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检索条件"任意字段=15th Indian Conference on Computer Vision Graphics and Image Processing"

共 1003 条记录，以下是311-320 订阅

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Maintaining Sharp Features in Surface Construction for Volumetric Objects

Maintaining Sharp Features in Surface Construction for Volum...

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15th International conference in Central Europe on computer graphics, Visualization and computer vision

作者： Loke, Robert Edward Jansen, Frederik W. IIT CNR Pisa Italy Delft Univ Technol EWI Delft Netherlands

ISBN: (纸本)9788086943022

Discretized Marching Cubes (DMC) is a standard method in computer graphics and visualization for constructing 3D surfaces in data represented on a regular grid. After thresholding, it builds high-resolution surfaces by tiling surface patches halfway between objects and background in the data. this paper shows that if surfaces are built locally, in a high-resolution sub-grid of a cell instead of directly in a cell, sharp surfaces can be generated in order to preserve concave and convex object features. the main advantage is the improved geometric models that are extracted. this makes lower approximation errors and lower triangle counts possible.

关键词： computer graphics: Curve surface solid and object representations computer aided design (modeling of curves and surfaces) Computational geometry image processing

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Color image Super Resolution in Real Noise 11

Color Image Super Resolution in Real Noise

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Mandal, Srimanta Purohit, Kuldeep Rajagopalan, A. N. DA IICT Gandhinagar Gujarat India IIT Madras Chennai Tamil Nadu India

ISBN: (纸本)9781450366151

In practice, images can contain different amounts of noise for different color channels, which is not acknowledged by existing super-resolution approaches. In this paper, we propose to super-resolve noisy color images by considering the color channels jointly. Noise statistics are blindly estimated from the input low-resolution image and are used to assign different weights to different color channels in the data cost. Implicit low-rank structure of visual data is enforced via nuclear norm minimization in association with adaptive weights, which is added as a regularization term to the cost. Additionally, multi-scale details of the image are added to the model through another regularization term that involves projection onto PCA basis, which is constructed using similar patches extracted across different scales of the input image. the results demonstrate the super-resolving capability of the approach in real scenarios.

关键词： Real Color image Noise Super Resolution Nuclear Norm PCA

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image and graphics Technologies and Applications 1

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丛书名： Communications in computer and Information Science

1000年

作者： Yongtian Wang Xueming Li Yuxin Peng

this book constitutes the refereed proceedings of the 15th conference on image and graphics Technologies and Applications, IGTA 2020, held in Beijing, China in September, 2020.*

ISBN: (数字)9789813360334

ISBN: (纸本)9789813360327

this book constitutes the refereed proceedings of the 15th conference on image and graphics Technologies and Applications, IGTA 2020, held in Beijing, China in September, 2020.*

关键词： image processing and computer vision Machine Learning Pattern Recognition Coding and Information theory computer Communication Networks

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Certainty in Uncertainty! An Improved GI Bleeding Detection Pipeline with Uncertainty Estimation 24

Certainty in Uncertainty! An Improved GI Bleeding Detection ...

引用

15th indian conference on computer vision graphics and image processing

作者： Alavala, Sasidhar Gorthi, Subrahmanyam Indian Inst Technol Tirupati Tirupati India

ISBN: (纸本)9798400710759

Effective detection of gastrointestinal (GI) bleeding in endoscopic images is essential for accurate medical diagnosis and timely intervention. Despite advancements in deep learning, most work is based on predicting the label without considering the certainty in their predictions, leading to potential diagnostic errors. this paper presents an improved GI bleeding detection pipeline that integrates advanced preprocessing techniques, robust uncertainty estimation using deep ensembles, and a comprehensive classification and subsequent detection framework. the preprocessing steps include Gaussian blurring of green and blue channels and applying Contrast-Limited Adaptive Histogram Equalization (CLAHE) on the L channel of the Lab colour space image. After preprocessing, the classification of frames with Swin Transformers and Class Activation Maps (CAMs) for visual explanations are generated using Ablation-CAM. RT-DETR is used to detect the classified bleeding frames precisely, and the doctor classified normal frames with a high uncertainty estimate. the dataset is part of the Auto-WCEBleedGen V2 Challenge of the IEEE ICIP 2024. the experiments are performed on two different test sets, one similar to training, and the proposed preprocessing with Swin Transformers increased classification accuracy by 6.06%. Uncertainty estimation using deep ensembles with predictive entropy metric improved detection average precision at 50, 50:95 IoU by 2.53% and 2.71%, respectively, compared to without uncertainty estimation. these improvements show that the proposed pipeline is more accurate, reliable and interpretable.

关键词： Wireless Capsule Endoscopy Gastrointestinal Bleeding Uncertainty Estimation Swin Transformer Real-Time Detection Transformer Interpretability Monte Carlo Dropout Deep Ensembles

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Enhancement of High Dynamic Range images Using Variational Calculus Regularizer with Stochastic Resonance 16

Enhancement of High Dynamic Range Images Using Variational C...

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

作者： Kumar, Sumit Jha, Rajib Kumar Indian Inst Technol Patna Patna Bihar India

ISBN: (纸本)9781450347532

While capturing pictures by a simple camera in a scene with the presence of harsh or strong lighting like a full sunny day, we often find loss of highlight detail information (overexposure) in the bright regions and loss of shadow detail information (underexposure) in dark regions. In this manuscript, a classical method for retrieval of minute information from the high dynamic range image has been proposed. Our technique is based on variational calculus and dynamic stochastic resonance (DSR). We use a regularizer function, which has been added in order to optimise the correct estimation of the lost details from the overexposed or underexposed region of the image. We suppress the dynamic range of the luminance image by attenuating large gradient with the large magnitude and low gradient with low magnitude. At the same time, dynamic stochastic resonance (DSR) has been used to improve the underexposed region of the image. the experimental results of our proposed technique are capable of enhancing the quality of images in both overexposed and underexposed regions. the proposed technique is compared with most of the state-of-the-art techniques and it has been observed that the proposed technique is better or at most comparable to the existing techniques.

关键词： Dynamic stochastic resonance (DSR) wavelet variational calculus regularizer gradient tone mapping

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Intrinsic image Decomposition using Focal Stacks 16

Intrinsic Image Decomposition using Focal Stacks

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

作者： Saini, Saurabh Sakurikar, Parikshit Narayanan, P. J. IIIT Hyderabad Kohli Ctr Intelligent Syst Ctr Visual Informat Technol Hyderabad Telangana India

ISBN: (纸本)9781450347532

In this paper, we presents a novel method (RGBF-IID) for intrinsic image decomposition of a wild scene without any restrictions on the complexity, illumination or scale of the image. We use focal stacks of the scene as input. A focal stack captures a scene at varying focal distances. Since focus depends on distance to the object, this representation has information beyond an RGB image towards an RGBD image with depth. We call our representation an RGBF image to highlight this. We use a robust focus measure and generalized random walk algorithm to compute dense probability maps across the stack. these maps are used to de fine sparse local and global pixel neighbourhoods, adhering to the structure of the underlying 3D scene. We use these neighbourhood correspondences with standard chromaticity assumptions as constraints in an optimization system. We present our results on both indoor and outdoor scenes using manually captured stacks of random objects under natural as well as artificial lighting conditions. We also test our system on a larger dataset of synthetically generated focal stacks from NYUv2 and MPI Sintel datasets and show competitive performance against current state-of-the-art IID methods that use RGBD images. Our method provides a strong evidence for the potential of RGBF modality in place of RGBD in computer vision.

关键词： Intrinsic image decomposition focal stacks generalized random walk RGBF RGBF-IID

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Ray Casting Deformable Models on the GPU

Ray Casting Deformable Models on the GPU

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6th indian conference on computer vision, graphics and image processing

作者： Patidar, Suryakant Narayanan, P. J. IIIT Hyderabad Ctr Visual Informat Technol Hyderabad Andhra Pradesh India

ISBN: (纸本)9781424442195

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 real time 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： graphics processing unit

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A Novel Learning-based Framework for Detecting Interesting Events in Soccer Videos

A Novel Learning-based Framework for Detecting Interesting E...

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6th indian conference on computer vision, graphics and image processing

作者： Jain, Nisha Chaudhury, Santanu Roy, Sumantra Dutta Mukherjee, Prasenjit Seal, Krishanu Talluri, Kumar Indian Inst Technol Delhi Dept Elect Engn Delhi India AOL New York NY 10003 USA

ISBN: (纸本)9781424442195

We present a novel learning-based framework for detecting interesting events in soccer videos. the input to the system is a raw soccer video. We have learning at three levels - learning to detect interesting low-level features from image and video data using Support Vector Machines (hereafter SVMs), and a hierarchical Conditional Random Field(hereafter CRF-) based methodology to learn the dependencies of mid-level features and their relation with the low level features, and high level decisions ('interesting events') and their relation with the mid-level features: all on the basis of training video data. Descriptors are spatio-temporal in nature - they can be associated with a region in an image or a set of frames. Temporal patterns of descriptors characterise an event. We apply this framework to parse soccer videos into Interesting (a goal or a goal miss) and Non-Interesting videos. We present results of numerous experiments in support of the proposed strategy.

关键词： Support vector machines

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Multi-Resolution Tracking in Space and Time

Multi-Resolution Tracking in Space and Time

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6th indian conference on computer vision, graphics and image processing

作者： Roy, Sumantra Dutta Tran, Son Dinh Davis, Larry Steven Vikram, B. Sreenivasa Indian Inst Technol Delhi Dept Elect Engn Delhi India Univ Maryland Dept Comp Sci College Pk MD USA Indian Inst Technol Bharti Sch Telecom Tech & Management Delhi India

ISBN: (纸本)9781424442195

this paper proposes efficient and robust methods for tracking a moving object at multiple spatial and temporal resolution levels. the efficiency comes from optimising the amounts of spatial and temporal data processed. the robustness results from multi-level coarse-to-fine state-space searching. Tracking across resolution levels incurs a accuracy-versus-speed trade-off. For example, tracking at higher resolutions incurs greater processing cost, while maintaining higher accuracy in estimating the position of the moving object. We propose a novel spatial multi-scale tracker that tracks at the optimal accuracy-versus-speed operating point. Next, we relax this requirement to propose a multi-resolution tracker that operates at a minimum acceptable performance level. Finally, we extend these ideas to a multi-resolution spatio-temporal tracker We show results of extensive experimentation in support of the proposed approaches.

关键词： Economic and social effects

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Object Category Detection by Statistical Test of Hypothesis

Object Category Detection by Statistical Test of Hypothesis

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6th indian conference on computer vision, graphics and image processing

作者： Sharma, Gaurav Chaudhury, Santanu Srivastava, J. B. Indian Inst Technol Delhi Dept Math Delhi India Indian Inst Technol Delhi Dept Elect Engn Delhi India

ISBN: (纸本)9781424442195

We propose a novel framework for object detection and localization in images containing appreciable clutter and occlusions. the problem is cast in a statistical hypothesis testing framework. the image under test is converted into a set of local features using affine invariant local region detectors, described using the popular SIFT descriptor Due to clutter and occlusions, this set is expected to contain features which do not belong to the object. We sample subsets of local features from this set and test for the alternate hypothesis of object present against the null hypothesis of object absent. Further, we use a method similar to the recently proposed spatial scan statistic to refine the object localization estimates obtained from the sampling process. We demonstrate the results of our method on the two datasets TUD Motorbikes and TUD Cars. TUD Cars database has background clutter TUD Motorbikes dataset is recognized to have substantial variation in terms of scale, back-ground, illumination, viewpoint and occlusions.

关键词： Statistical tests

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