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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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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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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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Voxel-based Morphometry and Minimum Redundancy Maximum Relevance Method for Classification of Parkinson's Disease and Controls from T1-Weighted MRI 16

Voxel-based Morphometry and Minimum Redundancy Maximum Relev...

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

作者： Rana, Bharti Juneja, Akanksha Saxena, Mohit Gudwani, Sunita Kumaran, S. Senthil Agrawal, R. K. Behari, Madhuri Jawaharlal Nehru Univ Sch Comp & Syst Sci Delhi India Case Western Reserve Univ Dept Neurol Case Med Ctr Cleveland OH 44106 USA All India Inst Med Sci Dept NMR Delhi India All India Inst Med Sci Dept Neurol Delhi India

ISBN: (纸本)9781450347532

Parkinson's disease (PD) is a neurodegenerative disorder, which needs to be accurately diagnosed in early stage. Voxel-based morphometry (VBM) has been extensively utilized to determine focal changes between PD patients and controls. However, it is not much utilized in differential diagnosis of an individual subject. thus, in this study, VBM findings in conjunction with minimum redundancy maximum relevance (mRMR) method are utilized to obtain a set of relevant and non-redundant features for computeraided diagnosis (CAD) of PD using T1-weighted MRI. In the proposed method, firstly, statistical features are extracted from the clusters obtained from statistical maps, generated using VBM, of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) independently and their different combinations. then mRMR, a multivariate feature selection method, is utilized to find a minimal set of relevant and non-redundant features. Finally, support vector machine is utilized to learn a decision model using the selected features. Experiments are performed on newly acquired T1-weighted MRI of 30 PD patients and 30 age & gender matched controls. the performance is evaluated using leave-one out cross-validation scheme in terms of sensitivity, specificity and classification accuracy. the maximum accuracy of 88.33% is achieved for GM+WM and GM+WM+CSF. In addition, the proposed method outperforms the existing methods. It is also observed that the selected clusters belong to regions namely middle and superior frontal gyrus for GM, inferior, middle frontal gyrus and insula for WM and lateral ventricle for CSF. Further, correlation of UPDRS/H&Y staging scale with GM/WM/CSF volume is observed to be not significant. Appreciable classification performance of the proposed method highlights the potential of the proposed method in CAD support system for the clinicians in PD diagnosis.

关键词： Parkinson's disease computer-aided diagnosis Voxel-based morphometry Minimum redundancy maximum relevance Magnetic resonance imaging

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Writer identification for handwritten words 10

Writer identification for handwritten words

引用

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

作者： Pandey, Shilpa Harit, Gaurav Indian Institute of Technology Jodhpur JodhpurRajasthan342011 India

In this work we present a framework for recognizing writer for a handwritten word. We make use of allographic features at sub-word level. Our work is motivated by previous techniques which make use of a codebook. However, instead of encoding the features using the code-words, we exploit the discriminative properties of features that belong to the same cluster, in a supervised approach. We are able to achieve writer identification rates close to 63% on the handwritten words drawn from a dataset by 10 writers. Our work has application in scenarios where multiple writers write/annotate on the same page. © Springer International Publishing AG 2017.

关键词： Character recognition

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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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ACM International conference Proceeding Series

ACM International Conference Proceeding Series

引用

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

ISBN: (纸本)9781450347532

the proceedings contain 95 papers. the topics discussed include: reinforced random forest;prototypes based discriminative appearance model for object tracking;adaptive artistic stylization of images;improving person re-identification systems: a novel score fusion framework for rank-n recognition;an image analysis approach for transcription of music played on keyboard-like instruments;deep automatic license plate recognition system;how much can a Gaussian smoother denoise?;supervised deep segmentation network for brain extraction;robust registration of mouse brain slices with severe histological artifacts;and hierarchical structured learning for indoor autonomous navigation of quadcopter.

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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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Cardiac ultrasound image enhancement using tissue selective total variation regularization 10

Cardiac ultrasound image enhancement using tissue selective ...

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

作者： Mishra, Deepak Chaudhury, Santanu Sarkar, Mukul Soin, Arvinder Singh Indian Institute of Technology Delhi New Delhi India Medanta Hospital Gurgaon India

Speckle reduction is desired to improve the quality of ultrasound images. However, a uniform speckle reduction from the entire image results in loss of important information, especially in cardiac ultrasound images. In this paper, a tissue selective total variation regularization approach is proposed for the enhancement of cardiac ultrasound images. It measures the pixel probability of belonging to blood regions and uses it in the total variation framework. As a result, the unwanted speckle from the blood chamber regions is removed and the useful speckle in the tissue regions is preserved. this helps to improve the visible contrast of the images and enhances the structural details. the proposed approach is evaluated using synthetic as well as real images. A better performance is observed as compared to the state-of-the-art filters in terms of speckle region’s signal to noise ratio, structural similarity measure index, figure of merit, and mean square error. © Springer International Publishing AG 2017.

关键词： Speckle

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