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IEEE International Workshop on Imaging systems and Techniques (IST)

作者： Conghui Wang Bochuan Hou Jing Shi Jianhua Yang Boning Wu Zixing Fu Kun Fang School of Automation Northwestern Polytechnical University Xi'an China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Ningbo Fotile Kitchen Ware Co. Ltd. Ningbo China

ISBN: (数字)9781728138688

ISBN: (纸本)9781728138695

The temperature field distribution of the oven directly affects the quality of the baked food. The traditional oven performance research mainly focuses on the heating method in the simulated oven, lacking quantitative analysis of the baked food. In this paper, the digital image processing technology is used to segment and extract different baking status of the baked food in order to qualitatively and quantitatively expresses the internal temperature field distribution of the oven. Firstly, the image of baked food is captured by high-definition digital camera. And then it will be preprocessed to obtain a denoised image with only baked food area. Thirdly, the simple linear iterative clustering (SLIC) segmentation is used to extract the different baking status. The experimental results show that the simple linear iterative clustering (SLIC) segmentation algorithm can digitally express the baking status in the form of superpixels. The proposed method can qualitatively and quantitatively reflect the distribution of the temperature field inside the oven corresponding to the baked food image, which provide a basis for further evaluation of the heat distribution field inside the oven.

关键词： image color analysis image segmentation Ovens Temperature distribution Temperature measurement Clustering algorithms Digital images

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ROI-based Robotic Grasp Detection for Object Overlapping Scenes

ROI-based Robotic Grasp Detection for Object Overlapping Sce...

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2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Hanbo Zhang Xuguang Lan Site Bai xinwen Zhou Zhiqiang Tian Nanning Zheng National Engineering Laboratory for Visual Information Processing and Applications School of Electronic and Information Engineering Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University No.28 Xianning Road Xi’an Shaanxi China

Grasp detection considering the affiliations between grasps and their owner in object overlapping scenes is a necessary and challenging task for the practical use of the robotic grasping approach. In this paper, a robotic grasp detection algorithm named ROI-GD is proposed to provide a feasible solution to this problem based on Region of Interest (ROI), which is the region proposal for objects. ROI-GD uses features from ROIs to detect grasps instead of the whole scene. It has two stages: the first stage is to provide ROIs in the input image and the second-stage is the grasp detector based on ROI features. We also contribute a multi-object grasp dataset, (a) which is much larger than Cornell Grasp Dataset, by labeling Visual Manipulation Relationship Dataset. Experimental results demonstrate that ROI-GD performs much better in object overlapping scenes and at the meantime, remains comparable with state-of-the-art grasp detection algorithms on Cornell Grasp Dataset and Jacquard Dataset. Robotic experiments demonstrate that ROI-GD can help robots grasp the target in single-object and multi-object scenes with the overall success rates of 92.5% and 83.8% respectively.

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A Prototype System for Three-Dimensional Cardiac Modeling and Printing with Clinical Computed Tomography Angiography 4th

A Prototype System for Three-Dimensional Cardiac Modeling an...

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4th Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2017

作者： Tang, Shao-jie Zhang, Hong Peng, Wei Shi, Hao-yang Guo, Cong Zhao, Guang-yuan Bian, Wei-guo Chen, Yang School of Automation Xi’an University of Posts and Telecommunications Xi’an China Orthopaedics Department The First Affiliated Hospital of Xi’an Jiaotong University Xi’an China Laboratory of Image Science and Technology Southeast University Nanjing China Ministry of Education Nanjing China

ISBN: (纸本)9783319685267

To help physicians and their assistants plan, rehearse and follow up a surgical therapy with a computer device, people may carry out a three-dimensional (3-D) cardiac modeling by digital image processing algorithms and a 3-D cardiac printing by appropriate materials, according to the DICOM data of patient’s heart acquired by an advanced computed tomography angiography (CTA) device in a hospital. In this work, we introduce the design and implementation of a prototype system of 3-D cardiac modeling and printing. Firstly, we converted the CTA data of the heart from the original DICOM format to *** format within the Matlab programming environment. Secondly, we carried out the 3-D image segmentation with the region growing to semi-automatically determine the region of interest (ROI), i.e., the cardiac tissue, by sequentially processing the CT image slices. Thirdly, we performed the 3-D modeling and volume rendering for the binary volume image of the cardiac tissue. Lastly, we converted the 3-D modeling into STL format and sent it to the table-top 3-D printer mounted in our lab, generated the corresponding G codes by Flashprint software to manipulate the 3-D printer. It is noted that the software part of the prototype system was developed within Matlab and performed well for the expected task. © 2018, Springer International Publishing AG.

关键词： Heart

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Dynamic Adaptive Network Edge Service Migration Method Based on a Docker Container

Dynamic Adaptive Network Edge Service Migration Method Based...

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IEEE International Conference on Computer and Communications

作者： xiangjun Zhang Weiguo Wu Chi Zhang Wei Song Schoo of Couer Science nd Technoogy Xi'n Jioong UniversiySchoo of Couer Science nd Technoogy Xi'n Jioong UniversiySchoo of Couer Science nd Technoogy Xi'n Jioong UniversiySchoo of Couer Science nd Technoogy Xi'n Jioong Universiy

ISBN: (数字)9781728147437

ISBN: (纸本)9781728147444

With the development and maturity of 5G technology, edge networks have become more suitable for moving many low-latency cloud services to the edges of networks, thus significantly improving user service quality. Since mobile terminal users move with their location, the edge computing platform needs to seamlessly migrate services using users' mobile devices. However, the network environment of an edge network is complex, and service migration faces many challenges, such as how to overcome network delays and reduce the service interruption time. In this paper, we propose a service migration method based on Docker base image and design a compression algorithm for an adaptive network environment to compress images and other data. This method balances the multi-core processor computing power and network bandwidth resources. We implement pipeline parallel processing of each process and build a model to handle the situation. The results show that our method is more adaptable to changing network environments, and the migration time is reduced by 45.4%, on average, compared with the traditional uncompressed service switching method.

关键词： Containers Servers Compression algorithms Cloud computing Bandwidth Adaptive systems File systems

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An Optimization Approach of Compressive Sensing Recovery Using Split Quadratic Bregman Iteration with l₀ Smoothed Norm 3

An Optimization Approach of Compressive Sensing Recovery Usi...

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3rd IEEE International Conference on image processing, Applications and systems (IPAS)

作者： Yang, Guoan You, Yuning Lu, Zhengzhi Yang, Junjie Wang, Yuhao Xi An Jiao Tong Univ Sch Elect & Informat Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9781728102474

An optimization algorithm for image recovery is a core issue in the field of compressive sensing (CS). This paper deeply studied the CS reconstruction algorithm based on split Bregman iteration with l(1) norm, which enables the l(1) norm to approximate the original l(0) norm during the optimization process. Consequently, we proposed another novel algorithm improving the precision and the convergence speed based on split quadratic Bregman iteration (SQBI) with l(0) norm. Besides, we analyzed its convergence by proving two monotonically decreasing theorems. Inspired by previous researches, we applied smoothed l(0) norm for the optimization problem to replace the traditional to norm in CS. The improvement is made by using a Gaussian function to approximate the l(0) norm, transforming it into a convex optimization problem, and eventually achieved a convergent solution by the steepest descent method. The experimental results show that under the same conditions, compared with other state-of-the-art algorithms, the reconstruction accuracy of the CS reconstruction algorithm based on the SQBI with smoothed l(0) norm is improved significantly, and its convergence rate is also accelerated as well.

关键词： Compressive sensing sparse representation image recovery split qradratic Bregman iteration convex optimization

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Effects of annotation granularity in deep learning models for histopathological images

Effects of annotation granularity in deep learning models fo...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Jiangbo Shi Zeyu Gao Haichuan Zhang Pargorn Puttapirat Chunbao Wang xiangrong Zhang Chen Li National Engineering Lab for Big Data Analytics Xi'an Jiao tong University Department of Pathology the First Affiliated Hospital of Xi'an Jiaotong University Institute of Intelligent Information Processing Xidian University

ISBN: (纸本)9781728118680

Pathological is crucial to cancer diagnosis. Usually, Pathologists draw their conclusion based on observed cell and tissue structure on histology slides. Rapid development in machine learning, especially deep learning have established robust and accurate classifiers. They are being used to analyze histopathological slides and assist pathologists in diagnosis. Most machine learning systems rely heavily on annotated data sets to gain experiences and knowledge to correctly and accurately perform various tasks such as classification and segmentation. Generally, annotations made in pathology-related datasets have inherited annotation methods from natural scene images. This work investigates different granularity of annotations in histopathological data set including image-wise, bounding box, ellipse-wise, and pixel-wise to verify the influence of annotation in pathological slide on deep learning models. We design corresponding experiments to test classification and segmentation performance of deep learning models based on annotations with different annotation granularity. In classification, state-of-the-art deep learning-based classifiers perform better when trained by pixel-wise annotation dataset. On average, precision, recall and F1-score improves by 7.87%, 8.83% and 7.85% respectively. Thus, it is suggested that finer granularity annotations are better utilized by deep learning algorithms in classification tasks. Similarly, semantic segmentation algorithms can achieve 8.33% better segmentation accuracy when trained by pixel-wise annotations. Our study shows not only that finer-grained annotation can improve the performance of deep learning models, but also help they extract more accurate phenotypic information from histopathological slides. The accurate and spatially precise acquisitions of phenotypic information can improve the reliability of the model prediction. Intelligence systems trained on granular annotations may help pathologists inspecting certain regions a

关键词： histopathological image annotation granularity deep learning classification semantic segmentation ANNOTATIONS Machine Learning Taxonomy Slide Pathologists Slide Dataset slump

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Cloud-Based Adaptive Particle Swarm Optimization for Waveband Selection in Big Data

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JOURNAL OF SIGNAL processing systems FOR SIGNAL image AND VIDEO TECHNOLOGY 2018年第8-9期90卷 1105-1113页

作者： Li, Yujun Liang, Kun Tang, xiaojun Gai, Keke Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Shaanxi Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Shaanxi Peoples R China Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian 710049 Shaanxi Peoples R China Pace Univ Dept Comp Sci New York NY 10038 USA

In the process of infrared spectrum analysis, wavebands selections is very important to deduce the dimension of spectrum data and improve the accuracy of analysis model. There are some methods are used in wavebands selections, such asGenetic Algorithm (GA) andParticle Swarm Optimization (PSO), variance analysis method, correlation coefficient method, Uninformative Variables Elimination (UVE) method, interval Partial Least Squares (iPLS) method, stepwise regression method, etc.. But most of these methods are used in nearly infrared spectrum analysis, and are not good enough in waveband selection. In this paper, Cloud-Based Adaptive Particle Swarm Optimization (CAPSO) algorithm is introduced to select the waveband. Its optimization process is based on cloud theory in the fuzzy control field. According to the prior computation result, it can adjust the current evolution strategy using three ways . Due to this strategy, CAPSO algorithm can reduce the number of particle easily and the convergence speed is faster than the other compared algorithms. Experiments showed that CAPSO algorithm has clear advantage in optimization time, the dimension of the selected spectrum data and the accuracy of the analysis model compared with PSO and GA.

关键词： Waveband Infrared spectrum Cloud-based adaptive particle swarm optimization Big data

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煤岩损伤研究的CT扫描技术发展现状及展望

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煤炭科学技术 2019年第1期47卷 44-51页

作者：文虎樊世星马砺郭军程小蛟童校长西安科技大学安全科学与工程学院陕西西安710054 教育部西部矿井开采及灾害防治重点实验室陕西西安710054

CT扫描技术因其无损伤、高分辨率、数字化等优点被广泛应用于煤岩损伤研究领域,为促进煤岩损伤理论研究的深入,从CT扫描技术着眼,基于前期文献调研,总结归纳了煤岩损伤领域该技术及装备的发展历程,回顾了利用CT扫描试验进行煤岩损伤表... 详细信息

CT扫描技术因其无损伤、高分辨率、数字化等优点被广泛应用于煤岩损伤研究领域,为促进煤岩损伤理论研究的深入,从CT扫描技术着眼,基于前期文献调研,总结归纳了煤岩损伤领域该技术及装备的发展历程,回顾了利用CT扫描试验进行煤岩损伤表征的研究进展,主要包括:煤岩损伤程度的CT图像和CT数表征,损伤变量的CT数定义,以及满足工程实践的煤岩损伤本构方程的构建。在此基础上,分析了当前技术条件下利用CT扫描技术进行煤岩损伤研究所遇到的问题,指出提高CT图像时空分辨率、改进图像处理软件、开发新型图像重构算法、研发与CT系统相适应的复杂应力加载系统,将会对煤岩损伤研究的发展起到更大的作用。

关键词：计算机断层扫描煤岩损伤损伤变量应力-应变本构方程

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A Hardware-Efficient Method for Extracting Statistic Information of Connected Component

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JOURNAL OF SIGNAL processing systems FOR SIGNAL image AND VIDEO TECHNOLOGY 2017年第1期88卷 55-65页

作者： Zhao, Chen Duan, Guodong Zheng, Nanning Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Shaanxi Peoples R China

The statistic information of connected components are fundamental for image processing, which could be acquired through connected components labeling. This paper proposes a hardware-efficient method for extracting statistic information of connected components in a binary image to accelerate image processing in embedded application. The proposed method scans two adjacent rows with 2 x 2 template simultaneously, meanwhile, statistic information of runs are recorded. After scanning two rows, the equivalent runs are merged, and then statistic information of completed connected region is exported directly. This method scans an image only once, which could reduce off-chip memory access massively. For a determined image resolution, the requirement of on-chip memory resource is also confirmed and not affected by the number of connected components. This algorithm is modeled with Verilog, and the simulation result shows that average processing speed could be real-time for various images with different resolution. Furthermore, the memory cost is little compared to other hardware based algorithms for labeling connected components, and the proposed method is appropriated for hardware implementation.

关键词： Connected component Extracting statistic information Hardware implementation

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An Optimization Approach of Compressive Sensing Recovery Using Split Quadratic Bregman Iteration with Smoothed ℓ0 Norm

An Optimization Approach of Compressive Sensing Recovery Usi...

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International image processing, Applications and systems Conference (IPAS)

作者： Guoan Yang Yuning You Zhengzhi Lu Junjie Yang Yuhao Wang The School of Electronic and Information Engineering Xi’an Jiaotong University Xi’an Shaanxi China

ISBN: (纸本)9781728102481;9781728102474

An optimization algorithm for image recovery is a core issue in the field of compressive sensing (CS). This paper deeply studied the CS reconstruction algorithm based on split Bregman iteration with ℓ 1 norm, which enables the ℓ 1 norm to approximate the original ℓ 0 norm during the optimization process. Consequently, we proposed another novel algorithm improving the precision and the convergence speed based on split quadratic Bregman iteration (SQBI) with ℓ 0 norm. Besides, we analyzed its convergence by proving two monotonically decreasing theorems. Inspired by previous researches, we applied smoothed ℓ 0 norm for the optimization problem to replace the traditional ℓ 0 norm in CS. The improvement is made by using a Gaussian function to approximate the ℓ 0 norm, transforming it into a convex optimization problem, and eventually achieved a convergent solution by the steepest descent method. The experimental results show that under the same conditions, compared with other state-of-the-art algorithms, the reconstruction accuracy of the CS reconstruction algorithm based on the SQBI with smoothed ℓ 0 norm is improved significantly, and its convergence rate is also accelerated as well.

关键词： Compressed sensing image reconstruction Convergence Optimization Reconstruction algorithms Convex functions

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