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检索条件"任意字段=6th Indian Conference on Computer Vision, Graphics and Image Processing"

共 2008 条记录，以下是361-370 订阅

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Classification of Schizophrenia Versus Normal Subjects Using Deep Learning 16

Classification of Schizophrenia Versus Normal Subjects Using...

引用

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

作者： Patel, Pinkal Aggarwal, Priya Gupta, Anubha Indraprastha Inst Informat Technol Delhi IIIT D Dept Elect & Commun Engn SBILab Delhi India

ISBN: (纸本)9781450347532

Motivated by deep learning approaches to classify normal and neuro-diseased subjects in functional Magnetic Resonance Imaging (fMRI), we propose stacked autoencoder (SAE) based 2-stage architecture for disease diagnosis. In the proposed architecture, a separate 4-hidden layer autoencoder is trained in unsupervised manner for feature extraction corresponding to every brain region. thereafter, these trained autoencoders are used to provide features on class-labeled input data for training a binary support vector machine (SVM) based classifier. In order to design a robust classifier, noisy or inactive gray matter voxels are filtered out using a proposed covariance based approach. We applied the proposed methodology on a public dataset, namely, 1000 Functional Connectomes Project Cobre dataset consisting of fMRI data of normal and Schizophrenia subjects. the proposed architecture is able to classify normal and Schizophrenia subjects with 10-fold cross-validation accuracy of 92% that is better compared to the existing methods used on the same dataset.

关键词： Classification Support Vector Machine Stacked Autoencoder Schizophrenia

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Qualitative Spatial and Temporal Reasoning over Diagrams for Activity Recognition 16

Qualitative Spatial and Temporal Reasoning over Diagrams for...

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

作者： Nath, Chayanika D. Hazarika, Shyamanta M. Tezpur Univ Sch Engn Comp Sci & Engn Biomimet & Cognit Robot Lab Tezpur 784028 India

ISBN: (纸本)9781450347532

In quest for an efficient representation schema for activity recognition in video, we employ techniques combining diagrammatic reasoning (DR) with qualitative spatial and temporal reasoning (QSTR). QSTR allows qualitative abstraction of spatio-temporal relations among objects of interest;and is often thwart by ambiguous conclusions. 'Diagrams' influence cognitive reasoning by externalizing mental context. Hence, QSTR over diagrams holds promise. We define 'diagrams' as explicit representation of objects of interest and their spatial information on a 2D grid. A sequence of 'key diagrams' is extracted. Inter diagrammatic reasoning operators combine 'key diagrams' to obtain spatio-temporal information. the qualitative spatial and temporal information thus obtained define short-term activity (STA). Several STAs combine to form long-term activities (LTA). Sequence of STAs as a feature vector is used for LTA recognition. We evaluate our approach over six LTAs from the CAVIAR dataset.

关键词： Qualitative spatial and temporal reasoning Diagrammatic reasoning Inter diagrammatic reasoning

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Deep neural network for foreground object segmentation: An unsupervised approach 6th

Deep neural network for foreground object segmentation: An u...

引用

6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Majumder, Avishek Venkatesh Babu, R. Indian Institute of Science BangaloreKarnataka560012 India

ISBN: (纸本)9789811300196

Saliency plays a key role in various computer vision tasks. Extracting salient regions from images and videos have been a well established problem of computer vision. While segmenting salient objects from images depend only on static information, temporal information in a video can make non salient objects be salient due to movement. Besides the temporal information, there are other challenges involved with video segmentation, such as 3D parallax, camera shake, motion blur, etc. In this work, we propose a novel unsupervised end to end trainable, fully convolutional deep neural network for object segmentation. Our model is robust and scalable across scenes, as it is tested unsupervisedly and can easily infer which objects constitute the foreground of the image. We run various tests on two well established benchmarks of video object segmentation, DAVIS and FBMS-59 datasets. We report our results and compare them against the state of the art methods. © Springer Nature Singapore Pte Ltd. 2018.

关键词： computer vision

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Outer Surface Reconstruction for 3D Fractured Objects

Outer Surface Reconstruction for 3D Fractured Objects

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International conference on computer vision and graphics

作者： Kornev, Anatoly Babout, Laurent Janaszewski, Marcin Talbot, Hugues Tech Univ Lodz Dept Comp Engn 18 Stefanowskiego Str PL-90924 Lodz Poland Univ Paris East Dep Infomat F-93162 Paris France

ISBN: (纸本)9783642159060

We study surface reconstruction using a combination of anisotropic Gaussian filter and image segmentation technique -the minimum surface method. Anisotropic Gaussian filtering allows to manage a contrast between intensities of the discontinuity and the object in a desired direction. the minimum surface method detects properly outer boundaries even affected by boundary leakage in the vicinity of blurred edges. the algorithm is tested on a set of real 3D images of large corrosion cracks in stainless steel that initiated at the surface of the tested samples. Results are presented and discussed.

关键词： CT image Segmentation Gaussian Filtering Minimal Surface

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An IDE-agnostic system to capture reading behaviour of C++ programs using eye-gaze tracker 5

An IDE-agnostic system to capture reading behaviour of C++ p...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2015

作者： Mondal, Sayani Das, Partha Pratim Rajendra Mishra School of Engineering Entrepreneurship Indian Institute of Technology Kharagpur721302 India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur721302 India

ISBN: (纸本)9781467385640

Over 70% of software development effort is spent in software maintenance comprising bug fixes and version updates. these activities involve fast comprehension of large codebases authored by multiple developers. Developers are mostly trained to write code while in practice they often need to read code. Hence effective and efficient systems are needed to assess the code reading ability of developers. © 2015 IEEE.

关键词： Eye tracking

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Block-based Glassy Effects for Natural images 6

Block-based Glassy Effects for Natural Images

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6th International conference on Multimedia computer graphics and Broadcasting (MulGraB)

作者： Jeon, Gwanggil Kang, Seokhoon Anisetti, Marco Incheon Natl Univ Dept Embedded Syst Engn 119 Acad Ro Inchon 406772 South Korea Univ Milan Dept Comp Sci Via Bramante 65 I-20122 Crema CR Italy

ISBN: (纸本)9781479977642

this paper presents a new grassy effect generation approach. the grassy effect changes original image as the one behind the grass. A random number generation function is employed to decide the same of block and location of the pixel. Obtained various block size and pixel location yield unexpected grassy execution to the original image. the subjective and the objective image performances are compared which conclude that the proposed approach successfully crates glassy effect in images.

关键词： image processing glassy effect random number generation variable length block

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VidSeg-GAN: Generative Adversarial Network for Video Object Segmentation Tasks 11

VidSeg-GAN: Generative Adversarial Network for Video Object ...

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

作者： Adak, Saptakatha Das, Sukhendu Indian Inst Technol Madras Chennai Tamil Nadu India

ISBN: (纸本)9781450366151

Video object segmentation aims to segment objects in a video sequence, given some user annotation which indicates the object of interest. Although Convolutional Neural Networks (CNNs) have been used in the recent past for the purpose of foreground segmentation in videos, adversarial training methods have not been used effectively to solve this problem, in spite of its extensive use for solving many other problems in computer vision. Earlier, flow features and motion trajectories have been extensively used to capture the temporal consistency between subsequent frames to segment moving objects in videos. However, we show that our proposed framework of processing the video frames independently using a deep generative adversarial network (GAN), is able to maintain the temporal coherency across frames without the use of any explicit trajectory based information, to provide superior results. Our main contribution lies in introducing a GAN based framework along with the incorporation of an Intersection-over-Union score based novel cost function for training the model, to solve the problem of foreground object segmentation in videos. the proposed method, when evaluated on popular real-world video segmentation datasets viz. DAVIS, SegTrack-v2 and YouTube-Objects, exhibits substantial performance gain over the recent state-of-the-art methods.

关键词： Generative adversarial networks

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A Distribution-Independent Risk Estimator for image Denoising 16

A Distribution-Independent Risk Estimator for Image Denoisin...

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

作者： Subramanian, Babu Kishore Gupta, Ashutosh Seelamantula, Chandra Sekhar Sattva MedTech Bangalore 560025 Karnataka India Indian Space Res Org Ahmadabad 380015 Gujarat India Indian Inst Sci Bangalore 560012 Karnataka India

ISBN: (纸本)9781450347532

We address the problem of image denoising for an additive white noise model without placing any restrictions on the statistical distribution of noise. We assume knowledge of only the first-and second-order noise statistics. In the recent mean-square error (MSE) minimization approaches for image denoising, one considers a particular noise distribution and derives an expression for the unbiased risk estimate of the MSE. For additive white Gaussian noise, an unbiased estimate of the MSE is Stein's unbiased risk estimate (SURE), which relies on Stein's lemma. We derive an unbiased risk estimate without using Stein's lemma or its counterparts for additive white noise model irrespective of the noise distribution. We refer to the MSE estimate as the generic risk estimate (GenRE). We demonstrate the effectiveness of GenRE using shrinkage in the undecimated Haar wavelet transform domain as the denoising function. the estimated peak-signal-to-noise-ratio (PSNR) using GenRE is typically within 1% of the PSNR obtained when optimizing with the oracle MSE. the performance of the proposed method is on par with SURE for Gaussian noise distribution, and better than SURE-based methods for other noise distributions such as uniform and Laplacian distribution in terms of both PSNR and structural similarity (SSIM).

关键词： Risk estimation undecimated wavelet transform image denoising wavelet shrinkage generic risk estimation (GenRE)

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User guided generation of corroded objects 16

User guided generation of corroded objects

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

作者： Jain, Nisha Ranjan, Rohit Kalra, Prem Kumar, Subodh Indian Inst Technol Delhi New Delhi India

ISBN: (纸本)9781450347532

Rendering of corrosion often requires pain-staking modeling and texturing. On the other hand, there exist techniques for stochastic modeling of corrosion, which can automatically perform simulation and rendering under control of some user-specified parameters. Unfortunately, these parameters are non-intuitive and have a global impact. It is hard to determine the values of these parameters to obtain a desired look. For example, in real life corrosion gets influenced by both internal object-specific geometric factors, like sharp corners and curvatures, and external interventions like scratches, blemishes etc. Further, a graphics designer may want to selectively corrode areas to obtain a particular scene. We present a technique for user guided spread of corrosion. Our framework encapsulates both structural and aesthetic factors. Given the material properties and the surrounding environmental conditions of an object, we employ a physio-chemically based stochastic model to deduce the decay of different points on that object. Our system equips the user with a platform where the imperfections can be provided by either manual or systematic interference on a rendering of the three dimensional object. We demonstrate several user guided characteristic simulations encompassing varied influences including material, object characteristics and environment conditions. Our results are visually validated to understand the impact of imperfections with elapsed time.

关键词： Corrosion Simulation Rendering User guided Curvatures

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Dynamic Gesture Recognition with Pose-based CNN Features derived from videos using LSTM 11

Dynamic Gesture Recognition with Pose-based CNN Features der...

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

作者： Roy, Kankana Sahay, Rajiv R. Indian Inst Technol Kharagpur Dept Comp Sci & Engn Computat Vis Lab Kharagpur W Bengal India Indian Inst Technol Kharagpur Dept Elect Engn Computat Vis Lab Kharagpur W Bengal India

ISBN: (纸本)9781450366151

In this work, we address the problem of dynamic gesture recognition using a pose based video descriptor. the proposed approach takes as input video frames and extracts pose-specific image regions which are further processed by a pre-trained Convolutional Neural Network (CNN) to derive a pose-based descriptor for each frame. A Long Short Term Memory (LSTM) network is trained from scratch for dynamic gesture classification by learning long-term spatiotemporal relations among features. We also demonstrate that only using video data (RGB frames and optical flow) one can design an effective model for recognizing dynamic gestures. We utilize ChaLearn multi-modal gesture challenge dataset [13] and Cambridge hand gesture dataset [18] for evaluation of the proposed algorithm achieving an accuracy of 91.27% and 96% respectively using only RGB data.

关键词： Long short term memory pose-based CNN feature video descriptor dynamic gesture classification

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