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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Zhengjie Zhao Yuxiang Dai Peixian Zhuang School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

In order to promote the visual quality of degraded underwater images, we develop an innovative variational model based on Retinex with a total generalized variation (TGV) prior on the illumination. The TGV prior is adopted to approximate piecewise smoothness and piecewise linear smoothness of the illumination, which combines the first-order and second-order total variation (TV) to model the variation of illumination. When adopting this illumination prior in the Retinex-based algorithm, the illumination and reflection are well separated, and underwater enhanced results appear more natural and their details and edges are better preserved. Then an efficient iterative optimization method is derived to settle the proposed model via alternately calculating the illumination and the reflection simultaneously. Numerous experiments on both visual results and objective metrics demonstrate the superiority of our method compared with several underwater enhancement methods. In addition, the proposed method can be extended for dehazing, sandstorm removal and low illumination image enhancement, which can illustrate better capacity of our model.

关键词： Lighting image color analysis Mathematical model Optimization image enhancement Visualization Adaptation models

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Underwater image Enhancement Based on Dehazing and Color Correction

Underwater Image Enhancement Based on Dehazing and Color Cor...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Hanyu Li Peixian Zhuang Wang Wei Jingjing Li Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Clear and clean underwater images can provide valuable information, which are crucial for developing, exploring, and protecting the underwater resources. However, the raw underwater image seldom fulfills the requirements concerning the underwater research due to the serious image degradation. To enhance single underwater image, we propose an effective and new method combined on the dehazing and color correction algorithm. First, we obtain the dehazed image by a fusion dehazing method calculated on the difference between maximum green-blue dark channels and maximum red dark channel, and the top 0:1% bright pixels in green-blue dark channels. Second, we obtain the enhanced image via the color restoration method corresponding to the human visual system. Finally, we use an efficient and simple weight fusion strategy to incorporate the dehazed image and the enhanced image for yielding the final high quality underwater image. Subjective assessment demonstrates that our method can remove haze, correct color cast, improve image brightness, and preserve image naturalness. The enhanced results obtain the highest values in terms of entropy, average gradient, UCIQE, UIQM, and CCF, which is superior to several existing underwater enhancement methods. Moreover, our method can improve other degraded image quality, such as low-light image and haze image, and can benefit the application tests, such as key points matching and edge detection.

关键词： image color analysis Degradation image restoration image edge detection image quality Channel estimation image enhancement

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Group Importance Sampling for particle filtering and MCMC

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DIGITAL SIGNAL processing 2018年 82卷 133-151页

作者： Martino, Luca Elvira, Victor Camps-Valls, Gustau Univ Valencia Image Proc Lab Valencia Spain Univ Carlos III Madrid Dept Stat Signal Proc Madrid Spain IMT Lille Douai CRISTAL UMR 9189 Villeneuve Dascq France

Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques have become very popular in signal processing over the last years. Importance Sampling (IS) is a wellknown Monte Carlo technique that approximates integrals involving a posterior distribution by means of weighted samples. In this work, we study the assignation of a single weighted sample which compresses the information contained in a population of weighted samples. Part of the theory that we present as Group Importance Sampling (GIS) has been employed implicitly in different works in the literature. The provided analysis yields several theoretical and practical consequences. For instance, we discuss the application of GIS into the Sequential Importance Resampling framework and show that Independent Multiple Try Metropolis schemes can be interpreted as a standard Metropolis-Hastings algorithm, following the GIS approach. We also introduce two novel Markov Chain Monte Carlo (MCMC) techniques based on GIS. The first one, named Group Metropolis Sampling method, produces a Markov chain of sets of weighted samples. All these sets are then employed for obtaining a unique global estimator. The second one is the distributed Particle Metropolis-Hastings technique, where different parallel particle filters are jointly used to drive an MCMC algorithm. Different resampled trajectories are compared and then tested with a proper acceptance probability. The novel schemes are tested in different numerical experiments such as learning the hyperparameters of Gaussian Processes, two localization problems in a wireless sensor network (with synthetic and real data) and the tracking of vegetation parameters given satellite observations, where they are compared with several benchmark Monte Carlo techniques. Three illustrative Matlab demos are also provided. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Importance Sampling Markov Chain Monte Carlo (MCMC) Particle filtering Particle Metropolis-Hastings Multiple Try Metropolis Bayesian inference

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distributed eigenfaces for massive face image data

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第24期76卷 25983-26000页

作者： Park, Jeong-Keun Park, Ho-Hyun Park, Jaehwa Chung Ang Univ Dept Comp Sci & Engn Seoul South Korea Chung Ang Univ Dept Elect & Elect Engn Seoul South Korea

The assumption that the number of training samples is less than the number of pixels in a face image is essential for conventional eigenface-based face recognition. But recently, it has become impractical for massive face image collections. A parallel processing method using distributed eigenfaces is presented. A massive face image set was divided into a bunch of small subsets that satisfied the assumption of conventional approaches. Eigenfaces were extracted from the subsets and stored in a cloud system. Face recognition was performed by parallel processing using the distributed eigenfaces in the cloud system. A face recognition system was implemented in the Hadoop system. Various experiments were performed to test the validity of the distributed eigenface-based approach. The experimental results show that, compared to conventional methods, the implemented distributed face recognition system worked well for large datasets without significant performance degradation.

关键词： Eigenface Face recognition parallel processing Hadoop

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image Matching Algorithm Based on GMS

Image Matching Algorithm Based on GMS

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Hua Li Wen Liu Cheng Han Xiangxu Kong School of Computer Science and Technology Changchun University of Science and Technology Changchun China Changchun University of Science and Technology Changchun China

ISBN: (纸本)9781728152103

image matching is a technique for measuring the similarity of two or more images to determine the correspondence between images. It is the main research content in the field of computer vision. In order to solve the problem of low matching accuracy of Grid-based Motion Statistics(GMS) algorithm in complex situations such as illumination changes, rotation changes or scale changes, an improved GMS image matching algorithm is proposed. Firstly, we use AKAZE to detect and describe the feature points. Then, the bi-directional KNN matching algorithm is adopted for rough matching. Finally, the GMS algorithm is used to filter and eliminate the false matching point pairs. In order to verify the effectiveness of the proposed method, the Oxford standard datasets and practical images are experimented. The experimental results show that the correct matching rate of the proposed algorithm can reach to 90%. Our method can match better in complex scenes than ORB, and GMS algorithms. In terms of matching time, the proposed method reduces about 15% than original GMS.

关键词： computer vision feature extraction image matching image motion analysis nearest neighbour methods image matching images computer vision image motion analysis Feature extraction nearest neighbor algorithm algorithms Object Request Broker PPP1R3A gene

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Meta-Learning Techniques to Analyze the Raman Data for Optical Diagnosis of Oral Cancer Detection

Meta-Learning Techniques to Analyze the Raman Data for Optic...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Mukta Sharma Lokesh Sharma Ming-Jer Jeng Liann-Be Chang Shiang-Fu Huang Shih-Lin Wu Department of Electronic Engineering Chang Gung University Taoyuan Taiwan AI Innovation Research Center Chang Gung University Taoyuan Department of Otolaryngology-Head and Neck Surgery Chang Gung Memorial Hospital Linkou Taiwan Department of Public Health Chang Gung University Taoyuan Taiwan Department of Cardiology Chang Gung Memorial Hospital Taoyuan Department of Electrical Engineering Ming Chi University of technology New Taipei City Taiwan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan

ISBN: (纸本)9781728152103

Recently, Machine Learning methods have shown great improvement while analyzing the biomedical data. Raman Spectroscopy (RS), a non-invasive technique, and widely used in screening to diagnose the oral cancer. In order to spot cancer in a smarter and faster way, we have employed Meta-Learning (ML) techniques to learn such as Bagging and Boosting on RS data. Further, we employed normal and tumor tissue class classification by Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Adaptive Boosting (AdaBoost) classifiers. The present study aims at examining the RS data with total 110 samples, including 57 tumor and 53 normal ones. To evaluate the performance, we have used the training samples to optimize, and testing samples to generalize the model parameters. The results show that the AdaBoost classifier with Bagging techniques showed the significant changes in accuracy.

关键词： biomedical optical imaging cancer image classification learning (artificial intelligence) medical image processing tumours Biomedical optical imaging Medical image processing image classification Tumours Mouth Neoplasms Malignant Oral Cavity Tumor cancer test OPTICAL DIAGNOSTICS Boost bagging PURSE

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DSANLS: Accelerating distributed Nonnegative Matrix Factorization via Sketching 18

DSANLS: Accelerating Distributed Nonnegative Matrix Factoriz...

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11th ACM International Conference on Web Search and Data Mining

作者： Qian, Yuqiu Tan, Conghui Mamoulis, Nikos Cheung, David W. Univ Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Univ Ioannina Ioannina Greece

ISBN: (纸本)9781450355810

Nonnegative matrix factorization (NMF) has been successfully applied in different fields, such as text mining, image processing, and video analysis. NMF is the problem of determining two non-negative low rank matrices U and V, for a given input matrix M, such that M approximate to UVT. There is an increasing interest in parallel and distributed NMF algorithms, due to the high cost of centralized NMF on large matrices. In this paper, we propose a distributed sketched alternating nonnegative least squares (DSANLS) framework for NMF, which utilizes a matrix sketching technique to reduce the size of nonnegative least squares subproblems in each iteration for U and V. We design and analyze two different random matrix generation techniques and two subproblem solvers. Our theoretical analysis shows that DSANLS converges to the stationary point of the original NMF problem and it greatly reduces the computational cost in each subproblem as well as the communication cost within the cluster. DSANLS is implemented using MPI for communication, and tested on both dense and sparse real datasets. The results demonstrate the efficiency and scalability of our framework, compared to the state-of-art distributed NMF MPI implementation.

关键词： Iterative methods

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DnM3Net: Multi-Scale & Multi-Level Shuffle-CNN Via Multi-Level Attention for image Denoising

DnM3Net: Multi-Scale & Multi-Level Shuffle-CNN Via Multi-Lev...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Yue Cao Jinhe He Yu Zhang Gang Lu Shigang Liu Xiaojun Wu School of Computer Science Shaanxi Normal University Xian Shaanxi Normal University Xi'an China

ISBN: (纸本)9781728152103

Recently, based on novel convolutional neural net-work architectures proposed, tremendous advances have been achieved in image denoising task. An effective and efficient multi-level network architecture for image denoising refers to restore the latent clean image from a coarser scale to finer scales and pass features through multiple levels of the model. Unfortunately, the bottleneck of applying multi-level network architecture lies in the multi-scale information from input images is not effectively captured and the fine-to-coarse feature fusion strategy to be ignored in image denoising task. To solve these problems, we propose a multi-scale & multi-level shuffle-CNN Via multi-level attention (DnM3Net), which plugs the multi-scale feature extraction, fine-to-coarse feature fusion strategy and multi-level attention module into the new network architecture in image denoising task. The advantage of this approach are two-fold: (1) It solve the multi-scale information extraction issue of multi-level network architecture, making it more effective and efficient for the image denoising task. (2) It is impressive performance because the better trade-off between denoising and detail preservation. The proposed novel network architecture is validated by applying on synthetic gaussian noise gray and RGB images. Experimental results show that the DnM3Net effectively improve the quantitative metrics and visual quality compared to the state-of-the-art denoising methods.

关键词： convolutional neural nets feature extraction Gaussian noise image colour analysis image denoising image sampling image denoising image samples image colour analysis NETWORK STRUCTURE Gaussian noise Feature extraction multi-scale hierarchical models Noise reduction

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Siamese Network for Visual Tracking via Focal Loss

Siamese Network for Visual Tracking via Focal Loss

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Shan Jiang Cheng Han Xiaoqiang Di Hua Li School of Computer Science and Technology ChangChun Unviersity of Science and Technology Changchun China

ISBN: (纸本)9781728152103

Visual tracking is a fundamental problem in computer vision. Lots efforts have been made in the past decades and researchers have obtained some achievements, but there are still problems exist. To get a more powerful feature representation, deep learning based methods appear layer upon layer, and most of them achieve good performance at the price of losing speed. In this paper, we propose a SiamFL network for visual tracking, which introduce the focal loss with the Siamese network. With the focal loss, the network is capable of filtering out the easy examples, leaving the hard samples to train a discriminative network. Moreover, the SiamFL network also can alleviate the class imbalance problem by properly sampling the positive and negative samples at a certain ratio. Experimental results demonstrate the SimaFL has quick convergence and also have a better performance over the baseline tracker.

关键词： computer vision image representation learning (artificial intelligence) visual tracking image representation images computer vision siamese Balance impairment Network

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Multiple License Plate Location Algorithm in Complex Scene

Multiple License Plate Location Algorithm in Complex Scene

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： YuMei Zhou MeiNi Lv ZiQing Ling DongLing Li School of Electrical and Eletronic Engineering TianHe College of GuangDong Polytechnic Normal University GuangZhou China School of Eletronic and Information Engineering WuZhou University WuZhou China

ISBN: (纸本)9781728152103

Aiming at the problem that the existing license plate location methods can't work well in locating the license plate in certain natural scenes such as low luminance, low resolution and inclination scene of vehicle, a license plate location method based on cascade classifier and convolution neural network is proposed. Firstly, the cascade classifier is used for license plate coarse location, and the convolution neural network is used for license plate precise location. Experiments show that, compared with cascade classifier, the method in this paper has higher accuracy and recall rate for target license plate location, and the method in this paper has strong adaptability to different environments.

关键词： character recognition convolutional neural nets edge detection feature extraction image colour analysis object detection traffic engineering computing License plates image colour analysis Character recognition Object detection Edge detection Feature extraction engineering computing cascades Retrieval recall

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