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Underwater image Enhancement with a Total Generalized Variation Illumination Prior

Underwater Image Enhancement with a Total Generalized Variat...

引用

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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Loop Closure Detection Based on Improved Hybrid Deep Learning Architecture

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

引用

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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Data Analysis of Blended Learning in Python Programming 18th

Data Analysis of Blended Learning in Python Programming

引用

18th International conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Chu, Qian Yu, Xiaomei Jiang, Yuli Wang, Hong Shandong Normal Univ Inst Informat & Engineer Jinan Peoples R China Shandong Prov Key Lab Distributed Comp Software N Jinan 250014 Shandong Peoples R China

ISBN: (纸本)9783030050573;9783030050566

The rapid emergence of blended learning has sparked a great deal of research interest in the field of educational data mining. We apply the novel educational form of blended learning in the undergraduate curriculum of python programming. With the questionnaire before curriculum is obtained to capture the basic information of undergraduate students, we design educational resources and activities for online studying and face-to-face teaching. Since the learning process of each student is captured continuously, we make teaching and learning evaluations weekly to improve current teaching methods hence arouse students' interest of continuous learning. With analyzing data and mining knowledge received in the process of blended learning, some beneficial results are gained to promote the quality of blended learning in the undergraduate curriculum of python programming, and benefit the undergraduate students as well as higher education in the long run.

关键词： Blended learning Education Python Data analysis

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The Challenge of a Strong Speed-Up of a Bio-Medical Big Data Application

The Challenge of a Strong Speed-Up of a Bio-Medical Big Data...

引用

IEEE International conference on Big Data (Big Data)

作者： Strutz, Marco Lindequist, Bjoern Hessling, Hermann Streit, Achim Univ Appl Sci HTW Berlin Wilhelminenhofstr 75A D-12459 Berlin Germany KIT SCC Hermann von Helmholtz Pl 1 D-76344 Eggenstein Leopoldshafen Germany

ISBN: (纸本)9781538650356

Digital data of patients can aid a pathologist along the diagnostic process In Medical devices generate data sets that are processed by specialized computing applications, which often run on a single computer. The resolution power of the devices is increasing steadily and, consequently, the volumes of the data sets are also growing and can no longer be analyzed in a reasonable amount of time. Big Data tools like Apache Spark [2] provide methods for analyzing data, however, they are not directly applicable and need considerable implementation efforts, in general. Usually, well established analysis tools for medical data are designed to run on single workstations. These tools are not designed to meet current and future challenges. Migrating processing tools from single nodes to distributed environments is nontrivial. Moreover, partitioning data sets for a parallel processing is a further challenge [3]. In this work, we continue our efforts for improving the speedup of a bio-medical big data application further by partitioning the images of a Whole Slide image (WSI) [4] into sub tiles and by analyzing these sub tiles on a cluster of computer nodes. The idea is to benefit from the divide and conquer strategy. However, it is shown that the score parameter is determined incorrectly, when the software package is applied to each sub tile and the score parameters of all sub tiles are combined in an apparently natural manner. The cause of this anomaly is determined and a solution suggested. The original software is based on implicit assumptions. For example, the size of the tiles is assumed to be 1024 x 1024 px(2). The anomaly shows up when this constraint is reduced.

关键词： Runtime Tumors Tools Big Data Medical diagnostic imaging parallel processing

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Underwater image Enhancement Based on Dehazing and Color Correction

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

引用

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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DSANLS: Accelerating distributed Nonnegative Matrix Factorization via Sketching 18

DSANLS: Accelerating Distributed Nonnegative Matrix Factoriz...

引用

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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image Matching Algorithm Based on GMS

Image Matching Algorithm Based on GMS

引用

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...

引用

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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Enhanced Knowledge Distillation for Face Recognition

Enhanced Knowledge Distillation for Face Recognition

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

作者： Hao Ni Jie Shen Chong Yuan Yingcai Honors College University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Face recognition have been widely used in different industries due to the advancement of deep convolutional neural networks. Although deep learning has greatly promoted the development of face recognition technology, its computing-intensive and memory-intensive features make it difficult to deploy the model on some embedded devices or mobile computing platforms. Many solutions which include Knowledge Distillation have been proposed to increase the calculation speed of model and reduce the storage space required for calculations, in this paper, we propose a novel Two Stage Knowledge Distillation which enhances the performance of knowledge distillation in face recognition and low resolution face recognition. After experimenting on the several major face datasets, our method turns out to have better results compared to the traditional optimization methods.

关键词： Face recognition Knowledge engineering Face Adaptation models image resolution Neural networks Computational modeling

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image Translation Between High-Resolution Remote Sensing Optical and SAR Data Using Conditional GAN 19th

Image Translation Between High-Resolution Remote Sensing Opt...

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19th Pacific-Rim conference on Multimedia (PCM)

作者： Niu, Xin Yang, Di Yang, Ke Pan, Hengyue Dou, Yong Natl Univ Def Technol Coll Comp Natl Lab Parallel & Distributed Proc Changsha Hunan Peoples R China

ISBN: (纸本)9783030007645;9783030007638

This paper presents a study on a new problem: applying machine learning approaches to translate remote sensing images between high-resolution optical and Synthetic Aperture Radar (SAR) data. To this end, conditional Generative Adversarial Networks (GAN) have been explored. Efficiency of the conditional GAN have been verified with different SAR parameters on three regions from the world: Toronto, Vancouver in Canada and Shanghai in China. The generated SAR images have been evaluated by pixel-based image classification with detailed land cover types including: low and high density residential area, industry area, construction site, golf course, water, forest, pasture and crops. In comparison with an unsupervised GAN translation approach, the proposed conditional GAN could effectively keep many land cover types with compatible classification accuracy to the ground truth SAR data. This is one of first study on multi-source remote sensing data translation by machine learning.

关键词： Remote sensing Generative Adversarial Network Deep learning

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