using Machine Learning for Network Intrusion Detection;data Collection for a Machine Learning Model to Suggest Gujarati Recipes to Cardiac Patients Using Gujarati Food and Fruit with Nutritive Values;plant and Weed Seedlings Classification Using Deep Learning Techniques;a Comprehensive Review on Various Artificial Intelligence Based Techniques and Approaches for Cyber Security;applicability of Machine Learning for Personalized Medicine;I-LAA: An Education Chabot;A Comparison of Machine Learning Approaches for Forecasting Heart Disease with PCA Dimensionality Reduction.

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Enhanced multi-stage network for defocus deblurring using dual-pixel images 13

Enhanced multi-stage network for defocus deblurring using du...

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13th International conference on Signal processing Systems, ICSPS 2021

作者： Li, Ru Xie, Junwei Xue, Yuyang Zou, Wenbin Tong, Tong Luo, Ming Gao, Qinquan College of Physics and Information Engineering Fuzhou University China Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology Fuzhou University China Imperial Vision Technology Fujian China Department of Computer Science University of Tsukuba Tsukuba Japan Fujian Provincial Key Laboratory of Photonics Technology Fujian Normal University China

ISBN: (数字)9781510653184

ISBN: (纸本)9781510653177

The defocus deblurring raised from the finite aperture size and exposure time is an essential problem in the shooting process, which seriously affects the quality of the images. However, studies based on defocus deblurring in monocular images yielded good results, while those on binocular images are rare. The current methods directly merge the left and right views regardless of their unique features. Objects within the camera's DoF will not have a difference in phase, while light rays from outside the DoF will have a relative shift that is directly correlated with the amount of defocus blur. In this paper, we firstly proposed an enhanced multi-stage network for defocus deblurring using dual-pixel images. Taking into account the parallax between the left and right views, the first two stages learn the information of them, respectively, and correct the deviation of the images under the supervision of the ground truth. The third stage consists of EERG and ERGS. It merges with the feature map of the previous stage, so that the left and right views are mutually enhanced, and a good restored image is obtained. ERGS uses the residual block as the basic unit to restore the details of the blurred area while maintaining the clear. Experimental results show that our proposed network can achieve better accuracy than state-of-the-art approaches on the public DPD dataset. © COPYRIGHT SPIE.

关键词： Geometrical optics

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Spatially-Adaptive image Restoration using Distortion-Guided Networks 18

Spatially-Adaptive Image Restoration using Distortion-Guided...

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18th IEEE/CVF International conference on computer vision (ICCV)

作者： Purohit, Kuldeep Suin, Maitreya Rajagopalan, A. N. Boddeti, Vishnu Naresh Michigan State Univ E Lansing MI 48824 USA Indian Inst Technol Madras Chennai Tamil Nadu India

ISBN: (纸本)9781665428125

We present a general learning-based solution for restoring images suffering from spatially-varying degradations. Prior approaches are typically degradation-specific and employ the same processing across different images and different pixels within. However, we hypothesize that such spatially rigid processing is suboptimal for simultaneously restoring the degraded pixels as well as reconstructing the clean regions of the image. To overcome this limitation, we propose SPAIR, a network design that harnesses distortion-localization information and dynamically adjusts computation to difficult regions in the image. SPAIR comprises of two components, (1) a localization network that identifies degraded pixels, and (2) a restoration network that exploits knowledge from the localization network in filter and feature domain to selectively and adaptively restore degraded pixels. Our key idea is to exploit the non-uniformity of heavy degradations in spatial-domain and suitably embed this knowledge within distortion-guided modules performing sparse normalization, feature extraction and attention. Our architecture is agnostic to physical formation model and generalizes across several types of spatially-varying degradations. We demonstrate the efficacy of SPAIR individually on four restoration tasks-removal of rain-streaks, raindrops, shadows and motion blur. Extensive qualitative and quantitative comparisons with prior art on 11 benchmark datasets demonstrate that our degradation-agnostic network design offers significant performance gains over state-of-the-art degradation-specific architectures. Code available at https://***/humananalysis/spatially-adaptive-image-restoration.

关键词： Pixels

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Performance, Trust, or both? COVID-19 Diagnosis and Prognosis using Deep Ensemble Transfer Learning on X-ray imagesg± 13

Performance, Trust, or both? COVID-19 Diagnosis and Prognosi...

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

作者： Tiwari, Abhishek Singh, Rajeev Kumar Department of Computer Science and Engineering Shiv Nadar University Gautam Buddha Nagar UP Delhi India

ISBN: (纸本)9781450398237

The COVID-19 pandemic still affects most parts of the world today. Despite a lot of research on diagnosis, prognosis, and treatment, a big challenge today is the limited number of expert radiologists who provide diagnosis and prognosis on X-Ray images. Thus, to make the diagnosis of COVID-19 accessible and quicker, several researchers have proposed deep-learning-based Artificial Intelligence (AI) models. While most of these proposed machine and deep learning models work in theory, they may not find acceptance among the medical community for clinical use due to weak statistical validation. For this article, radiologists' views were considered to understand the correlation between the theoretical findings and real-life observations. The article explores Convolutional Neural Network (CNN) classification models to build a four-class viz. "COVID-19", "Lung Opacity", "Pneumonia", and "Normal"classifiers, which also provide the uncertainty measure associated with each class. The authors also employ various pre-processing techniques to enhance the X-Ray images for specific features. To address the issues of over-fitting while training, as well as to address the class imbalance problem in our dataset, we use Monte Carlo dropout and Focal Loss respectively. Finally, we provide a comparative analysis of the following classification models - ResNet-18, VGG-19, ResNet-152, MobileNet-V2, Inception-V3, and EfficientNet-V2, where we match the state-of-the-art results on the Open Benchmark Chest X-ray datasets, with a sensitivity of 0.9954, specificity of 0.9886, the precision of 0.9880, F1-score of 0.9851, accuracy of 0.9816, and receiver operating characteristic (ROC) of the area under the curve (AUC) of 0.9781 (ROC-AUC score). © 2022 ACM.

关键词： Coronavirus

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MSDNet: A novel multi-stage progressive image dehazing network 21

MSDNet: A novel multi-stage progressive image dehazing netwo...

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

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

ISBN: (纸本)9781450391276

This paper presents a novel algorithm to dehaze a given hazy input image using a Multi-Stage progressive image Dehazing Network (MSDNet) architecture. The proposed multi-stage strategy framework splits the challenge of image recovery into sub-tasks for a degraded image to be gradually restored. The network is trained with images from two benchmark datasets - RESIDE and NTIRE 2021. Experimental evaluations have been conducted with several hazy images from different datasets. The performance evaluation of MSDNet is done using various metrics such as Peak-Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), Feature Similarity Metric (FSIM), Visibility Index (VI), Realness Index (RI), Fog Aware Density Evaluator (FADE), Naturalness image Quality Evaluator (NIQE) and Blind/Referenceless image Spatial Quality Evaluator (BRISQUE). An average PSNR of 27.687, 36.25, 34.98, 20.52, and 20.97 have been obtained with images from ITS, SOTS indoor, SOTS outdoor, Dense-Haze, and NH-Haze datasets, respectively. The experimental results reveals the enhanced performance of MSDNet compared to other state-of-the-art techniques. © 2021 ACM.

关键词： image reconstruction

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An Information Multiplexed Encoder-Decoder Network for image Captioning in Hindi

An Information Multiplexed Encoder-Decoder Network for Image...

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IEEE International conference on Systems, Man, and Cybernetics (SMC)

作者： Mishra, Santosh Kumar Peethala, Mahesh Babu Saha, Sriparna Bhattacharyya, Pushpak Indian Inst Technol Patna Dept Comp Sci & Engn Patna Bihar India Indian Inst Technol Mumbai Maharashtra India

ISBN: (纸本)9781665442077

image captioning is a multi-modal problem linking computer vision and natural language processing, which combines image analysis and text generation challenges. In the literature, most of the image captioning works have been accomplished in the English language only. This paper proposes a new approach for image captioning in the Hindi language using deep learning-based encoder-decoder architecture. Hindi, widely spoken in India and South Asia, is the fourth most spoken language globally;it is India's official language. In recent years, significant advancement has been made in image captioning, utilizing encoder-decoder architectures based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Encoder CNN extracts features from input images, whereas decoder RNN performs language modeling. The proposed encoder-decoder architecture utilizes information multiplexing in the encoder CNN to achieve a performance gain in feature extraction. Extensive experimentation is carried out on the benchmark MSCOCO Hindi dataset, and significant improvements in BLEU score are reported compared to the baselines. Manual human evaluation in terms of adequacy and fluency of the generated captions further establishes the proposed method's efficacy in generating good quality captions.

关键词： Recurrent neural networks

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Contrastive Multi-View Textual-Visual Encoding: Towards One Hundred Thousand-Scale One-Shot Logo Identificationg 13

Contrastive Multi-View Textual-Visual Encoding: Towards One ...

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

作者： Sharma, Nakul Penamakuri, Abhirama Subramanyam V B Mishra, Anand Indian Institute of Technology Rajasthan Jodhpur India

ISBN: (纸本)9781450398237

In this paper, we study the problem of identifying logos of business brands in natural scenes in an open-set one-shot setting. This problem setup is significantly more challenging than traditionally-studied gclosed-set' and glarge-scale training samples per category' logo recognition settings. We propose a novel multi-view textual-visual encoding framework that encodes text appearing in the logos as well as the graphical design of the logos to learn robust contrastive representations. These representations are jointly learned for multiple views of logos over a batch and thereby they generalize well to unseen logos. We evaluate our proposed framework for cropped logo verification, cropped logo identification, and end-to-end logo identification in natural scene tasks;and compare it against state-of-the-art methods. Further, the literature lacks a gvery-large-scale' collection of reference logo images that can facilitate the study of one-hundred thousand-scale logo identification. To fill this gap in the literature, we introduce Wikidata Reference Logo Dataset (WiRLD), containing logos for 100K business brands harvested from Wikidata. Our proposed framework that achieves an area under the ROC curve of 91.3% on the QMUL-OpenLogo dataset for the verification task, outperforms state-of-the-art methods by 9.1% and 2.6% on the one-shot logo identification task on the Toplogos-10 and the FlickrLogos32 datasets, respectively. Further, we show that our method is more stable compared to other baselines even when the number of candidate logos is on a 100K scale. © 2022 ACM.

关键词： Encoding (symbols)

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HSADML: Hyper-Sphere Angular Deep Metric Based Learning for Brain Tumor Classification 12th

HSADML: Hyper-Sphere Angular Deep Metric Based Learning for ...

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

作者： Verma, Aman Singh, Vibhav Prakash Department of Electronics and Communications National Institute of Technology Raipur Raipur India Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Prayagraj India

ISBN: (纸本)9789811941351

Brain Tumors are an abnormal mass of clustered cells penetrating regions of the brain. Their timely identification and classification help doctors to provide appropriate treatment. However, Classification of Brain Tumors is quite intricate because of high-intra-class similarity and low-inter- class variability. Due to morphological similarity amongst various MRI-Slices of different classes the challenge deepens more. This all leads to hampering the generalizability of classification models. To this end, this paper proposes HSADML, a novel framework which enables Deep Metric Learning (DML) using SphereFace Loss. SphereFace loss embeds the features into a hyperspheric-manifold and then imposes margin on the embedding to enhance differentiability between the classes. With the utilization of SphereFace loss-based deep metric learning it is ensured that samples from class are clustered together while the different ones are pushed apart. Results reflect the prominence of the approach, the proposed framework achieved state-of-the-art 98.69% validation accuracy using k-NN (k = 1) and this is significantly higher than normal Soft Max Loss training which though obtains 98.47% validation accuracy but that too with limited inter-class reparability and intra-class closeness. Experimental analysis is done over various classifiers and loss function settings suggest potential in the approach. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Brain

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Automated tree generation using grammar & particle system 21

Automated tree generation using grammar & particle system

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

作者： Jain, Aryamaan Sunkara, Jyoti Shah, Ishaan Sharma, Avinash Rajan, K.S. IIIT Hyderabad India

ISBN: (纸本)9781450391276

Trees are an integral part of many outdoor scenes and are rendered in a wide variety of computer applications like computer games, movies, simulations, architectural models, AR and VR. This has led to increasing demand for realistic, intuitive, lightweight and easy to produce computer-generated trees. The current approaches at 3D tree generation using a library of trees lack variations in structure and are repetitive. This paper presents an extended grammar-based automated solution for 3D tree generation that can model a wide range of species, both Western and indian. For the foliage, we adopt a particle system approach that models the leaf, its size, orientation and changes. The proposed solution additionally allows control for individual trees, thus modelling the tree growth variations, changes in foliage across seasons, and leaf structure. This enables the generation of virtual forests with different tree compositions. In addition, a Blender add-on has been developed for use and will be released. © 2021 ACM.

关键词： computer games

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Towards interpretable facial emotion recognition 21

Towards interpretable facial emotion recognition

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

作者： Malik, Sarthak Kumar, Puneet Raman, Balasubramanian Indian Institute of Technology Roorkee India

ISBN: (纸本)9781450391276

In this paper, an interpretable deep-learning-based system has been proposed for facial emotion recognition. A novel approach to interpret the proposed system's results, Divide & Conquer based Shapley additive explanations (DnCShap), has also been developed. The proposed approach computes 'Shapley values' that denote the contribution of each image feature towards a particular prediction. The Divide and Conquer algorithm has been incorporated for computing the Shapley values in linear time instead of the exponential time taken by the existing interpretability approaches. The experiments performed on four facial emotion recognition datasets, i.e., FER-13, FERG, JAFFE, and CK+, resulted in the emotion classification accuracy of 62.62%, 99.68%, 91.97%, and 99.67%, respectively. The results show that DnCShap has consistently interpreted the highly relevant facial features for the emotion classification for various datasets. © 2021 ACM.

关键词： Emotion Recognition

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