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检索条件"任意字段=Proceedings of the Fourteenth Indian Conference on Computer Vision, Graphics and Image Processing"

共 1495 条记录，以下是1171-1180 订阅

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Interpreting Intrinsic image Decomposition using Concept Activations 13

Interpreting Intrinsic Image Decomposition using Concept Act...

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

作者： Gupta, Avani Saini, Saurabh Narayanan, P.J. CVIT KCIS IIIT-Hyderabad India

ISBN: (纸本)9781450398237

Evaluation of ill-posed problems like Intrinsic image Decomposition (IID) is challenging. IID involves decomposing an image into its constituent illumination-invariant Reflectance (R) and albedo-invariant Shading (S) components. Contemporary IID methods use Deep Learning models and require large datasets for training. The evaluation of IID is carried out on either synthetic Ground Truth images or sparsely annotated natural images. A scene can be split into reflectance and shading in multiple, valid ways. Comparison with one specific decomposition in the ground-truth images used by current IID evaluation metrics like LMSE, MSE, DSSIM, WHDR, SAW AP%, etc., is inadequate. Measuring R-S disentanglement is a better way to evaluate the quality of IID. Inspired by ML interpretability methods, we propose Concept Sensitivity Metrics (CSM) that directly measure disentanglement using sensitivity to relevant concepts. Activation vectors for albedo invariance and illumination invariance concepts are used for the IID problem. We evaluate and interpret three recent IID methods on our synthetic benchmark of controlled albedo and illumination invariance sets. We also compare our disentanglement score with existing IID evaluation metrics on both natural and synthetic scenes and report our observations. Our code and data are publicly available for reproducibility 1. © 2022 ACM.

关键词： Large dataset

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Ray Casting Deformable Models on the GPU

Ray Casting Deformable Models on the GPU

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Suryakant Patidar P.J. Narayanan Center for Visual Information Technology IIIT-Hyderabad India

The GPUs pack high computation power and a restricted architecture into easily available hardware today. They are now used as computation co-processors and come with programming models that treat them as standard parallel architectures. We explore the problem of realtime ray casting of large deformable models (over a million triangles) on large displays (a million pixels) on an off-the-shelf GPU in this paper. Ray casting is an inherently parallel and highly compute intensive operation. We build a GPU-efficient three-dimensional data structure for this purpose and a corresponding algorithm that uses it for fast ray casting. We also present fast methods to build the data structure on the SIMD GPUs, including a fast multi-split operation. We achieve real-time ray-casting of a million triangle model onto a million pixels on current Nvidia GPUs using the CUDA model. Results are presented on the data structure building and ray casting on a number of models. The ideas presented here are likely to extend to later models and architectures of the GPU as well as to other multi-core architectures.

关键词： Casting Deformable models Data structures Concurrent computing computer architecture Hardware Coprocessors Parallel programming Parallel architectures Displays

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Multi-criteria energy minimization with boundedness, edge-density and rarity, for object saliency in natural images 14

Multi-criteria energy minimization with boundedness, edge-de...

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

作者： Roy, Sudeshna Das, Sukhendu Indian Institute Technology Madras India

ISBN: (纸本)1595930361

Recent methods of bottom-up salient object detection have attempted to either: (i) obtain a probability map with a 'contrast rarity' based functional, formed using low level cues;or (ii) Minimize an objective function, to detect the object. Most of these methods fail for complex, natural scenes, such as the PASCAL-VOC challenge dataset which contains images with diverse appearances, illumination conditions, multiple distracting objects and varying scene environments. We thus formulate a novel multi-criteria objective function which captures many dependencies and the scene structure for correct spatial propagation of low-level priors to perform salient object segmentation, in such cases. Our proposed formulation is based on CRF modeling where the minimization is performed using graph cut and the optimal parameters of the objective function are learned using a max-margin framework from the training set, without the use of class labels. Hence the method proposed is unsu-pervised, and works efficiently when compared to the very recent state-of-the art methods of saliency map detection and object proposals. Results, compared using F-measure and intersection-over-union scores, show that the proposed method exhibits superior performance in case of the complex PASCAL-VOC 2012 object segmentation dataset as well as the traditional MSRA-B saliency dataset. Copyright 2014 ACM.

关键词： Object detection

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Mesh denoising by improved 3D geometric bilateral filter

Mesh denoising by improved 3D geometric bilateral filter

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Somnath Dutta Sumandeep Banerjee Prabir Kumar Biswas Partha Bhowmick Department of Electronics and Electrical Communication Engineering Department of Computer Science and Engineering Indian Institute of Technology Kharagpur

We present an improved mesh denoising method based on 3D geometric bilateral filtering. Its novelty is that it can preserve the details of the object as well as reduce the noise in an effective manner. The previous approach of geometric bilateral filtering for 3D-scan points has a limitation that it reduces the point density, thereby losing the details present in the object. The approach proposed by us, on the contrary, works on the surface mesh obtained after triangulating the 3D-scan points without any data downsampling. Each vertex of the mesh is repositioned appropriately based on the estimated centroid of the vertices in its local neighborhood and a Gaussian weight function. Experimental results demonstrate its strength, efficiency, and robustness.

关键词： Noise reduction Three-dimensional displays Surface treatment Noise measurement Laplace equations Smoothing methods computer graphics

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Multi-disciplinary Trends in Artificial Intelligence 1

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丛书名： Lecture Notes in computer Science

1000年

作者： M. Narasimha Murty Xiangjian He Raghavendra Rao Chillarige Paul Weng

ISBN: (数字)9783319133652

ISBN: (纸本)9783319133645

This book constitutes the refereed conference proceedings of the 8th International conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2014, held in Bangalore, India, in December 2014. The 22 revised full papers were carefully reviewed and selected from 44 submissions. The papers feature a wide range of topics covering both theory, methods and tools as well as their diverse applications in numerous domains.

关键词： Artificial Intelligence Data Mining and Knowledge Discovery image processing and computer vision Algorithm Analysis and Problem Complexity Information Systems Applications (incl. Internet)

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Learning ontology for personalized video retrieval

Learning ontology for personalized video retrieval

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International Multimedia conference, MM'07 - Workshop on the Many Faces of Multimedia Semantics, MS'07

作者： Ghosh, Hiranmay Poornachander, P. Mallik, Anupama Chaudhury, Santanu Innovation Labs Delhi TCS Limited 249 D and E Udyog Vihar Phase 4 Gurgaon 122016 India Electrical Engineering Deptt. Indian Institute of Technology Delhi New Delhi 110016 India

ISBN: (纸本)9781595937827

This paper proposes a new method for using implicit user feedback from clickthrough data to provide personalized ranking of results in a video retrieval system. The annotation based search is complemented with a feature based ranking in our approach. The ranking algorithm uses belief revision in a Bayesian Network, which is derived from a multimedia ontology that captures the probabilistic association of a concept with expected video features. We have developed a content model for videos using discrete feature states to enable Bayesian reasoning and to alleviate on-line feature processing overheads. We propose a reinforcement learning algorithm for the parameters of the Bayesian Network with the implicit feedback obtained from the clickthrough data. Copyright 2007 ACM.

关键词： image retrieval

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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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Monitoring a large surveillance space through distributed face matching

Monitoring a large surveillance space through distributed fa...

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Richa Mishra Prasanna Kumar Santanu Chaudhury Indu S Delhi Technological University Delhi India Indian Institute of Technology Delhi India

Large space with many cameras require huge storage and computational power to process these data for surveillance applications. In this paper we propose a distributed camera and processing based face detection and recognition system which can generate information for finding spatiotemporal movement pattern of individuals over a large monitored space. The system is built upon Hadoop Distributed File System using map reduce programming model. A novel key generation scheme using distance based hashing technique has been used for distribution of the face matching task. Experimental results have established effectiveness of the technique.

关键词： Face Surveillance Cameras Streaming media Real-time systems Artificial intelligence

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Quantization Based Data Hiding Scheme for Efficient Quality Access Control of images Using DWT via Lifting

Quantization Based Data Hiding Scheme for Efficient Quality ...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Amit Phadikar Santi P. Maity Malay K. Kundu Department of Information Technology MCKV Institute of Engineering Howrah India Department of Information Technology Bengal Engineering and Science University Howrah India Machine Intelligence Unit & Center for Soft Computing Research Indian Statistical Institute Kolkata India

This paper proposes a transform domain data-hiding scheme for quality access control of images. The original image is decomposed into tiles by applying n-level lifting-based discrete wavelet transformation (DWT). A binary watermark image (external information) is spatially dispersed using the sequence of number generated by a secret key. The encoded watermark bits are then embedded into all DWT-coefficients of n th -level and only in the high-high (HH) coefficients of the subsequent levels using dither modulation (DM) but without complete self-noise suppression. It is well known that due to insertion of external information, there will be degradation in visual quality of the host image (cover). The degree of deterioration depends on the amount of external data insertion as well as step size used for DM. If this insertion process is reverted, better quality of images can be accessed. To achieve that goal, watermark bits are detected using minimum distance decoder and the remaining self-noise due to information embedding is suppressed to provide better quality of image. The simulation results have shown the validity of this claim.

关键词： Quantization Data encapsulation Access control Discrete wavelet transforms Watermarking Delta modulation Discrete transforms Tiles Degradation Decoding

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Gradient sensitive kernel for image Denoising, using Gaussian Process Regression

Gradient sensitive kernel for Image Denoising, using Gaussia...

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Arka Ujjal Dey Gaurav Harit Dept. of Computer Science and Engineering Indian Institute of Technology Jodhpur

We target the problem of image Denoising using Gaussian Processes Regression (GPR). Being a non-parametric regression technique, GPR has received much attention in the recent past and here we further explore its versatility by applying it to a denoising problem. The focus is primarily on the design of a local gradient sensitive kernel that captures pixel similarity in the context of image denoising. This novel kernel formulation is used to shape the smoothness of the joint GP prior. We apply the GPR denoising technique to small patches and then stitch back these patches, this allows the priors to be local and relevant, also this helps us in dealing with GPR complexity. We demonstrate that our GPR based technique gives better PSNR values in comparison to existing popular denoising techniques.

关键词： Kernel Noise reduction Signal to noise ratio Noise measurement Ground penetrating radar Gaussian processes Mathematical model

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