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A Fast Adaptive Subpixel Extraction Method for Light Stripe Center 10th

A Fast Adaptive Subpixel Extraction Method for Light Stripe ...

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10th International conference on image and graphics (ICIG)

作者： Zou, Wei Wei, Zhenzhong Beihang Univ Key Lab Precis Optomechatron Technol Minist Educ 37 Xueyuan Rd Beijing 100191 Peoples R China

ISBN: (纸本)9783030341206;9783030341190

Aiming at the problem of light stripe distribution uneven and large curvature variation, which results in wrong stripe center extraction, a fast light stripe center extraction method based on the adaptive template is proposed. Firstly, the adaptive threshold method is used to reduce the image convolution area, and the multi-thread parallel operation is used to improve the speed of extracting the light stripe center. Secondly, the multi-direction template method is used to estimate the width of the light stripe along the normal direction, so that the size of the Gaussian template can be automatically obtained. Finally, the Hessian matrix eigenvalues are normalized to eliminate the multiple light stripe centers at both ends of the light stripe, and avoid extracting the wrong light stripe centers at the intersection position or the large curvature change, thus ensuring the continuity of the light stripe. this method has fast processing speed, good robustness, and high precision. It is very suitable for vision measurement image, medical image, and remote sensing image.

关键词： Light stripe center image extraction Hessian matrix Normalization Gussian filter

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Temporal Modeling of EEG Signals using Block Sparse Variational Bayes Framework 18

Temporal Modeling of EEG Signals using Block Sparse Variatio...

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Proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Shruti Sharma Santanu Chaudhury Jayadeva Jayadeva Indian Institute of Technology Delhi

ISBN: (纸本)9781450366151

Compressed Sensing (CS) has emerged as an alternate method to acquire high dimensional signals effectively by exploiting the sparsity assumption. However, owing to non-sparse and non-stationary nature, it is extremely difficult to process Electroencephalograph (EEG) signals using CS paradigm. the success of Bayesian algorithms in recovering non-sparse signals has triggered the research in CS based models for neurophysiological signal processing. In this paper, we address the problem of Temporal Modeling of EEG Signals using Block Sparse Variational Bayes (SVB) Framework. Temporal correlation of EEG signals is modeled blockwise using normal variance scale mixtures parameterized via some random and deterministic parameters. Variational inference is exploited to infer the random parameters and Expectation Maximization (EM) is used to obtain the estimate of deterministic parameters. To validate the framework, we present experimental results for benchmark State Visual Evoked Potential (SSVEP) dataset with 40-target Brain-computer Interface (BCI) speller using two frequency recognition algorithms viz. Canonical Correlation Analysis (CCA) and L1-regularized Multiway CCA. Results show that the proposed temporal model is highly useful in processing SSVEP-EEG signals irrespective of the recognition algorithms used.

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image Co-segmentation using Graph Convolution Neural Network 18

Image Co-segmentation using Graph Convolution Neural Network

引用

Proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Sayan Banerjee Avik Hati Subhasis Chaudhuri Rajbabu Velmurugan Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9781450366151

image co-segmentation is jointly segmenting two or more images sharing common foreground objects. In this paper, we propose a novel graph convolution neural network (graph CNN) based end-to-end model for performing co-segmentation. At the beginning, each input image is over-segmented into a set of superpixels. Next, a weighted graph is formed using the over-segmented images exploiting spatial adjacency and both intra-image and inter-image feature similarities among the image superpixels (nodes). Subsequently, the proposed network, consisting of graph convolution layers followed by node classification layers, classifies each superpixel either into the common foreground or its complement. During training, along with the co-segmentation network, an additional network is introduced to exploit the corresponding semantic labels, and the two networks share the same weights in graph convolution layers. the whole model is learned in an end-to-end fashion using a novel cost function comprised of a superpixel wise binary cross entropy and a multi-label cross entropy. We also use empirical class probabilities in the loss function to deal with class imbalance. Experimental results reflect that the proposed technique is very competitive with the state-of-the-art methods on two challenging datasets, Internet and Pascal-VOC.

关键词： Co-segmentation

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Distance Estimation for Endoscopy Based on Depth Detection in the Small Intestine Using image processing techniques

Distance Estimation for Endoscopy Based on Depth Detection i...

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International conference of Signal processing and Intelligent Systems (ICSPIS)

作者： Majid Ramedani Vahid J. Majd Tarbiat Modares University Tehran Iran

ISBN: (数字)9781728186290

ISBN: (纸本)9781728186306

In recent years, three-dimensional measurement techniques have been widely used in medical sciences, and thus, depth detection in an image plays an important role in computer vision applications. In this paper, we discuss the estimation of the distance between the head of an endoscope and the small intestine septum and its problems. the main objective is to detect the depth of the small intestine to estimate distance. images were collected through video sampling, and then the data are preprocessed. Morphological reconstruction, bounding box, Convex Hull, and Euclidean distance are employed to update the mentioned distance. At the end of this process, the outputs are simulated, and we are given the output distance in centimeters. this method will assist the endoscope to move inside the small intestine without injuries.

关键词： Intestines Endoscopes Estimation Signal processing algorithms Measurement techniques Signal processing Intelligent systems

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Deep Learning Models for Medical image Analysis: Challenges and Future Directions 1

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7th International conference on Big Data analytics (BDA)

作者： Agrawal, R. K. Juneja, Akanksha Jawaharlal Nehru Univ New Delhi 110067 India

ISBN: (数字)9783030371883

ISBN: (纸本)9783030371883;9783030371876

Artificial neural network (ANN) introduced in the 1950s, is a machine learning framework inspired by the functioning of human neurons. However, for a long time the ANN remained inadequate in solving real problems, because of - the problems of overfitting and vanishing gradient while training a deep architecture, dearth of computation power, and non-availability of enough data for training the framework. this concept has lately re-emerged, in the form of Deep Learning (DL) which initially developed for computer vision and became immensely popular in several other domains. It gained traction in late 2012, when a DL approach i.e. convolutional neural network won in the imageNet Classification - an acclaimed worldwide computer vision competition. thereafter, researchers in practically every domain, including medical imaging, started vigorously contributing in the massively progressing field of DL. the success of DL methods can be owed to the availability of data, boosted computation power provided by the existing graphics processing units (GPUs), and ground-breaking training algorithms. In this paper, we have overviewed the area of DL in medical imaging, including (1) machine learning and DL basics, (2) cause of power of DL, (3) common DL models, (4) their applications to medical imaging and (5) challenges and future work in this field.

关键词： Medical image analysis Machine learning Deep Learning computer-aided diagnosis

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Research on Design and Verification of Sobel image Edge Detection Based on High Level Synthesis

Research on Design and Verification of Sobel Image Edge Dete...

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International conference on Dependable Systems and their Applications (DSA)

作者： Pan Chen Ying Wang Junhua Xing Beijing Jinghang Research Institute of Computing and Communication Beijing China

ISBN: (数字)9781728160573

ISBN: (纸本)9781728160580

image edge detection technology is very important in the field of digital image processing, which is widely used in many fields,such as computer graphics processing, target matching, machine recognition, traffic control, national defense security and so on [1]. Because of the complicated calculation steps of the image edge detection algorithm and the high processing speed requirements, it is difficult to meet the requirements only by software method, which is a problem of image edge detection. the design of FPGA can effectively solve the above disadvantages due to its parallelism and real-time performance, but the traditional FPGA development needs to master the hardware description language such as Verilog or VHDL design, which has long development cycles and high costs. In order to solve the above disadvantages effectively, this paper presents a method that using C and C++ language to develop Sobel algorithm by Vivado HLS(short for High Level Synthesis). the method can be implemented in FPGA design after High Level Synthesis, and provide a new design idea for software developers who are not familiar with the HDL language. Verified by theoretical analysis and simulation, using this algorithm can achieve a good image edge detection effect.

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Estimation of Ball Possession Statistics in Soccer Video 18

Estimation of Ball Possession Statistics in Soccer Video

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Proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Saikat Sarkar Amlan Chakrabarti Dipti Prasad Mukherjee Bangabasi College University of Calcutta Kolkata India A. K. Choudhury School of Information Technology University of Calcutta Kolkata India Indian Statistical Institute Kolkata India

ISBN: (纸本)9781450366151

In this work, we have estimated ball possession statistics from the video of a soccer match. the ball possession statistics is calculated based on the valid pass counts of two playing teams. We propose a player-ball interaction energy function to detect ball pass event. Based on position and velocity of the ball and players, a model for interaction energy is defined. the energy increases when the ball is closer and about to collide with a player. Lower energy denotes that the ball is freely moving and not near to any player. the interaction energy generates a binary state sequence which determines a valid pass or a miss-pass. We assess the performance of our model on publicly available soccer videos and have achieved close to 83% accuracy.

关键词： Ball possession statistics

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A Neural Network Based Approach for Geo-Localizing Events in Crowd Sourced Videos 2018

A Neural Network Based Approach for Geo-Localizing Events in...

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Proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Amit More Subhasis Chaudhuri Indian Institute of Technology Bombay Mumbai Maharastra

ISBN: (纸本)9781450366151

Popular events are often video recorded simultaneously by a general crowd using smartphones. In the present work, we propose a robust recurrent neural network (RNN) based approach for geo-localizing these events using sensor data collected by user smartphones while recording such events. For this task we use GPS and compass sensors, which are commonly available on the smartphones. the circular nature (modulo 2π) of the orientation data from compass limits the ability of the classical neural networks (NN) to geo-localize these events. We mitigate this issue by incorporating circular nodes in our network and show the performance improvements. We train the proposed NN model using simulated data and apply it directly on real data. We train several RNN models using this strategy and show our analyses. the proposed work outperforms all previous approaches in terms of event geo-localization accuracy.

关键词： Event Geo-localization Neural Network Compass Data Smartphone Circular Nodes GPS data

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A Rapid Non-Linear Diffusion Compressed Sensing parallel MR image Reconstruction 18

A Rapid Non-Linear Diffusion Compressed Sensing parallel MR ...

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Proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Ajin Joy Joseph Suresh Paul Indian Institute of Information Technology and Management-Kerala Trivandrum India

ISBN: (纸本)9781450366151

Optimization of the tradeoff between computation time and image quality is essential for reconstructing high-quality magnetic resonance image (MRI) from a limited number of acquired samples in a short time using compressed sensing (CS) algorithms. In this paper, we achieve this for the edge preserving non-linear diffusion reconstruction (NLDR) which eliminates the critical step-size tuning of the total variation (TV) based CS-MRI. Based on optimization of contrast parameter that controls noise and signal in sensitivity modulated channel images, we propose an â-switching NLDR technique for a faster approximation of reconstruction image without affecting the image quality. Proposed algorithm exploits the difference in the extent of undersampling artifacts in signal-background regions of the channel images to arrive at different estimates of contrast parameter, leading to an effective optimization of speed and quality. While maintaining better image quality as compared to conventional TV reconstruction, the switched NLDR also achieves 25-35% gain in convergence time over NLDR without switching. this makes the switched NLDR a better candidate for fast reconstruction over traditional TV and NLDR approaches. In the detailed numerical experiments, we have compared and optimized the tradeoff for various state-of-the-art choices of contrast parameter.

关键词： contrast parameter

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An Efficient Multi Lingual Optical Character Recognition System for indian Languages through Use of Bharati Script 4th

An Efficient Multi Lingual Optical Character Recognition Sys...

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

作者： Vorugunti, Chandra Sekhar Chakravarthy, Srinivasa Pulabaigari, Viswanath Indian Institute of Information Technology-SriCity Chittoor India Indian Institute of Technology-Madras Chennai India

ISBN: (纸本)9789811393600

Optical character recognition performs a critical part in interpreting videos and documents. Document specific issues like low image quality, distortions, composite background, noise etc. and language specific issues like cursive connectivity among the characters etc. makes OCR challenging and erroneous for indian languages. the language specific challenges can be overcome by computing the script-based features and can achieve better accuracy. Computing the script based invariant features and patterns is computationally complex and error prone. In this background, we put forward Bharathi script (*** ) based OCR system in which the inherent drawbacks of indian scripts i.e. Hindi, Tamil, Telugu etc. are eliminated. the proposed OCR model has been tested on a synthetic dataset of documents of Bharathi script (in which Hindi scripts are converted to Bharathi script). thorough experimental analysis with varied levels of noise confirms the promising results of character recognition accuracy of the proposed OCR model which out-performs the state-of-the-art OCR systems for indian scripts. the proposed model achieves 76.70% with test documents consists of 50% noise and 99.98% with test documents of 0% noise. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Optical character recognition

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