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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： Chuanyun Wang Keyi Si Tian Wang Zhaokui Li Na Lin Ershen Wang Shenyang Aerospace University Shenyang China Beihang University Beijing China

ISBN: (纸本)9781728152103

Addressing the problems of flying small target detection in infrared image sequences, a new approach is proposed based on generalized low-rank background estimation. Firstly, the generalized low rank approximations is introduced to model infrared background image over sequential infrared images. Subsequently, the foreground target image is achieved by background subtraction with the generalized low-rank background estimation. Finally, flying small target detection is achieved over separated target image by threshold segmentation. The experiment results on two infrared image sequences of flying plane demonstrate that the proposed method have effective detection performance and outperform the baseline methods in precision and recall evaluation.

关键词： approximation theory estimation theory image segmentation image sequences infrared imaging object detection Infrared imaging Object detection estimation theory Approximation Theory image sequences sequential detection image segmentation Flying target image baseline method Infrared radiation

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Blind Video Quality Assessment Based on Human Visual Characteristic

Blind Video Quality Assessment Based on Human Visual Charact...

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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： Shiyu Zhou Xiqing Liu Yuanbing Tian Bin Fu Xiuyan Xia Jin Ge China Unicom Network Technology Research Institute Beijing China Zhiyou Open Source Communication Research Institute (Beijing) Co. Ltd Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781728152103

Based human visual speed perception characteristic, a video quanlity assessment(VQA) algorithm is introduced in the paper. Natural video statistics features extraction and weighting factors are incorporated in the scheme. In the VQA, considering the impact both video content itself and HVS's characteristic on human subjective perception, we propose weighting factors to scale the effect of those features, and it contains two parts: motion information and perception noise. And natural video statistics features relate to the spatial and temporal domain are extracted. The weighting factors would be used to combine both the temporal and spatial features, then generate the quality of each frame. Finally, the video quality score can be obtained by pooling scheme. LIVE database, EPFL-PoliMI database and some other generated test videos were used in our experiments, and the results indicate our model has outstanding performance.

关键词： feature extraction image denoising image motion analysis statistical analysis video signal processing visual databases image motion analysis visual databases Video signal processing image denoising Feature extraction Videotapes Quality assessment Statistical methods Statistical analysis WEIGHTING FACTORS

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Loop Closure Detection Based on Improved Hybrid Deep Learning Architecture

Loop Closure Detection Based on Improved Hybrid Deep Learnin...

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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： Yuan Liu Rong Xiang Qieshi Zhang Ziliang Ren Jun Cheng Guilin University of Electronic Technology Guilin China Key Laboratory of Human-Machine Intelligence-Synergy Shenzhen China

ISBN: (纸本)9781728152103

Loop closure detection plays a vital role in the visual Simultaneous Localization and Mapping (SLAM) system. Traditional Bag-of-Visual-Word (BoVW) methods for loop closure detection has been successfully applied to various SLAM systems. The Convolutional Neural Networks (ConvNets) based methods can automatically learn feature representation from original image, and it is more robust to illumination changes. However, these two methods all have some limitations. In order to overcome the shortcomings of the traditional methods extract insufficient features, and the ConvNets based methods unable to adapt complex environment changes, in this paper, we propose an improved hybrid deep learning architecture (HDLA) to generate high-level semantic image features for loop closure detection specifically. This network is based on hybrid ConvNet modified by us to especially cope with robust and real-time feature extraction. We present extensive experiments on three real world datasets to evaluate each of the specific challenges in loop closure detection. The results demonstrate that the proposed method achieves superior performances and provides a feasible solution for loop closure detection.

关键词： convolutional neural nets feature extraction image classification image representation learning (artificial intelligence) object detection robot vision SLAM (robots) robot vision Feature extraction image representation Object detection image classification surgical closure Loops Contraceptive Coil Feasible Solution original image simultaneous localization and mapping

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Denoising convolutional autoencoder based B-mode ultrasound tongue image feature extraction

arXiv

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arXiv 2019年

作者： Li, Bo Xu, Kele Feng, Dawei Mi, Haibo Wang, Huaimin Zhu, Jian Beijing University of Posts and Telecommunications Automation Dept. Beijing100876 China National Key Lab of Parallel and Distributed Processing Changsha China National University of Defense Technology Changsha China Department of Linguistics University of Michigan Ann Arbor United States

B-mode ultrasound tongue imaging is widely used in the speech production field. However, efficient interpretation is in a great need for the tongue image sequences. Inspired by the recent success of unsupervised deep learning approach, we explore unsupervised convolutional network architecture for the feature extraction in the ultrasound tongue image, which can be helpful for the clinical linguist and phonetics. By quantitative comparison between different unsupervised feature extraction approaches, the denoising convolutional autoencoder (DCAE)-based method outperforms the other feature extraction methods on the reconstruction task and the 2010 silent speech interface challenge. A Word Error Rate of 6.17% is obtained with DCAE, compared to the state-of-the-art value of 6.45% using Discrete cosine transform as the feature extractor. Our codes are available at https://***/DeePBluE666/Source-code1. Copyright © 2019, The Authors. All rights reserved.

关键词： Feature extraction

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Underwater image Enhancement with a Total Generalized Variation Illumination Prior

Underwater Image Enhancement with a Total Generalized Variat...

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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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