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Reconstruction of Sparse-View Tomography via Banded Matrices 10

Reconstruction of Sparse-View Tomography via Banded Matrices

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

作者： Prasad, T. Kumar, P. U. Praveen Sastry, C. S. Jampana, P. V. Indian Inst Technol Hyderabad Dept Math Hyderabad Telangana India Indian Inst Technol Hyderabad Dept Chem Engn Hyderabad Telangana India

ISBN: (纸本)9783319681245;9783319681238

Computed Tomography (CT) is one of the significant research areas in medical image analysis. One of the main aspects of CT that researchers remain focused, is on reducing the dosage as Xrays are generally harmful to human bodies. In order to reduce radiation dosage, compressed sensing (CS) based methodologies appear to be promising. the basic premise is that medical images have inherent sparsity in some transformation domain. As a result, CS provides the possibility of recovering a high quality image from fewer projection data. In general, the sensing matrix in CT is generated from Radon projections by appropriately sampling the radial and angular parameters. In our work, by restricting the number of such parameters, we generate an under-determined linear system involving projection (Radon) data and a sparse sensing matrix, bringing thereby the problem into CS framework. Among various recent solvers, the Split-Bregman iterative scheme has of late become popular due to its suitability for solving a wide variety of optimization problems. Intending to exploit the underlying structure of sensing matrix, the present work analyzes its properties and finds a banded structure for an associated intermediate matrix. Using this observation, we simplify the Split-Bregman solver, proposing thereby a CT-specific solver of low complexity. We also provide the efficacy of proposed method empirically.

关键词： Compressed sensing Split-Bregman Computed tomography Least squares

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DeepFly: Towards Complete Autonomous Navigation of MAVs with Monocular Camera 16

DeepFly: Towards Complete Autonomous Navigation of MAVs with...

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

作者： Shah, Utsav Khawad, Rishabh Krishna, K. Madhava IIIT Hyderabad KCIS Robot Res Ctr Hyderabad Andhra Pradesh India

ISBN: (纸本)9781450347532

Recently, the interest in Micro Aerial Vehicles (MAVs) and their autonomous flights has increased tremendously and significant advances have been made. the monocular camera has turned out to be most popular sensing modality for MAVs as it is light-weight, does not consume more power, and encodes rich information about the environment around. In this paper, we present DeepFly, our framework for autonomous navigation of a quadcopter equipped with monocular camera. the navigable space detection and waypoint selection are fundamental components of autonomous navigation system. they have broader meaning than just detecting and avoiding immediate obstacles. Finding the navigable space emphasizes equally on avoiding obstacles and detecting ideal regions to move next to. the ideal region can be defined by two properties: 1) All the points in the region have approximately same high depth value and 2) the area covered by the points of the region in the disparity map is considerably large. the waypoints selected from these navigable spaces assure collision-free path which is safer than path obtained from other waypoint selection methods which do not consider neighboring information. In our approach, we obtain a dense disparity map by performing a translation maneuver. this disparity map is input to a deep neural network which predicts bounding boxes for multiple navigable regions. Our deep convolutional neural network with shortcut connections regresses variable number of outputs without any complex architectural add on. Our autonomous navigation approach has been successfully tested in both indoors and outdoors environment and in range of lighting conditions.

关键词： Micro Aerial Vehicles Autonomous Navigation Deep Learning

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Info-graphics retrieval: A multi-kernel distance based hashing scheme 10

Info-graphics retrieval: A multi-kernel distance based hashi...

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10th indian conference on computer vision, graphics and image processing, icvgip 2016

作者： Garg, Ritu Chaudhury, Santanu Department of Electrical Engineering Indian Institute of Technology Delhi Delhi India

Information retrieval research has shown significant improvement and provided techniques that retrieve documents in image or text form. However, retrieval of multi-modal documents has been given very less attention. We aim to build a system for retrieval of documents with embedded information graphics (Info-graphics). Info-graphics are images of bar charts and line graphs appearing with textual components in magazines, newspapers, and journals. In this paper, we present multi-modal document image retrieval framework by learning an optimal fusion of information from text and info-graphics regions. the evaluation of the proposed concept is demonstrated on documents collected from various sources such as magazines and journals. © Springer International Publishing AG 2017.

关键词： image enhancement

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COMA-Boost: Co-Operative Multi Agent AdaBoost 16

COMA-Boost: Co-Operative Multi Agent AdaBoost

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

作者： Lahiri, Avisek Paria, Biswajit Biswas, Prabir Kumar IIT Kharagpur Dept E&ECE Kharagpur W Bengal India IIT Kharagpur Dept CSE Kharagpur W Bengal India

ISBN: (纸本)9781450347532

A Abstract : Multi feature space representation is a common practise in computer vision applications. Traditional features such as HOG, SIFT, SURF etc., individually encapsulates certain discriminative cues for visual classification. On the other hand, each layer of a deep neural network generates multi ordered representations. In this paper we present a novel approach for such multi feature representation learning using Adaptive Boosting (AdaBoost). General practise in AdaBoost [8] is to concatenate components of feature spaces and train base learners to classify examples as correctly/incorrectly classified. We posit that multi feature space learning should be viewed as a derivative of cooperative multi agent learning. To this end, we propose a mathematical framework to leverage performance of base learners over each feature space, gauge a measure of "difficulty" of training space and finally make soft weight updates rather than strict binary weight updates prevalent in regular AdaBoost. this is made possible by periodically sharing of response states by our learner agents in the boosting framework. theoretically, such soft weight update policy allows in finite combinations of weight updates on training space compared to only two possibilities in AdaBoost. this opens up the opportunity to identify 'more difficult' examples compared to 'less difficult' examples. We test our model on traditional multi feature representation of MNIST handwritten character dataset and 100-Leaves classification challenge. We consistently outperform traditional and variants of multi view boosting in terms of accuracy while margin analysis reveals that proposed method fosters formation of more con fident ensemble of learner agents. As an application of using our model in conjecture with deep neural network, we test our model on the challenging task of retinal blood vessel segmentation from fundus images of DRIVE dataset by using kernel dictionaries from layers of unsupervised trained stac

关键词： AdaBoost multi view learning deep learning

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Low Complexity Encoder for Feedback-Channel-Free Distributed Video Coding using Deep Convolutional Neural Networks at the Decoder 16

Low Complexity Encoder for Feedback-Channel-Free Distributed...

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

作者： Bhagath, P. Raj Mukherjee, Jayanta Mukopadhayay, Sudipta Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur W Bengal India Indian Inst Technol Dept Comp Sci & Engn Kharagpur W Bengal India

ISBN: (纸本)9781450347532

We propose a very low complexity encoder for feedback channel-free distributed video coding (DVC) applications using deep convolutional neural network (CNN) at the decoder side. Deep CNN on super resolution uses low resolution (LR) images with 25% pixels information of high resolution (HR) image to super resolve it by the factor 2. Instead we train the network with 50% of noisy Wyner-Ziv (WZ) pixels to get full original WZ frame. So at the decoder, deep CNN reconstructs the original WZ image from 50% noisy WZ pixels. these noisy samples are obtained from the iterative algorithm called DLRTex. At the encoder side we compute local rank transform (LRT) of WZ frames for alternate pixels instead of all to reduce bit rate and complexity. these local rank transformed values are merged and their rank positions in the WZ frame are entropy coded using MQ-coder. In addition, average intensity values of each block of WZ frame are also transmitted to assist motion estimation. At the decoder, side information (SI) is generated by implementing motion estimation and compensation in LRT domain. the DLRTex algorithm is executed on SI using LRT to get the 50% noisy WZ pixels which are used in reconstructing full WZ frame. We compare our results with pixel domain DVC approaches and show that the coding efficiency of our codec is better than pixel domain distributed video coders based on low-density parity check and accumulate (LDPCA) or turbo codes. We also derive the complexity of our encoder interms of number of operations and prove that its complexity is very less compared to the LDPCA based methods.

关键词： Deep convolutional neural network (CNN) Local rank transform (LRT) delta-LRT with extended neighborhood (DLRTex)

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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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SegFast : A Faster SqueezeNet based Semantic image Segmentation Technique using Depth-wise Separable Convolutions 11

SegFast : A Faster SqueezeNet based Semantic Image Segmentat...

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

作者： Pal, Anisha Jaiswal, Shourya Ghosh, Swarnendu Das, Nibaran Nasipuri, Mita Manipal Inst Technol Dept Informat & Commun Technol Manipal Karnataka India Manipal Inst Technol Dept Elect & Commun Engn Manipal Karnataka India Jadavpur Univ Dept Comp Sci & Engn Kolkata W Bengal India

ISBN: (纸本)9781450366151

Recent trends in image segmentation algorithms have shown various large scale networks with impressive performance for natural scene images. However most of the networks come with costly overheads such has large memory requirements or dependence on huge number of parallel processing units. In most cases costly graphics processing units or GPUs are used to boost computational capability. However for creating products in the real world we need to consider speed, performance as well as cost of deployment. We propose a novel "spark" module which is a combination of the "fire" module of SqueezeNet and depth-wise separable convolutions. Along with this modified SqueezeNet as an encoder we also propose the use of depth-wise separable transposed convolution for a decoder. the resultant encoder-decoder network has approximately 49 times lesser number of parameters than SegNet and almost 223 times lesser number of parameters than fully convolutional networks(FCN). Even in a CPU the network completes a forward pass for a single sample in approximately 0.39 seconds which is almost 5.1 times faster as compared to SegNet and almost 8.7 times faster compared to FCN.

关键词： Deep Learning image Segmentation Squeeze Net Depthwise separable convolution

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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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Anveshak - A groundtruth generation tool for foreground regions of document images 10

Anveshak - A groundtruth generation tool for foreground regi...

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10th indian conference on computer vision, graphics and image processing, icvgip 2016

作者： Dey, Soumyadeep Mukherjee, Jayanta Sural, Shamik Nandedkar, Amit Vijay Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur721302 India

We propose a graphical user interface based groundtruth generation tool in this paper. Here, annotation of an input document image is done based on the foreground pixels. Foreground pixels are grouped together with user interaction to form labeling units. these units are then labeled by the user with the user defined labels. the output produced by the tool is an image with an XML file containing its metadata information. this annotated data can be further used in different applications of document image analysis. © Springer International Publishing AG 2017.

关键词： Pixels

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Scale-invariant image inpainting using gradient-based image composition 10

Scale-invariant image inpainting using gradient-based image ...

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10th indian conference on computer vision, graphics and image processing, icvgip 2016

作者： Ghorai, Mrinmoy Samanta, Soumitra Chanda, Bhabatosh Indian Statistical Institute Kolkata India

In this paper, we propose a novel scale-invariant image inpainting algorithm that combines several inpainted images obtained from multiple pyramids of different coarsest scales. To achieve this, first we build the pyramids and then we run an image inpainting algorithm individually on each of the pyramids to obtain different inpainted images. Finally, we combine those inpainted images by a gradient based approach to obtain the final inpainted image. the motivation of this approach is to solve the problem of appearing artifacts in traditional single pyramid-based approach since the results depend on the starting scale of the pyramid. Here we assume that most of the inpainted images produced by the pyramids are quite good. However, some of them may have artifacts and these artifacts are eliminated by gradient based image composition. We test the proposed algorithm on a large number of natural images and compare the results with some of the existing methods to demonstrate the efficacy and superiority of the proposed method. © Springer International Publishing AG 2017.

关键词： image processing

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