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检索条件"任意字段=Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing"

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Choice of the regularization parameter for total variation image denoising using no-reference metric 12

Choice of the regularization parameter for total variation i...

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12th International conference on Interfaces and Human computer Interaction 2018, 11th International conference on Game and Entertainment Technologies 2018 and 12th International conference on computer graphics, Visualization, computer vision and image processing 2018, part of the Multi conference on computer Science and Information Systems 2018, MCCSIS 2018

作者： Mamaev, Nikolay Krylov, Andrey Yurin, Dmitry Lomonosov Moscow State University GSP-1 Leninskie Gory Moscow Russia

ISBN: (纸本)9789898533791

An automatic method for total variation denoising parameter calculation has been proposed. Parameter optimization is performed in the ridge areas of the difference image between original noisy and filtered images (so-called method noise image). Appearance of regular components on method noise is controlled using mutual information closely connected with conditional entropy. images corrupted with Gaussian-like noise with small correlation radius are considered. Hessian matrix eigenvalues analysis is used for estimation of sizes and directions of image characteristic details. FISTA optimizer was used for the minimization of the regularizing total variation functional. images with added controlled Gaussian noise from retinal DRIVE and general image BSDS500 databases, and biomedical images of nuclei of HL60 cell line were used for testing. © 2018.

关键词： image denoising

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Sparse Signal Recovery for Multiple Measurement Vectors with Temporally Correlated Entries: A Bayesian Perspective 11

Sparse Signal Recovery for Multiple Measurement Vectors with...

引用

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

作者： Sharma, Shruti Chaudhury, Santanu Jayadeva, Jayadeva Bhagat, Snigdha Indian Inst Technol Delhi New Delhi India

ISBN: (纸本)9781450366151

Bayesian Sparse Signal Recovery (SSR) for Multiple Measurement Vectors, when elements of each row of solution matrix are correlated, is addressed in the paper. We propose a standard linear Gaussian observation model and a three-level hierarchical estimation framework, based on Gaussian Scale Mixture (GSM) model with some random and deterministic parameters, to model each row of the unknown solution matrix. this hierarchical model induces heavy-tailed marginal distribution over each row which encompasses several choices of distributions viz. Laplace distribution, Student's t distribution and Jeffery prior. Automatic Relevance Determination (ARD) phenomenon introduces sparsity in the model. It is interesting to see that Block Sparse Bayesian Learning framework is a special case of the proposed framework when induced marginal is Jeffrey prior. Experimental results for synthetic signals are provided to demonstrate its effectiveness. We also explore the possibility of using Multiple Measurement Vectors to model Dynamic Hand Posture Database which consists of sequence of temporally correlated hand posture sequence. It can be seen that by exploiting temporal correlation information present in the successive image samples, the proposed framework can reconstruct the data with less linear random measurements with high fidelity.

关键词： Matrix algebra

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On-Demand Augmentation of Contour Trees 11

On-Demand Augmentation of Contour Trees

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

作者： Sharma, Mohit Natarajan, Vijay Indian Inst Sci Bangalore Karnataka India

ISBN: (纸本)9781450366151

the contour tree represents the topology of level sets of a scalar function. Nodes of the tree correspond to critical level sets and arcs of the tree represent a collection of topologically equivalent level sets connecting two critical level sets. the augmented contour tree contains degree-2 nodes on the arcs that represent regular level sets. the degree-2 nodes correspond to regular points of the scalar function and other critical points that do not affect the number of level set components. the augmented contour tree is significantly larger in size and requires more effort to compute when compared to the contour tree. Applications of the contour tree to data exploration and visualization require the augmented contour tree. Current approaches propose algorithms to compute the contour tree and the augmented contour tree from scratch. Precomputing and storing the large augmented contour tree will not be necessary if the contour tree can be augmented on-demand. this paper poses the problem of computing the augmented contour tree given a contour tree as input. Computational experiments demonstrate that the on-demand augmentation can be computed fast while resulting in good memory savings.

关键词： Data visualization

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11th Asian conference on Intelligent Information and Database Systems, ACIIDS 2019

11th Asian Conference on Intelligent Information and Databas...

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11th Asian conference on Intelligent Information and Database Systems, ACIIDS 2019

ISBN: (纸本)9783030148010

the proceedings contain 124 papers. the special focus in this conference is on Intelligent Information and Database Systems. the topics include: Content-Based Music Classification by Advanced Features and Progressive Learning;appliance of Social Network Analysis and Data Visualization Techniques in Analysis of Information Propagation;a Temporal Approach for Air Quality Forecast;quad-Partitioning-Based Robotic Arm Guidance Based on image Data processing with Single Inexpensive Camera For Precisely Picking Bean Defects in Coffee Industry;on the Analysis of Kelly Criterion and Its Application;single image Super-Resolution with vision Loss Function;content-Based Motorcycle Counting for Traffic Management by image Recognition;use of Blockchain in Education: A Systematic Literature Review;a Comparative Study of Techniques for Avoiding Premature Convergence in Harmony Search Algorithm;Analysis of Different Approaches to Designing the Parallel Harmony Search Algorithm for ATSP;an Independence Measure for Expert Collections Based on Social Media Profiles;differential Evolution in Agent-Based Computing;Verifying Usefulness of Ant Colony Community for Solving Dynamic TSP;physical Layer Security Cognitive Decode-and-Forward Relay Beamforming Network with Multiple Eavesdroppers;co-exploring a Search Space in a Group Recommender System;differential Cryptanalysis of Symmetric Block Ciphers Using Memetic Algorithms;modeling of Articular Cartilage with Goal of Early Osteoarthritis Extraction Based on Local Fuzzy thresholding Driven by Fuzzy C-Means Clustering;modeling and Features Extraction of Heel Bone Fracture Reparation Dynamical Process from X-Ray images Based on Time Iteration Segmentation Model Driven by Gaussian Energy;A Semi-Supervised Learning Approach for Automatic Segmentation of Retinal Lesions Using SURF Blob Detector and Locally Adaptive Binarization.

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LEDNet: Deep Network for Single image Haze Removal 11

LEDNet: Deep Network for Single Image Haze Removal

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

作者： Dudhane, Akshay Murala, Subrahmanyam Dhall, Abhinav Indian Inst Technol Ropar Comp Vis & Pattern Recognit Lab Rupnagar India Indian Inst Technol Ropar Learning Affect & Semant Image Anal Grp Rupnagar India

ISBN: (纸本)9781450366151

Haze during the bad weather, degrades the visibility of the scene drastically. Degradation of scene visibility varies with respect to the transmission coefficient/map (T-c) of the scene. Estimation of accurate T-c is key step to reconstruct the haze free scene. Previously, local as well as global priors were proposed to estimate the T-c. We, on the other hand, propose integration of local and global approaches to learn both point level and object level T-c. the proposed local encoder decoder network (LEDNet) estimates the scene transmission map in two stages. During first stage, network estimates the point level T-c using parallel convolutional filters and spatial invariance filtering. the second stage comprises of a two level encoder-decoder architecture which anticipates the object level T-c. We also propose, local air-light estimation (LAE) algorithm, which is able to obtain the air-light component of the outdoor scene. Combination of LEDNet and LAE improves the accuracy of haze model to recover the scene radiance. Structural similarity index, mean square error and peak signal to noise ratio are used to evaluate the performance of the proposed approach for single image haze removal. Experiments on benchmark datasets show that LEDNet outperforms the existing state-of-the-art methods for single image haze removal.

关键词： Haze removal CNN transmission map

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Surveying games with a combined model of immersion and flow 12

Surveying games with a combined model of immersion and flow

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作者： Kannegieser, Ehm Atorf, Daniel Meier, Josua Fraunhofer Institute of Optronics System Technologies and Image Exploitation Fraunhoferstraße. 1 Karlsruhe76131 Germany Karlsruhe Institute of Technology Kaiserstraße. 12 Karlsruhe76131 Germany

ISBN: (纸本)9789898533791

Detecting flow in games is key for successful adaptation processes. Until now, the method of choice to measure flow in games, is the usage of questionnaires. Because of the shortcomings of this method, the ultimate goal is, to establish an alternative measuring method through correlations of physiological sensor data. Beforehand the enhancement of the theoretical model of flow with the more fine-grained model of immersion is introduced. A unified flow/immersion model is proposed to allow a more detailed mapping of flow depth. In conclusion a way to evaluate the unified model is presented and a perspective towards preliminary test runs and future data recordings is given. © 2018.

关键词： Surveys

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Learning to Prevent Monocular SLAM Failure using Reinforcement Learning 11

Learning to Prevent Monocular SLAM Failure using Reinforceme...

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

作者： Prasad, Vignesh Yadav, Karmesh Saurabh, Rohitashva Singh Daga, Swapnil Pareekutty, Nahas Krishna, K. Madhava Ravindran, Balaraman Bhowmick, Brojeshwar TCS Res & Innovat Embedded Syst & Robot Kolkata India Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA Johns Hopkins Univ Dept Robot Engn Baltimore MD USA IIIT Hyderabad Robot Res Ctr KCIS Hyderabad Telangana India Indian Inst Technol Dept Comp Sci Madras Tamil Nadu India

ISBN: (纸本)9781450366151

Monocular SLAM refers to using a single camera to estimate robot ego motion while building a map of the environment. While Monocular SLAM is a well studied problem, automating Monocular SLAM by integrating it with trajectory planning frameworks is particularly challenging. this paper presents a novel formulation based on Reinforcement Learning (RL) that generates fail safe trajectories wherein the SLAM generated outputs do not deviate largely from their true values. Quintessentially, the RL framework successfully learns the otherwise complex relation between perceptual inputs and motor actions and uses this knowledge to generate trajectories that do not cause failure of SLAM. We show systematically in simulations how the quality of the SLAM dramatically improves when trajectories are computed using RL. Our method scales effectively across Monocular SLAM frameworks in both simulation and in real world experiments with a mobile robot.

关键词： Reinforcement Learning Robot Navigation Monocular SLAM

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Moving Average Recurrent Neural Network Model for Video-based Person Re-Identification 11

Moving Average Recurrent Neural Network Model for Video-base...

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

作者： Fasil, Muhammed C. Chaudhuri, Subhasis Indian Inst Technol Dept Elect Engn Mumbai Maharashtra India

ISBN: (纸本)9781450366151

Person re-identification has great applications in video surveillance. It can be viewed as recognizing the same person across non-overlapping cameras. Video-based person re-identification methods are gaining increased attention due to the better discriminative nature of spatio-temporal feature representations. Current video-based methods make use of RNN to extract temporal information. In this paper, we propose a novel Moving Average Recurrent Neural Network (MA-RNN) model that can build a strong feature representation by taking both previous and present inputs at each time stamp. Specifically, here the recurrent layer produces a better sequential information by looking back directly in to the past values where as general RNNs has only an indirect dependence on the previous values in the form of hidden-state information. the proposed model is tested on two publicly available datasets: iLIDS-VID and PRID-2011 and it performed better in comparison with the state-of-the-art methods with a significant margin. We also analyze the effect of the depth of previous input dependence of the MA-RNN model on the matching accuracy.

关键词： RNN MA-RNN person re-identification

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Activity Recognition in Egocentric Videos Using Bag of Key Action Units 11

Activity Recognition in Egocentric Videos Using Bag of Key A...

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

作者： Suma, K. Sai Aditya, G. Mukherjee, Snehasis Indian Inst Informat Technol Sricity Chittoor Andhra Pradesh India

ISBN: (纸本)9781450366151

In this paper we present a novel methodology for recognizing human activity in Egocentric video based on the Bag of Visual Features. the proposed technique is based on the assumption that, only a portion of the whole video can be sufficient to identify an activity. Rather, we argue that, for activity recognition in egocentric videos, the proposed approach performs better than any deep learning based method. Because, in egocentric videos, often the person wiring the sensor, becomes static for long time, or moves his head frequently. In both the cases, it becomes difficult to learn the spatio-temporal pattern of the video during action. the proposed approach divides the video into smaller video segments called Video Units. Spatio-temporal features extracted from the units, are clustered to construct the dictionary of Action Units (AU). the AUs are ranked based upon their score of likeliness. the scores are obtained by constructing a weighted graph with the AUs as vertices and edge weights calculated based on the frequencies of occurrences of the AUs during the activity. the less significant AUs are pruned out from the dictionary, and the revised dictionary of key AUs are used for activity *** test our approach on benchmark egocentric dataset and achieve a good accuracy.

关键词： Deep learning

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DP-GAN: Dual Pathway Generative Adversarial Network for Face Recognition in Degraded Scenarios 11

DP-GAN: Dual Pathway Generative Adversarial Network for Face...

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

作者： Bhattacharjee, Avishek Banerjee, Samik Das, Sukhendu Indian Inst Technol Madras Chennai 600036 Tamil Nadu India

ISBN: (纸本)9781450366151

Face Recognition (FR) using Convolutional Neural Network (CNN) based models have achieved considerable success in constrained environments. they however fail to perform well in unconstrained scenarios, especially when the images are captured using surveillance cameras. these probe samples suffer from degradations such as noise, poor illumination, low resolution, blur as well as aliasing, when compared to the rich training (gallery) set, comprising mostly of mugshot images captured in laboratory settings. these images in the training (gallery) set are crisp and have high contrast, compared to the probe samples. To cope with this scenario, we propose a novel dual-pathway generative adversarial network (DP-GAN) which maps low resolution images captured using surveillance camera into their corresponding high resolution images, which are gallery-like, using a novel combination of multi-scale reconstruction and Jensen-Shannon divergence based loss. these images thus obtained are then used to train a deep domain adaptation (deep-DA) network to perform the task of FR. the proposed network achieves superior results (>90%) on four benchmark surveillance face datasets, evident from the rank-1 recognition rates when compared with recent state-of-the-art CNN-based techniques.

关键词： Face recognition

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