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Neovascularization detection on retinal images 10

Neovascularization detection on retinal images

引用

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

作者： Kar, Sudeshna Sil Maity, Santi P. Maity, Seba Department of Information Technology Indian Institute of Engineering Science and Technology Shibpur Howrah711103 India Department of Electronics and Telecommunication Engineering College of Engineering and Management Kolaghat Kolaghat India

Proliferative Diabetic Retinopathy (PDR) is characterized by the growth of new abnormal, thin blood vessels called neovascu-larzation that spread along the retinal surface. An automated computer aided diagnosis system needs to identify neovasculars for PDR screening. Retinal images are often noisy and poorly illuminated. The thin vessels mostly appear to be disconnected and are inseparable from the background. This paper proposes a new method for neovascularization detection on retinal images. Blood vessels are extracted as thick, medium and thin types using multilevel thresholding on matched filter response. The total mutual information between the vessel density and the tortuosity of the thin vessel class is maximized to obtain the optimal thresholds to classify the normal and the abnormal vessels. Simulation results demonstrate that the proposed method outperforms the existing ones for neovascularization detection with an average accuracy of 97.54%. © Springer International Publishing AG 2017.

关键词： Blood vessels

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Dictionary based approach for facial expression recognition from static images 10

Dictionary based approach for facial expression recognition ...

引用

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

作者： Sharma, Krishan Rameshan, Renu School of Computing and Electrical Engineering Indian Institute of Technology MandiHimachal Pradesh India

We present a simple approach for facial expression recognition from images using the principle of sparse representation using a learned dictionary. Visual appearance based feature descriptors like histogram of oriented gradients (HOG), local binary patterns (LBP) and eigenfaces are used. We use Fisher discrimination dictionary which has discrimination capability in addition to being reconstructive. The classification is based on the fact that each expression class with in the dictionary spans a subspace and these subspaces have non-overlapping directions so that they are widely separated. Each test feature point has a sparse representation in the union of subspaces of dictionary formed by labeled training points. To check recognition performance of the proposed approach, extensive experimentation is done over Jaffee and CK databases. Results show that the proposed approach has better classification accuracy than state-of-the-art techniques. © Springer International Publishing AG 2017.

关键词： Local binary pattern

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Web-Based Physics Experiments in Dynamics Using image processing 1

Web-Based Physics Experiments in Dynamics Using Image Proces...

引用

1st IEEE International conference on Power Electronics, Intelligent Control, and Energy Systems (ICPEICES)

作者： Pattanashetty, Kiran C. Kumar, Ranjeet Pandian, S. R. Indian Inst Informat Technol Design & Mfg Kanchee Dept Elect Engn Madras Tamil Nadu India

ISBN: (纸本)9781467385879

Web-based laboratories are promising to save the cost required to setup a hands-on laboratory in schools. There are several basic experiments in physics which help the students understand the basics of classical physics. However, the rising cost of laboratory infrastructure and costly equipment to conduct these experiments make it difficult for schools, in developing countries like India to build laboratories. This paper presents innovative web-based laboratory for physics to study equations of motion. One such experiment in equations of motion is the determination of acceleration due to gravity of a free falling object. However, measuring the distance traveled and time taken by a free-falling object is a tedious process. To overcome this challenge, a computer vision based system is presented which can measure the distance traveled and time taken by the object during free fall. By the technique of object detection in computer vision, the free-falling object is detected and tracked. The system is implemented using OpenCV libraries. The result of the analysis of free falling object is provided to the students through the web page.

关键词： OpenCV Object Detection Acceleration Due to Gravity Dynamics Experiment

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Text and non-text separation in scanned color-official documents 10

Text and non-text separation in scanned color-official docum...

引用

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

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

Official documents consist of text and non-textual elements such as logo, stamp, and signature. Separation of these elements from a scanned document plays a significant role in document image retrieval, recognition, and verification. This paper presents a novel scheme to separate text and non-text elements of official documents using part-based features. In this work, we exploit the fact that intensity distributions of text and non-text elements in HSV color space are of distinctive nature. A new approach to compute part-based features using S and V channels is proposed. The classification of text and non-text components is performed based on majority voting scheme and K-approximate nearest neighbors. The knowledge base acquired during training is indexed using kD-tree indexing scheme. Subsequently, the method is extended for detection of logo, stamp, and signature. Experimental results show the effectiveness of the proposed approach. © Springer International Publishing AG 2017.

关键词： Nearest neighbor search

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Brain tumor segmentation from multimodal MR images using rough sets 10

Brain tumor segmentation from multimodal MR images using rou...

引用

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

作者： Saha, Rupsa Phophalia, Ashish Mitra, Suman K. Dhirubhai Ambani Institute of Information and Communication Technology GandhinagarGujarat382007 India Indian Institute of Information Technology Vadodara GandhinagarGujarat382028 India

Automatic segmentation of brain tumors from Magnetic Resonance images is a challenging task due to the wide variation in intensity, size, location of tumors in images. Defining a precise boundary for a tumor is essential for diagnosis and treatment of patients. Rough set theory, an extension of classical set theory, deals with the vagueness of data by determining the boundary region of a set. The aim of this work is to explore the possibility and effectiveness of using a rough set model to represent the tumor regions in MR images accurately, with Quadtree partitioning and simple K-means as precursors to indicate and limit the possible relevant regions. The advantage of using rough sets lie in its ability to represent the impreciseness of set boundaries, which is one of the major challenges faced in tumor segmentation. Experiments are carried out on the BRATS 2013 and 2015 databases and results are comparable to those reported by recent works. © Springer International Publishing AG 2017.

关键词： Magnetic resonance imaging

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Distribution based EEG baseline classification 10

Distribution based EEG baseline classification

引用

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

作者： Gopika Gopan, K. Sinha, Neelam Dinesh Babu, J. International Institute of Information Technology Bangalore Bangalore India

Electrical signals generated in the brain, known as Electroen-cephalographic (EEG) signals, form a non-invasive measure of brain functioning. Baseline states of EEG are Eyes Open (EO) and Eyes Closed (EC) relaxed states. The choice of baseline used in an experiment is of critical importance since they form a reference with which other states are measured. In Brain Machine Interface, it is imperative that the system should be able to distinguish between these states and hence the need for automated classification of EEG baselines. In the proposed method, Statistical Moments are utilized. The Moment Generating Functions (MGFs) obtained using these moments are given as features to SVM and k-NN classifiers resulting in mean accuracies of 86.71% and 86.54%. The fact that MGF is able to differentiate between these states indicate that the two states have different source distribution parameters. A Smirnov test verified that the data of two classes indeed come from different distributions. © Springer International Publishing AG 2017.

关键词： Brain computer interface

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Segmentation of lumen and external elastic laminae in intravascular ultrasound images using ultrasonic backscattering physics initialized multiscale random walks 10

Segmentation of lumen and external elastic laminae in intrav...

引用

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

作者： China, Debarghya Mitra, Pabitra Sheet, Debdoot Advanced Technology Development Centre Indian Institute of Technology Kharagpur Kharagpur India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Department of Electrical Engineering Indian Institute of Technology Kharagpur Kharagpur India

Coronary artery disease accounts for a large number of deaths across the world and clinicians generally prefer using x-ray computed tomography or magnetic resonance imaging for localizing vascular pathologies. Interventional imaging modalities like intravascular ultrasound (IVUS) are used to adjunct diagnosis of atherosclerotic plaques in vessels, and help assess morphological state of the vessel and plaque, which play a significant role for treatment planning. Since speckle intensity in IVUS images are inherently stochastic in nature and challenge clinicians with accurate visibility of the vessel wall boundaries, it requires automation. In this paper we present a method for segmenting the lumen and external elastic laminae of the artery wall in IVUS images using random walks over a multiscale pyramid of Gaussian decomposed frames. The seeds for the random walker are initialized by supervised learning of ultrasonic backscattering and attenuation statistical mechanics from labelled training samples. We have experimentally evaluated the performance using 77 IVUS images acquired at 40 MHz that are available in the IVUS segmentation challenge dataset (http://***. ***/IVUSchallenge2011/***.) to obtain a Jaccard score of 0.89 ± 0.14 for lumen and 0.85 ± 0.12 for external elastic laminae segmentation over a 10-fold cross-validation study. © Springer International Publishing AG 2017.

关键词： Statistical mechanics

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Multi-font telugu text recognition using hidden Markov models and Akshara Bi-grams 10

Multi-font telugu text recognition using hidden Markov model...

引用

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

作者： Devarapalli, Koteswara Rao Negi, Atul Department of Computer Science and Engineering Mahatma Gandhi Institute of Technology Hyderabad500075 India School of Computer and Information Sciences University of Hyderabad Gachibowli Hyderabad500046 India

Recent advances in the information technology made possible to introduce many Unicode Telugu fonts for the documentation needs of present society. But the recognition of documents printed in a variety of fonts poses new challenges in building Telugu OCR systems. In this paper, we demonstrate multi-font Telugu printed word recognition using implicit segmentation approach that provides segmentation as a by-product of recognition. Our word recognition approach relies on Hidden Markov Models and akshara bi-gram language model to recognize word images in terms of aksharas (characters). The training set of word images is prepared from document images of popular books and the synthetic document images generated using 8 different Unicode fonts. The testing involves matching the feature vector sequence against sequence of akshara HMMs based on bi-grams. The CER and WER of this system are 21% and 37% respectively. The performance of our system is very encouraging. © Springer International Publishing AG 2017.

关键词： Hidden Markov models

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A hybrid deep architecture for face recognition in real-life scenario 10

A hybrid deep architecture for face recognition in real-life...

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

作者： Sanya, A. Bhattacharya, U. Parui, S.K. Department of CSE Indian Institute of Technology Kanpur India CVPR Unit Indian Statistical Institute Kolkata India

This article describes our recent study of a real-life face recognition problem using a hybrid architecture consisting of a very deep convolution neural network (CNN) and a support vector machine (SVM). The novel aspects of this study include (i) implementation of a really deep CNN architecture consisting of 11 layers to study the effect of increasing depth on recognition performance by a subsequent SVM, and (ii) verification of the recognition performance of this hybrid classifier trained by samples of a certain standard size on test face images of smaller sizes reminiscent to various real-life scenarios. Results of the present study show that the features computed at various shallow levels of a deep architecture have identical or at least comparable performances and are more robust than the deepest feature computed at the inner most sub-sampling layer. We have also studied a simple strategy of recognizing face images of very small sizes using this hybrid architecture trained by standard size face images and the recognition performance is reported. We obtained simulation results using the cropped images of the standard extended Yale Face Database which show an interesting characteristic of the proposed architecture with respect to face images captured in a very low intensity lighting condition. © Springer International Publishing AG 2017.

关键词： Face recognition

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Tracking of retinal microsurgery tools using late fusion of responses from convolutional neural network over pyramidally decomposed frames 10

Tracking of retinal microsurgery tools using late fusion of ...

引用

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

作者： Mishra, Kaustuv Sathish, Rachana Sheet, Debdoot Indian Institute of Technology Kharagpur Kharagpur India

computer vision and robotic assistance are increasingly being used to improve the quality of surgical interventions. Tool tracking becomes critical in interventions viz. endoscopy, laparoscopy and retinal microsurgery (RM) where unlike open surgery the surgeons do not have direct visual and physical access to the surgical site. RM is performed using miniaturized tools and requires careful observation through a surgical microscope by the surgeon. Tracking of surgical tools primarily provides robotic assistance during surgery and also serves as a means to assess the quality of surgery, which is extremely useful during surgical training. In this paper we propose a deep learning based visual tracking of surgical tool using late fusion of responses from convolutional neural network (CNN) which comprises of 3 steps: (i) training of CNN for localizing the tool tip on a frame (ii) coarsely estimating the tool tip region using the trained CNN and (iii) a finer search around the estimated region to accurately localize the tool tip. Scale invariant tracking of tool is ensured by incorporating multi-scale late fusion where the CNN responses are obtained at each level of the Gaussian scale decomposition pyramid. Performance of the proposed method is experimentally validated on the publicly available retinal microscopy instrument tracking (RMIT) dataset (https://***. com/site/sznitr/code-and-datasets). Our method tracks tools with a maximum accuracy of 99.13% which substantiates the efficacy of the proposed method in comparison to existing approaches. © Springer International Publishing AG 2017.

关键词： Convolution

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