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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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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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Semi-automatic Vortex Flow Classification in 4D PC-MRI Data of the Aorta

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computer graphics FORUM 2016年第3期35卷 351-360页

作者： Meuschke, M. Koehler, B. Preim, U. Preim, B. Lawonn, K. Univ Magdeburg Dept Simulat & Graph D-39106 Magdeburg Germany Municipal Hosp Dept Diagnost Radiol Magdeburg Germany Univ Koblenz Landau Inst Computat Visualist Mainz Germany

We present an Aortic Vortex Classification (AVOCLA) that allows to classify vortices in the human aorta semi-automatically. Current medical studies assume a strong relation between cardiovascular diseases and blood flow patterns such as vortices. Such vortices are extracted and manually classified according to specific, unstandardized properties. We employ an agglomerative hierarchical clustering to group vortex-representing path lines as basis for the subsequent classification. Classes are based on the vortex' size, orientation and shape, its temporal occurrence relative to the cardiac cycle as well as its spatial position relative to the vessel course. the classification results are presented by a 2D and 3D visualization technique. To confirm the usefulness of both approaches, we report on the results of a user study. Moreover, AVOCLA was applied to 15 datasets of healthy volunteers and patients with different cardiovascular diseases. the results of the semi-automatic classification were qualitatively compared to a manually generated ground truth of two domain experts considering the vortex number and five specific properties.

关键词： Categories and Subject Descriptors (according to ACM CCS) I.4.9 [computer graphics]: image processing and computer vision&mdash Applications

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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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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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Methods and system for segmentation of isolated nuclei in microscopic breast fine needle aspiration cytology images 10

Methods and system for segmentation of isolated nuclei in mi...

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

作者： Garud, Hrushikesh Karri, Sri Phani Krishna Sheet, Debdoot Maity, Ashok Kumar Chatterjee, Jyotirmoy Mahadevappa, Manjunatha Ray, Ajoy Kumar Indian Institute of Technology Kharagpur Kharagpur721301 India Midnapur Medical College and Hospital Midnapur721101 India

computer vision systems for automated breast cancer diagnosis using Fine Needle Aspiration Cytology (FNAC) images are under development for a while now. Accurate segmentation of the nuclei in microscopic images is crucial for functioning of these systems, as most quantify and analyze nuclear features for diagnosis. this paper presents a nucleus segmentation system (NSS) involving pre-processing, pre-segmentation and refined segmentation stages. the NSS includes a novel pixel transformation step to create a high contrast grayscale representation of the input color image. the grayscale image gives NSS the capability-to disregard elements that mimic nuclear morphological and luminescence characteristics, and to minimize effects of non-specific staining of cytoplasm by Hematoxylin. Experimental results illustrate generalizability of the NSS to use multiple refined segmentation techniques and particularly achieve accurate nucleus segmentation using active contours without edges(F-score > 0.92). the paper also presents the results of experiments conducted to study the impact of image pre-processing steps on the NSS performance. the pre-processing steps are observed to improve accuracy and consistency across tested refined segmentation techniques. © Springer International Publishing AG 2017.

关键词： Cytology

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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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Writer identification for handwritten words 10

Writer identification for handwritten words

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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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ACM International conference Proceeding Series

ACM International Conference Proceeding Series

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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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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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