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Mosaicing Deep Underwater imagery 16

Mosaicing Deep Underwater Imagery

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

作者： Purohit, Kuldeep Vasu, Subeesh Rajagopalan, A. N. Jyothi, V. Bala Naga Raju, Ramesh IIT Madras Madras Tamil Nadu India Natl Inst Ocean Technol Madras Tamil Nadu India

ISBN: (纸本)9781450347532

Numerous sources of distortions render mosaicing of underwater (UW) images an immensely challenging effort. Methods that can process conventional photographs (terrestrial/aerial) fail to deliver the desired results on UW images. Taking the sources of underwater degradations into account is central to ensuring quality performance. The work described in this paper specifically deals with the problem of mosaicing deep UW images captured by Remotely Operated Vehicles (ROVs). These images are mainly degraded by haze, color changes, and non-uniform illumination. We propose a framework that restores these images in accordance with a suitably derived degradation model. Furthermore, our scheme harnesses the scene geometry information present in each image to aid in constructing a mosaic that is free from artifacts such as local blurring, ghosting, double contouring and visible seams. Several experiments on real underwater images sequences have been carried out to demonstrate the performance of our mosaicing pipeline along with comparisons.

关键词： Mosaic dehazing illumination correction scattering underwater

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Pap Smear image Classification Using Convolutional Neural Network 16

Pap Smear Image Classification Using Convolutional Neural Ne...

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

作者： Bora, Kangkana Chowdhury, Manish Mahanta, Lipi B. Kundu, Malay K. Das, Anup K. Inst Adv Study Sci & Technol Cent Computat & Numer Studies Gauhati Assam India KTH Sch Technol & Hlth Stockholm Sweden Indian Stat Inst Machine Intelligence Unit Kolkata India Ayursundra Healthcare Pvt Ltd Dept Pathol Gauhati Assam India

ISBN: (纸本)9781450347532

This article presents the result of a comprehensive study on deep learning based computer Aided Diagnostic techniques for classification of cervical dysplasia using Pap smear images. All the experiments are performed on a real indigenous image database containing 1611 images, generated at two diagnostic centres. Focus is given on constructing an effective feature vector which can perform multiple level of representation of the features hidden in a Pap smear image. For this purpose Deep Convolutional Neural Network is used, followed by feature selection using an unsupervised technique with Maximal Information Compression Index as similarity measure. Finally performance of two classifiers namely Least Square Support Vector Machine (LSSVM) and Softmax Regression are monitored and classifier selection is performed based on five measures along with five fold cross validation technique. Output classes reflects the established Bethesda system of classification for identifying pre-cancerous and cancerous lesion of cervix. The proposed system is also compared with two existing conventional systems and also tested on a publicly available database. Experimental results and comparison shows that proposed system performs efficiently in Pap smear classification.

关键词： Pap smear image Deep learning LSSVM Softmax Regression

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Complementary Tracker's Fusion for Robust Visual Tracking 16

Complementary Tracker's Fusion for Robust Visual Tracking

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

作者： Kakanuru, Sumithra Rapuru, Madan Kumar Mishra, Deepak Gorthi, Sai Subrahmanyam Indian Inst Space Sci & Technol Dept Avion Thiruvananthapuram Kerala India

ISBN: (纸本)9781450347532

Though visual object tracking algorithms are capable of handling various challenging scenarios individually, still none of them is robust enough to handle all the challenges simultaneously. This paper aims at proposing a novel robust tracking algorithm by elegantly fusing the frame level detection strategy of Tracking Learning & Detection (TLD) with systematic model update strategy of Kernelized Correlation Filter tracker (KCF). The motivation behind the selection of trackers is their complementary nature in handling tracking challenges. The proposed algorithm efficiently combines the two tracking algorithms based on conservative correspondence measure with strategic model updates, which takes advantages of both and outperforms them on their short-ends by the virtue of other. The proposed fusion approach is quite general and any complimentary tracker (not just KCF) can be fused with TLD to leverage the best performance. Extensive evaluation of the proposed method based on different metrics is carried out on the datasets ALOV300++, Online Tracking Benchmark (OTB) and Visual Object Tracking (VOT2015) and demonstrated its superiority in terms of robustness and success rate by comparing with state-of-theart trackers.

关键词： Visual tracking trackers fusion correspondence measure

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3D Binary Signatures 16

3D Binary Signatures

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

作者： Srivastava, Siddharth Lall, Brejesh Indian Inst Technol Dept Elect Engn Delhi India

ISBN: (纸本)9781450347532

In this paper, we propose a novel binary descriptor for 3D point clouds. The proposed descriptor termed as 3D Binary Signature (3DBS) is motivated from the matching efficiency of the binary descriptors for 2D images. 3DBS describes keypoints from point clouds with a binary vector resulting in extremely fast matching. The method uses keypoints from standard keypoint detectors. The descriptor is built by constructing a Local Reference Frame and aligning a local surface patch accordingly. The local surface patch constitutes of identifying nearest neighbours based upon an angular constraint among them. The points are ordered with respect to the distance from the keypoints. The normals of the ordered pairs of these keypoints are projected on the axes and the relative magnitude is used to assign a binary digit. The vector thus constituted is used as a signature for representing the keypoints. The matching is done by using hamming distance. We show that 3DBS outperforms state of the art descriptors on various evaluation metrics.

关键词： 3D Descriptors 3D Matching 3D Object Recognition

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Immersive Augmented Reality System for Assisting Needle Positioning During Ultrasound Guided Intervention 16

Immersive Augmented Reality System for Assisting Needle Posi...

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

作者： Kanithi, Praveen Kumar Chatterjee, Jyotirmoy Sheet, Debdoot Indian Inst Technol Sch Med Sci & Technol Kharagpur W Bengal India Indian Inst Technol Elect Engn Dept Kharagpur W Bengal India

ISBN: (纸本)9781450347532

Ultrasound (US) guided intervention is a surgical procedure where the clinician makes use of imaging in realtime, to track the position of the needle, and correct its trajectory for accurately steering it to the lesion of interest. However, the needle is visible in the US image, only when aligned in-plane with the scanning plane of the US probe. In practice, clinicians often use a mechanical needle guide, thus restricting their available degrees of freedom in the US probe movement. Alternatively, during free-hand procedure, they use multiple needle punctures to achieve this in-plane positioning. Our present work details an augmented reality (AR) system for patient comfort centric aid to needle intervention through an overlaid visualization of the needle trajectory on the US frame prior to its insertion. This is implemented by continuous visual tracking of the US probe and the needle in 3D world coordinate system using fiducial markers. The tracked marker positions are used to draw the needle trajectory and tip visualized in realtime to augment on the US feed. Subsequently, the continuously tracked US probe and needle, and the navigation assistance information, would be overlaid with the visual feed from a head mounted display (HMD) for generating totally immersive AR experience for the clinician.

关键词： Augmented reality fiducial markers head mounted display image guided interventions ultrasonography

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Deep Automatic Licence Plate Recognition system 10

Deep Automatic Licence Plate Recognition system

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

作者： Jain, Vishal Sasindran, Zitha Rajagopal, Anoop Biswas, Soma Bharadwaj, Harish S. Ramakrishnan, K. R. Indian Inst Sci Bengaluru Karnataka India Myntra Designs Bengaluru Karnataka India

ISBN: (纸本)9781450347532

Automatic License Plate Recognition (ALPR.) has important applications in traffic surveillance. It is a challenging problem especially in countries like in India where the license plates have varying sizes, number of lines, fonts etc. The difficulty is all the more accentuated in traffic videos as the cameras are placed high and most plates appear skewed. This work aims to address ALPR. using Deep CNN methods for real-time traffic videos. We first extract license plate candidates from each frame using edge information and geometrical properties, ensuring high recall. These proposals are fed to a CNN classifier for License Plate detection obtaining high precision. We then use a CNN classifier trained for individual characters along with a spatial transformer network (STN) for character recognition. Our system is evaluated on several traffic videos with vehicles having different license plate formats in terms of tilt, distances, colors, illumination, character size, thickness etc. Results demonstrate robustness to such variations and impressive performance in both the localization and recognition. We also make available the dataset for further research on this topic.

关键词： Convolution Neural Network Spatial Transformer Network image Transformation Pursuit Automatic License Plate Recognition

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Automatic Detection of Malaria Infected RBCs from a Focus Stack of Bright Field Microscope Slide images 16

Automatic Detection of Malaria Infected RBCs from a Focus St...

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

作者： Gopakumar, G. Swetha, M. Siva, Gorthi. Sai Subrahmanyam, G. R. K. S. Indian Inst Space Sci & Technol Dept ESS Trivandrum 695547 Kerala India Indian Inst Sci Dept Instrumentat & Appl Phys Bangalore 560012 Karnataka India Indian Inst Space Sci & Technol Dept Avion Trivandrum 695547 Kerala India

ISBN: (纸本)9781450347532

Malaria is a deadly infectious disease affecting red blood cells in humans due to the protozoan of type Plasmodium. In 2015, there is an estimated death toll of 438, 000 patients out of the total 214 million malaria cases reported worldwide. Thus, building an accurate automatic system for detecting the malarial cases is beneficial and has huge medical value. This paper addresses the detection of Plasmodium Falciparum infected RBCs from Leishman's stained microscope slide images. Unlike the traditional way of examining a single focused image to detect the parasite, we make use of a focus stack of images collected using a bright field microscope. Rather than the conventional way of extracting the specific features we opt for using Convolutional Neural Network that can directly operate on images bypassing the need for hand-engineered features. We work with image patches at the suspected parasite location there by avoiding the need for cell segmentation. We experiment, report and compare the detection rate received when only a single focused image is used and when operated on the focus stack of images. Altogether the proposed novel approach results in highly accurate malaria detection.

关键词： Malaria Diagnosis Plasmodium Falciparum CNN

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Bangla Online Handwriting Recognition Using Recurrent Neural Network Architecture 16

Bangla Online Handwriting Recognition Using Recurrent Neural...

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

作者： Chakraborty, Bappaditya Mukherjee, Partha Sarathi Bhattacharya, Ujjwal Univ Calcutta AKCSIT Kolkata W Bengal India Indian Stat Inst CVPR Unit Kolkata W Bengal India

ISBN: (纸本)9781450347532

Recognition of unconstrained handwritten texts is always a difficult problem, particularly if the style of handwriting is a mixed cursive one. Among various indian scripts, only Bangla has this additional difficulty of tackling mixed cursiveness of its handwriting style in the pipeline of a method towards its automatic recognition. A few other common recognition difficulties of handwriting in an indian script include the large size of its alphabet and the extremely cursive nature of the shapes of its alphabetic characters. These are among the reasons of achieving only limited success in the study of unconstrained handwritten Bangla text recognition. Artificial Neural Network (ANN) models have often been used for solving difficult real-life pattern recognition problems. Recurrent Neural Network models (RNN) have been studied in the literature for modeling sequence data. In this study, we consider Long Short Term Memory (LSTM) network model, a useful member of this family. In fact, Bidirectional Long Short-Term Memory (BLSTM) neural networks is a special kind of RNN and have recently attracted special attention in solving sequence labelling problems. In this article, we present a BLSTM architecture based approach for unconstrained online handwritten Bangla text recognition.

关键词： RNN BLSTM-CTC Online Handwriting Recognition

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Spectral Decomposition and Progressive Reconstruction of Scalar Volumes 16

Spectral Decomposition and Progressive Reconstruction of Sca...

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

作者： Mukherjee, Uddipan Univ Calif Irvine Irvine CA 92697 USA

ISBN: (纸本)9781450347532

Modern 3D imaging technologies often generate large scale volume datasets that may be represented as 3-way tensors. These volume datasets are usually compressed for compact storage, and interactive visual analysis of the data warrants efficient decompression techniques at real time. Using well known tensor decomposition techniques like CP or Tucker decomposition the volume data can be represented by a few basis vectors, the number of such vectors, called the rank of the tensor, determining the visual quality. However, in such methods, the basis vectors used between successive ranks are completely different, thereby requiring a complete recomputation of basis vectors whenever the visual quality needs to be altered. In this work, a new progressive decomposition technique is introduced for scalar volumes wherein new basis vectors are added to the already existing lower rank basis vectors. Large scale datasets are usually divided into bricks of smaller size and each such brick is represented in a compressed form. The bases used for the different bricks are data dependent and are completely different from one another. The decomposition method introduced here uses the same basis vectors for all the bricks at all hierarchical levels of detail. The basis vectors are data independent thereby minimizing storage and allowing fast data reconstruction.

关键词： Brick

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Deep Neural Networks for Segmentation of Basal Ganglia Sub-Structures in Brain MR images 16

Deep Neural Networks for Segmentation of Basal Ganglia Sub-S...

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

作者： Sethi, Akshay Sinha, Akshat Agarwal, Ayush Arora, Chetan Gupta, Anubha Indraprastha Inst Informat Technol Delhi Delhi India

ISBN: (纸本)9781450347532

Automated segmentation of brain structure in magnetic resonance imaging (MRI) scans is an important first step in diagnosis of many neurological diseases. In this paper, we focus on segmentation of the constituent sub-structures of basal ganglia (BG) region of the brain that are responsible for controlling movement and routine learning. Low contrast voxels and undefined boundaries across sub-regions of BG pose a challenge for automated segmentation. We pose the segmentation as a voxel classification problem and propose a Deep Neural Network (DNN) based classifier for BG segmentation. The DNN is able to learn distinct regional features for voxel-wise classification of BG area into four sub-regions, namely, Caudate, Putamen, Pallidum, and Accumbens. We use a public dataset with a collection of 83 T-1 weighted uniform dimension structural MRI scans of healthy and diseased (Bipolar with and without Psychosis, Schizophrenia) subjects. In order to build a robust classifier, the proposed classifier has been trained on a mixed collection of healthy and diseased MRs. We report an accuracy of above 94% (as calculated using the dice coefficient) for all the four classes of healthy and diseased dataset.

关键词： Basal Ganglia MR segmentation Deep Neural Network

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