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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 1496 条记录，以下是461-470 订阅

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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...

引用

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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Epipolar Geometry based Learning of Multi-view Depth and Ego-Motion from Monocular Sequences 11

Epipolar Geometry based Learning of Multi-view Depth and Ego...

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

作者： Prasad, Vignesh Das, Dipanjan Bhowmick, Brojeshwar TCS Res & Amp Innovat Embedded Syst & Amp Robot Kolkata India

ISBN: (纸本)9781450366151

Deep approaches to predict monocular depth and ego-motion have grown in recent years due to their ability to produce dense depth from monocular images. The main idea behind them is to optimize the photometric consistency over image sequences by warping one view into another, similar to direct visual odometry methods. One major drawback is that these methods infer depth from a single view, which might not effectively capture the relation between pixels. Moreover, simply minimizing the photometric loss does not ensure proper pixel correspondences, which is a key factor for accurate depth and pose estimations. In contrast, we propose a 2-view depth network to infer the scene depth from consecutive frames, thereby learning inter-pixel relationships. To ensure better correspondences, thereby better geometric understanding, we propose incorporating epipolar constraints to make the learning more geometrically sound. We use the Essential matrix obtained using Nister's Five Point Algorithm, to enforce meaningful geometric constraints, rather than using it as training labels. This allows us to use lesser no. of trainable parameters compared to state-of-the-art methods. The proposed method results in better depth images and pose estimates, which capture the scene structure and motion in a better way. Such a geometrically constrained learning performs successfully even in cases where simply minimizing the photometric error would fail.

关键词： Monocular Depth Visual Odometry Epipolar Geometry computer vision Unsupervised Learning

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Regularized Random Walk Ranking for Co-Saliency Detection in images 11

Regularized Random Walk Ranking for Co-Saliency Detection in...

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

作者： Bardhan, Sayanti Jacob, Shibu Indian Inst Technol Madras Madras Tamil Nadu India Natl Inst Ocean Technol Madras Tamil Nadu India

ISBN: (纸本)9781450366151

Co-saliency detection refers to the computational process for identification of common but prominent and salient foreground regions in an image. However most of the co-saliency detection methods suffer from the following two limitations. First, co-saliency detection models largely generate superpixel level co-saliency maps that leads to sacrifice of significant information from the pixel level input images. Second, co-saliency detection frameworks mostly involve redesigned models for detection of co-salient objects in an image group, instead of utilization of the existing single image saliency detection models. To address these problems, we propose a novel framework, Co-saliency via Regularized Random Walk Ranking (CR2WR), which provides highly efficient pixel level co-saliency maps and utilizes existing saliency models on a single image to detect co-salient objects in an image sequence. This is achieved by: (1) Introducing Regularized random walk as the ranking function for a two-stage co-saliency detection framework. (2) Novel weighting function to incorporate more image information in graph construction and utilization of normalized Laplacian matrix for efficient cosaliency maps. (3) Generated saliency maps are fused further with high level priors namely, Location and Objectness priors, that enhances detection of co-salient regions. Suitably designed novel objective functions provide an enriched solution. The proposed model is evaluated on challenging benchmark co-saliency datasets. It is demonstrated that the proposed method outperforms prominent state-of-the-art methods in terms of efficiency and computational time.

关键词： Computational efficiency

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Jointly Learning Convolutional Representations to Compress Radiological images and Classify Thoracic Diseases in the Compressed Domain 11

Jointly Learning Convolutional Representations to Compress R...

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

作者： Ranjan, Ekagra Paul, Soumava Kapoor, Siddharth Kar, Aupendu Sethuraman, Ramanathan Sheet, Debdoot Indian Inst Technol Gauhati India Indian Inst Technol Kharagpur W Bengal India Natl Inst Technol Mangalore Karnataka India Intel Technol India Pvt Ltd Bangalore Karnataka India

ISBN: (纸本)9781450366151

Deep learning models trained in natural images are commonly used for different classification tasks in the medical domain. Generally, very high dimensional medical images are down-sampled by using interpolation techniques before feeding them to deep learning models that are imageNet compliant and accept only low-resolution images of size 224 x 224 px. This popular technique may lead to the loss of key information thus hampering the classification. Significant pathological features in medical images typically being small sized and highly affected. To combat this problem, we introduce a convolutional neural network (CNN) based classification approach which learns to reduce the resolution of the image using an autoencoder and at the same time classify it using another network, while both the tasks are trained jointly. This algorithm guides the model to learn essential representations from high-resolution images for classification along with reconstruction. We have used the publicly available dataset of chest x-rays to evaluate this approach and have outperformed state-of-the-art on test data. Besides, we have experimented with the effects of different augmentation approaches in this dataset and report baselines using some well known imageNet class of CNNs.

关键词： Convolutional autoencoder compression X-Ray classification

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