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Attention guided complementary feature integration for latent image recovery from noisy/blurry pairs 21

Attention guided complementary feature integration for laten...

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

作者： Rosh, K.S. Green Lomte, Sachin Krishnan, Nikhil Prasad, B.H. Pawan Samsung Research Institute Karnataka Bangalore India

ISBN: (纸本)9781450391276

Low-light imaging using a hand-held camera is a challenging task due to excessive noise in the scene. Most of the existing methods try to address this problem either by denoising an image captured using high ISO or by deblurring an image captured using long exposure time. However these methods use a single image to estimate the latent scene and hence fail to leverage the complimentary information available in the scene. In this paper, we propose a method to estimate the latent image using a pair of images captured using high ISO and high exposure time respectively, to leverage the complimentary information present in the two captures. We propose a novel deep learning based method to efficiently extract and integrate the information present in the two images. Contrary to other methods, we use separate filters to extract the complimentary information from the two images. We also progressively integrate the extracted features using a novel attention-guided mechanism. Further, we address the spatially varying nature and localization of motion blur in real life captures by using spatial attention layers. the proposed method achieves state-of-the-art performance against single as well as other noisy/blurry approaches to the problem. We also show that the network learns spatial attention maps with strong correlation to the blur in the scene, and thus the proposed method is more interpretable and easier to analyze. © 2021 ACM.

关键词： Deep learning

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CDA-Net: computer vision based automated car damage analysis 14

CDA-Net: Computer vision based automated car damage analysis

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

作者： Kannan, Iyshwarya Ratthi Balasubramanian, Yogameena Saravana Permaal, S. Menaka, K. Ramesh, Sivasankari Thiagarajar College of Engineering Tamil Nadu Madurai India National Institute of Technical Teachers' Training and Research TamilNadu Chennai India Velammal Institute of Technology TamilNadu Chennai India

ISBN: (纸本)9798400716256

Car insurance claims are rising in tandem with the rising tide of car users. Every insurance claim requires an engineer's manual assessment and a surveyor's actual examination. this procedure can last anywhere from a few days to several weeks. Current deep-learning techniques have paved the way for this type of mechanization. Both the business and the client would benefit from a comprehensive system. To assess the damage cost for the insurance claim process, the make and model of the car, damaged parts, damage type, and severity of the damage are important parameters. We introduced two datasets, a piqued car make and model (CMM) dataset containing images of the most popular 23 car makes and 148 vehicle models available in the indian automotive market. the second dataset consists of 11,380 images collected from insurance offices and web resources of different types of car damage, including annotations. In addition, it provides an assessment of the damage to each part, the severity of the damage (dents, scratches, bent, broken, cracks, smashed, punched, and pushed), and the location of the damage (front, back, side). the assessment helps estimate the damage cost when combined with structured data. the proposed CDA_YOLOv5 car damage assessment framework outperformed existing state-of-the-art one-stage models with an average per-class accuracy of 87.36% and an overall accuracy of 90.45%. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Damage detection

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Worst-Case Adversarial Perturbation and Effect of Feature Normalization on Max-Margin Multi-label Classifiers 12th

Worst-Case Adversarial Perturbation and Effect of Feature No...

引用

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

作者： Gupta, Ritesh Kumar Verma, Yashaswi Pvt Ltd. Noida India IIT Jodhpur India

ISBN: (纸本)9789811941351

Multi-label classification is a generalization of single-label classification, where an unseen sample is automatically assigned a subset of semantically relevant labels from a given vocabulary. In parallel, recent research has demonstrated the impact of adversarial examples, which are modifications of original samples and aim at fooling machine learning models. Unlike existing adversary generation techniques which are specific to single-label data and mostly assume the availability of training data and/or model to the attacker, in this paper, we propose a generalized adversary generation mechanism by generating worst-case perturbation. this perturbation, when added to the feature vector of the original sample, generates an adversarial sample without the need for the availability of either training data or model to the attacker. Next, for the first time as per our knowledge, we study and demonstrate the effect of feature normalization as a defense mechanism against adversarial attacks. Extensive experiments show the effectiveness of our adversarial attack and defense mechanisms using state-of-the-art max-margin multi-label classification algorithms on two benchmark datasets. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image annotation

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Localization of curved scene text in video based on Fuzzy Curve Tracing of extracted planar surface 14

Localization of curved scene text in video based on Fuzzy Cu...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Boaz, Too Kipyego Prabhakar, C.J. Department of Computer Science Kuvempu University Shankaraghatta Shivamogga Karnataka577 415 India

ISBN: (纸本)1595930361

In this paper, we present a novel technique to localize curved multi-script text contained in natural scene video based on Fuzzy Curve Tracing (FCT) of extracted planar surface. In order to read and interpret easily, text information is usually written on planar surfaces, for instance billboards, walls of buildings, road-signs and banners. this motivated us to detect planar surfaces by fitting a planar model, that is constructed using Random Sample Consensus (RANSAC). It is assumed that the detected planar surface contains text and is segmental from background using Graph Cuts through Markov Random Field (MRF) labeling of pixels belongs to planar surface. Within the extracted planar surface, the curved text is detected using fuzzy curve tracing, which traces and generates curve path of the text by establishing spatial relations among the cluster centers identified through fuzzy c-means clustering of character regions. Finally, curved text is localized by identifying character regions wherever generated curve path pass through it. the experimental results are evaluated for text localization using recall, precision and f-measure. Based on these metrics result, it's incontestible that the projected technique outperforms the popular existing methods. Copyright 2014 ACM.

关键词： Markov processes

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A novel approach for computer aided diagnosis of schizophrenia using auditory oddball functional MRI 14

A novel approach for computer aided diagnosis of schizophren...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Juneja, Akanksha Rana, Bharti Agrawal, R.K. School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

ISBN: (纸本)1595930361

Schizophrenia is a serious mental illness that requires timely and accurate diagnosis. Functional magnetic resonance imaging (fMRI) helps in identifying variations in activation patterns of schizophrenia patients and healthy subjects. But, manual diagnosis using fMRI is cumbersome and prone to subjective errors. this has drawn the attention of pattern recognition and computer vision research community towards developing a reliable and efficient decision model for computer aided diagnosis (CAD) of schizophrenia. However, high dimensionality and limited availability of fMRI samples leads to curse-of-dimensionality which may deteriorate the performance of a decision model. In this research work, a combination of feature extraction and feature selection techniques is employed to obtain a reduced set of relevant features for differentiating schizophrenia patients from healthy subjects. A general linear model approach is used for feature extraction on pre-processed fMRI data. Further t-test based feature selection is employed to determine a subset of discriminative features which are used for learning a decision model using support vector machine. Experiments are carried out on two balanced and well-age matched datasets (acquired on 1.5 Tesla and 3 Tesla scanners) of auditory odd-ball task derived from a publicly available multisite FBIRN dataset. the performance is evaluated in terms of sensitivity, specificity and classification accuracy, and compared with two well-known existing approaches. Experimental results demonstrate that the proposed model outperforms the two existing approaches in terms of sensitivity, specificity and classification accuracy. With the proposed approach, the classification accuracy of 80.9% and 88.0% is achieved for 1.5 Tesla and 3 Tesla datasets respectively. In addition, the brain regions containing the discriminative features are identified which may be used as biomarkers for CAD of schizophrenia using fMRI. Copyright 2014 ACM.

关键词： computer aided diagnosis

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Multi-font telugu text recognition using hidden Markov models and Akshara Bi-grams 10

Multi-font telugu text recognition using hidden Markov model...

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10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

作者： Devarapalli, Koteswara Rao Negi, Atul Department of Computer Science and Engineering Mahatma Gandhi Institute of Technology Hyderabad500075 India School of Computer and Information Sciences University of Hyderabad Gachibowli Hyderabad500046 India

Recent advances in the information technology made possible to introduce many Unicode Telugu fonts for the documentation needs of present society. But the recognition of documents printed in a variety of fonts poses new challenges in building Telugu OCR systems. In this paper, we demonstrate multi-font Telugu printed word recognition using implicit segmentation approach that provides segmentation as a by-product of recognition. Our word recognition approach relies on Hidden Markov Models and akshara bi-gram language model to recognize word images in terms of aksharas (characters). the training set of word images is prepared from document images of popular books and the synthetic document images generated using 8 different Unicode fonts. the testing involves matching the feature vector sequence against sequence of akshara HMMs based on bi-grams. the CER and WER of this system are 21% and 37% respectively. the performance of our system is very encouraging. © Springer International Publishing AG 2017.

关键词： Hidden Markov models

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Flooded Area Segmentation from UAV images Based on Generative Adversarial Networks

Flooded Area Segmentation from UAV Images Based on Generativ...

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International conference on Control, Automation, Robotics and vision (ICARCV)

作者： Dan Popescu Loretta Ichim Florin Stoican Faculty of Automatic Control and Computer Science University Politehnica of Bucharest Romania Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest

the detection, localization and evaluation of small flooded areas can contribute to decrease the economical damages of such disasters. the cheapest and most accurate method is to segment the aerial images taken from UAV. In this paper, we propose a new method for detection of regions of interest, like flooding in rural areas, using Generative Adversarial Networks (GAN) and graphics processing Units (GPU). the classical GPU is used to create, by parallel calculation of textural features, extracted from the co-occurrence matrix, the supervised mask of flood segmentation in the images from the learning set. Based on these images and their associated real masks, the weights of the generator and discriminator are established. A set of 40 images were used for the learning phase and another set of 60 images were used for method validation. the results demonstrate that the proposed method provide a high accuracy and robustness, comparing with other papers for flooding evaluation. Even if it is a relative long time to learn the GAN, in the operational phase the time for image segmentation process is very short.

关键词： image segmentation Gallium nitride Floods Instruction sets graphics processing units Generative adversarial networks Generators

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Assessment of computational visual attention models on medical images 12

Assessment of computational visual attention models on medic...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Jampani, Varun Ujjwal Sivaswamy, Jayanthi Vaidya, Vivek CVIT IIIT-Hyderabad Hyderabad India Medical Image Analysis Lab. GE Global Research Bangalore India

ISBN: (纸本)9781450316606

Several computational visual saliency models have been proposed in the context of viewing natural scenes. We aim to investigate the relevance of computational saliency models in medical images in the context of abnormality detection. We report on two studies aimed at understanding the role of visual saliency in medical images. Diffuse lesions in Chest X-Ray images, which are characteristic of Pneumoconiosis and high contrast lesions such as 'Hard Exudates' in retinal images were chosen for the study. these approximately correspond to conjunctive and disjunctive targets in a visual search task. Saliency maps were computed using three popular models namely Itti-Koch [7], GBVS [3] and SR [4]. the obtained maps were evaluated against gaze maps and ground truth from medical experts. Our results show that GBVS is seen to perform the best (Mdn. ROC area = 0.77) for chest X-Ray images while SR performs the best (ROC area = 0.73) for retinal images, thus asserting that searching for conjunctive targets calls for a more local examination of an image while disjunctive targets call for a global examination. Based on the results of the above study, we propose extensions for the two best performing models. the first extension makes use of top down knowledge such as lung segmentation. this is shown to improve the performance of GBVS to some extent. In the second case the extension is by way of including multi-scale information. this is shown to significantly (by 28.76%) improve abnormality detection. the key insight from these studies is that bottom saliency continues to play a predominant role in examining medical images. © 2012 ACM.

关键词： Ophthalmology

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Few Shots Learning: Caricature to image Recognition Using Improved Relation Network 1

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5th International conference on computer vision and image processing, CVIP 2020

作者： Agrawal, Rashi Singh, Upendra Pratap Singh, Krishna Pratap Machine Learning and Optimization Lab Indian Institute of Information Technology Allahabad Prayagraj India

ISBN: (数字)9789811611032

ISBN: (纸本)9789811611025

Inspired by the human ability to learn instance-label associations given only a handful of examples, few-shots learning is gaining increased attention from the researchers. In this direction, we have proposed a modified relation network to perform few shots caricature to image recognition. Since caricature and real images come from different modalities, we have extended the relation network to contain separate feature extracting networks for the two modalities in the embedding module. this extension was necessitated by the fact that since caricature and real images come from different modalities and hence different distributions, a single feature extracting network in the embedding module may not learn cross-modal features. Experiment results on caricature recognition dataset and subsequent comparison of our proposed network against the baseline model quantitatively substantiates our hypothesis. While comparing the performance of our modified network against the baseline, we were able to improve the recognition accuracy by 26.3 % for 5 - way, 1 - shot setting and by 25.5 % for 5 - way, 5 - shot setting. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Embeddings

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A Schrödinger formalism for simultaneously computing the Euclidean distance transform and its gradient density 14

A Schrödinger formalism for simultaneously computing the Eu...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Gurumoorthy, Karthik S. Rangarajan, Anand International Center for Theoretical Sciences Tata Institute of Fundamental Research Bangalore Karnataka India Department of Computer and Information Science and Engineering University of Florida GainesvilleFL United States

ISBN: (纸本)1595930361

In this work, we employ the well-known Hamilton-Jacobi to Schrödinger connection to present a unified framework for computing both the Euclidean distance function and its gradient density in two dimensions. Previous work in this direction considered two different formalisms for independently computing these quantities. While the two formalisms are very closely related, their lack of integration is theoretically troubling and practically cumbersome. We introduce a novel Schrödinger wave function for representing the Euclidean distance transform from a discrete set of points. An approximate distance transform is computed from the magnitude of the wave function while the gradient density is estimated from the Fourier transform of the phase of the wave function. In addition to its simplicity and efficient O(N logN) computation, we prove that the wave function-based density estimator increasingly, closely approximates the distance transform gradient density (as a free parameter approaches zero) with the added benefit of not requiring the true distance function. Copyright 2014 ACM.

关键词： Wave functions

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