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A Rapid Non-Linear Diffusion Compressed Sensing parallel MR image Reconstruction 11

A Rapid Non-Linear Diffusion Compressed Sensing parallel MR ...

引用

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

作者： Joy, Ajin Paul, Joseph Suresh Indian Inst Informat Technol & Management Kerala Trivandrum Kerala India

ISBN: (纸本)9781450366151

Optimization of the tradeoff between computation time and image quality is essential for reconstructing high-quality magnetic resonance image (MRI) from a limited number of acquired samples in a short time using compressed sensing (CS) algorithms. In this paper, we achieve this for the edge preserving non-linear diffusion reconstruction (NLDR) which eliminates the critical step-size tuning of the total variation (TV) based CS-MRI. Based on optimization of contrast parameter that controls noise and signal in sensitivity modulated channel images, we propose an a-switching NLDR technique for a faster approximation of reconstruction image without affecting the image quality. Proposed algorithm exploits the difference in the extent of undersampling artifacts in signal-background regions of the channel images to arrive at different estimates of contrast parameter, leading to an effective optimization of speed and quality. While maintaining better image quality as compared to conventional TV reconstruction, the switched NLDR also achieves 25-35% gain in convergence time over NLDR without switching. This makes the switched NLDR a better candidate for fast reconstruction over traditional TV and NLDR approaches. In the detailed numerical experiments, we have compared and optimized the tradeoff for various state-of-the-art choices of contrast parameter.

关键词： parallel MRI nonlinear diffusion contrast parameter

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Reinforcement Learning in computer vision 10

Reinforcement Learning in Computer Vision

引用

10th International conference on Machine vision (ICMV)

作者： Bernstein, A. V. Burnaev, E. V. Skolkovo Inst Sci & Technol Moscow Russia

ISBN: (数字)9781510619425

ISBN: (纸本)9781510619425

Nowadays, machine learning has become one of the basic technologies used in solving various computer vision tasks such as feature detection, image segmentation, object recognition and tracking. In many applications, various complex systems such as robots are equipped with visual sensors from which they learn state of surrounding environment by solving corresponding computer vision tasks. Solutions of these tasks are used for making decisions about possible future actions. It is not surprising that when solving computer vision tasks we should take into account special aspects of their subsequent application in model-based predictive control. Reinforcement learning is one of modern machine learning technologies in which learning is carried out through interaction with the environment. In recent years, Reinforcement learning has been used both for solving such applied tasks as processing and analysis of visual information, and for solving specific computer vision problems such as filtering, extracting image features, localizing objects in scenes, and many others. The paper describes shortly the Reinforcement learning technology and its use for solving computer vision problems.

关键词： computer vision reinforcement learning

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An incremental topological map building using monocular vision 11

An incremental topological map building using monocular visi...

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

作者： Bhowmick, Soumabha Mukherjee, Jayanta Deb, Alok Kanti Indian Inst Technol Kharagpur W Bengal India Inst Clar Documentat Kharagpur W Bengal India

ISBN: (纸本)9781450366151

In this work a memory efficient topological map generation algorithm has been proposed using local descriptors. A topological map is a graphical data structure where each node signifies an area within an environment. These nodes are connected by links which ensure the presence of a physical path between the pair. Experiments have been conducted with feature descriptors using a vocabulary based approach. These approaches take huge memory and time. To deal with these a KD-tree based map generation algorithm has been proposed where each node in the tree stores a descriptor and a table of occurrence. This table stores node ids of the locations, where the corresponding descriptor is present. The map generation algorithm is a two-stage algorithm. In the first stage, the visual similarity based position identification is conducted in order to check for loop-closures. It is followed by a corrective step on validating the decision of loop closure, if any. The table of occurrence keeps track of presence of each descriptor. The least occurring descriptors are pruned at regular intervals, making the algorithm memory-efficient. The approach has been experimented with several benchmark datasets.

关键词： KD-tree Topological Map Loop Closure Local descriptor

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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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Bag of Visual Words based Correlation Filter Tracker (BoVW-CFT) 11

Bag of Visual Words based Correlation Filter Tracker (BoVW-C...

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

作者： Raju, Priya Mariam Mishra, Deepak Subrahmanyam, Gorthi Rama Krishna Sai Indian Inst Space Sci & Technol Thiruvananthapuram Kerala India Indian Inst Technol Tirupati Andhra Pradesh India

ISBN: (纸本)9781450366151

Accurate and robust visual object tracking is one of the most challenging computer vision problems. Recently, discriminative correlation filter trackers have shown promising results on benchmark datasets with continuous performance improvements in tracking accuracy and robustness. Still, these algorithms fail to track as the target object and background conditions undergo drastic changes over time. They are also incapable to resume tracking once the target is lost, limiting the ability to track long term. The proposed BoVW-CFT is a classifier-based generic technique to handle tracking uncertainties in correlation filter trackers. Tracking failures in correlation trackers are automatically identified and an image classifier with training, testing and online update stages is proposed as detector in the tracking scenario using Bag of VisualWords (BoVW) features. The proposed detector falls under the parts based model and is quite well suited in the tracking framework. Further, the online training stage in the proposed framework with updated model or training samples, incorporates temporal information, helping to detect rotated, blurred and scaled versions of the target. On detecting a target loss in the correlation tracker, the trained classifier, referred to as detector, is invoked to re-initialize the tracker with the actual target location. Therefore, for each tracking uncertainty, two output patches are obtained, one each from the base tracker and the classifier. The final target location is estimated using the normalized cross-correlation with the initial target patch. The method has the advantages of mitigating the model drift in correlation trackers and learns a robust model that tracks long term. Extensive experimental results demonstrate an improvement of 4.1% in the expected overlap, 1.86% in accuracy and 15.46% in robustness on VOT2016 and 1.82% in overlap precision, 2.32% in AUC and 2.87% in success rates on OTB100.

关键词： Benchmarking

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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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Salient Object Detection based on Bayesian Surprise of Restricted Boltzmann Machine 11

Salient Object Detection based on Bayesian Surprise of Restr...

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

作者： Roy, Rahul Ghosh, Susmita Ghosh, Ashish Indian Stat Inst Machine Intelligence Unit Kolkata W Bengal India Jadavpur Univ Dept Comp Sci & Technol Kolkata W Bengal India

ISBN: (纸本)9781450366151

This article presents an algorithm for salient object detection by leveraging the Bayesian surprise of the Restricted Boltzmann Machine (RBM). Here an RBMis trained on patches sampled randomly from the input image. Due to this random sampling, the RBM is likely to get more exposed to background patches than that of the object. Thus, the trained RBM will minimize the free energy of its hidden states with respect to the background patches as opposed to the object. This, according to the free energy principle, implies minimizing Bayesian surprise which is a measure for saliency based on Kullback Leibler divergence between the input and reconstructed patch distribution. Hence, when the trained RBM is exposed to patches from the object region, it would have high divergence and in turn a high Bayesian surprise. Thus such pixels with high Bayesian surprise could be considered as salient pixels. For each pixel, a neighborhood (with the same size of training patch) is considered and is fed to the trained RBM to obtain the reconstructed patch. Thereafter, the Kullback Leibler divergence between the input and reconstructed neighborhood of each pixel is computed to measure the Bayesian surprise and is stored in the corresponding position in a matrix to form the saliency map. Experiments are carried out on three datasets namelyMSRA-10K, ECSSD and DUTS. The results obtained depict promising performance by the proposed approach.

关键词： Object Detection Restricted Boltzmann Machine Bayesian Surprise Free Energy Principle

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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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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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proceedings of 2nd International conference on computer vision & image processing 1

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丛书名： Advances in Intelligent Systems and Computing

2018年

作者： Bidyut B. Chaudhuri Mohan S. Kankanhalli Balasubramanian Raman

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