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检索条件"任意字段=Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing"

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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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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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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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Color Restoration in Turbid Medium 16

Color Restoration in Turbid Medium

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

作者： Nimisha, T. M. Seemakurthy, Karthik Rajagopalan, A. N. Vedachalam, Narayanaswamy Raju, Ramesh IIT Madras Madras Tamil Nadu India Natl Inst Ocean Technol Madras Tamil Nadu India

ISBN: (纸本)9781450347532

Light scattering and color distortions are two major issues with underwater imaging. Scattering occurs due to turbidity of the medium and color distortions are caused by differential attenuation of wavelengths as a function of depth. As a result, underwater images taken in a turbid medium have low contrast, color cast, and color loss. The main objective of this work is color restoration of underwater images i.e, produce its equivalent image as seen outside of the water surface. As a first step, we account for low contrast by employing dark channel prior based dehazing. These images are then color corrected by learning a mapping function between a pair of color chart images, one taken inside water and another taken outside. The mapping thus learned is with respect to a reference distance from the water surface. We also propose a color modulation scheme that is applied prior to color mapping to accommodate the same mapping function for different depths as well. Color restoration results are given on several images to validate the efficacy of the proposed methodology.

关键词： Color mapping color modulation white balancing boosting dehazing

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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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A Differential Excitation Based Rotational Invariance for Convolutional Neural Networks 16

A Differential Excitation Based Rotational Invariance for Co...

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

作者： Kandi, Haribabu Mishra, Deepak Subrahmanyam, G. R. K. S. Indian Inst Space Sci & Technol Dept Avion Trivandrum 695547 Kerala India

ISBN: (纸本)9781450347532

Deep Learning (DL) methods extract complex set of features using architectures containing hierarchical set of layers. The features so learned have high discriminative power and thus represents the input to the network in the most efficient manner. Convolutional Neural Networks (CNN) are one of the deep learning architectures, extracts structural features with little invariance to smaller translational, scaling and other forms of distortions. In this paper, the learning capabilities of CNN's are explored towards providing improvement in rotational invariance to its architecture. We propose a new CNN architecture with an additional layer formed by differential excitation against distance for the improvement of rotational invariance and is called as RICNN. Moreover, we show that the proposed method is giving superior performance towards invariance to rotations against the original CNN architecture (training samples with different orientations are not considered) without disturbing the invariance to smaller translational, scaling and other forms of distortions. Different profiles like training time, testing time and accuracies are evaluated at different percentages of training data for comparing the performance of the proposed configuration with original configuration.

关键词： Differential Excitation Rotational Invariance Deep Learning Convolutional Neural Networks Back Propagation

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Don't See Me, Just Filter Me: Towards secure cloud based filtering using Shamir's secret sharing and POB number system 16

Don't See Me, Just Filter Me: Towards secure cloud based fil...

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

作者： Singh, Priyanka Agarwal, Nishant Raman, Balasubramanian Indian Inst Technol Roorkee Uttar Pradesh India Natl Inst Technol Kurukshetra Kurukshetra Haryana India

ISBN: (纸本)9781450347532

Cloud computing paradigm is attracting individuals as well as organizations all round the globe due to its multiple facilities of powerful resources, storage hubs, computational power and cost effective solutions. However, distributed cloud data centers pose the threat of security breaches to the privacy of the data and pose high risks. A secured image data sharing scheme over cloud domain based on the Shamir's secret sharing and permutation ordered binary number system has been proposed here. It distributes the image information into multiple random shares that reveal no information and thus can be stored securely over the distributed cloud data centers. Different image operations could be applied in the encrypted domain over the cloud servers itself. This approach reduces the threat to security which is a major hindrance in utilizing the facilities of the cloud based architecture. Only the authentic owner possessing the secret keys could restore back the original image information from the random shares. Comparative results for both the plain domain as well as the encrypted domain have been presented to validate the efficiency of using the various image filtering operations like motion blur, unsharp masking, weiner, gaussian etc in the encrypted domain over cloud.

关键词： Cloud Computing Shamir's Secret Sharing Permutation Ordered Binary Number System Encrypted Domain

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Prototypes based Discriminative Appearance Model for Object Tracking 16

Prototypes based Discriminative Appearance Model for Object ...

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

作者： Mondal, Ajoy Ghosh, Ashish Ghosh, Susmita Indian Stat Inst Machine Intelligence Unit Kolkata India Jadavpur Univ Dept CSE Kolkata India

ISBN: (纸本)9781450347532

Occlusion is one of the major challenges for object tracking in real life scenario. Various techniques in particle filter framework have been developed to solve this problem. This framework depends on two issues: motion model and observation (likelihood) model. Due to the lack of effective observation model and efficient motion model, problem of occlusion still remains unsolvable in the tracking task. In this article, an effective observation model is proposed based on confidence (classification) score provided by the developing online prototypes based discriminative appearance model. This appearance model is constructed with the prior knowledge of two classes (object and background) and tries to discriminate between three classes such as object, background and occluded part of the object. The considered composite motion model can handle both the object motion as well as scale change of the object. The proposed update mechanism is able to adapt the appearance changes during tracking. We show a realization of the proposed method and demonstrate its performance (both quantitatively and qualitatively) with respect to state-of-the-art techniques on several challenging sequences. Analysis of the results concludes that the proposed technique can track (fully or partially) occluded objects as well as objects in various complex environments in a better way as compared to the existing ones.

关键词： Occluded object prototypes likelihood

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MPMF: Multi-part Multi-feature Based Object Tracking 16

MPMF: Multi-part Multi-feature Based Object Tracking

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

作者： Bhargava, Neha Chaudhuri, Subhasis Indian Inst Technol Bombay Maharashtra India

ISBN: (纸本)9781450347532

The objective of tracking is to determine the states of an object in video frames while maintaining appearance and motion consistency. In this paper, we propose a novel multi-part multi-feature (MPMF) based object tracking which falls in the category of part-based trackers. We represent a target by a set of fixed parts (not semantic as body parts such as limbs, face) and each part is represented by a set of features. The multi-part representation of the object aids in partial occlusion handling and the multi-feature based object description increases robustness of the target representation. Instead of considering all the features of the parts, we measure tracker's confidence for a candidate by utilizing only the strong features of the candidate. This ensures that weak features do not interfere in the decision making. We also present an automatic method for selecting this subset of appropriate features for each part. To increase the tracker's speed and to reduce the number of erroneous candidates, we do not search in the whole frame. We keep the size of search area adaptive that depends on the tracker's confidence for the predicted location of the object. Additionally, it is easy to integrate more parts and features to the proposed tracker. The results on various challenging videos from VOT dataset are encouraging. MPMF outperforms state-of-the-art trackers on some of the standard challenging videos.

关键词： tracking part based algorithm feature selection

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Limitations with Measuring Performance of Techniques for Abnormality Localization in Surveillance Video and How to Overcome Them? 16

Limitations with Measuring Performance of Techniques for Abn...

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

作者： Sharma, Manoj Kumar Sarcar, Sayan Sheet, Debdoot Biswas, Prabir Kumar Indian Inst Technol Kharagpur Kharagpur W Bengal India Kochi Univ Technol Kami Kochi Japan

ISBN: (纸本)9781450347532

Now a days video surveillance is becoming more popular due to global security concerns and with the increasing need for effective monitoring of public places. The key goal of video surveillance is to detect suspicious or abnormal behavior. Various efforts have been made to detect an abnormality in the video. Further to these advancements, there is a need for better techniques for evaluation of abnormality localization in video surveillance. Existing technique mainly uses forty percent overlap rule with ground-truth data, and does not considers the extra predicted region into the computation. Existing metrics have been found to be inaccurate when more than one region is present within the frame which may or may not be correctly localized or marked as abnormal. This work attempts to bridge these limitations in existing metrics. In this paper, we investigate three existing metrics and discuss their benefits and limitations for evaluating localization of abnormality in video. We further extend the existing work by introducing penalty functions and substantiate the validity of proposed metrics with a sufficient number of instances. The presented metric are validated on data (35 different situations) for which the overlap has been computed analytically.

关键词： Video surveillance Abnormality localization Evaluation technique

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