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IEEE International Conference on Cyber technology in Automation, Control, and Intelligent Systems

作者： Liqian Feng Sheng Bi Min Dong Fating Hong Yuhong Liang Qinjie Lin Yunda Liu School of Computer Science&Engineering South China University of Technology Guangzhou

ISBN: (纸本)9781538604915

This paper demonstrates a set of approaches addressing mobile robot localization problems. The approaches can operate efficiently in 2D occupancy grid map created by robot which is equipped with LIDAR sensor. Given a complex environment and a known map, the robot could be placed at anywhere initially and the global localization system described in this paper can be applied to startup stage in which robot can realize its initial location and orientation in the map without any movement. In the following stage, the system can track the moving robot's position in real time. Thus as long as the robot start the navigation process, the robot system knows where the robot is at the beginning and the robot can be navigated to a reachable place directly. Combined with other sensors, such as cliff sensor, the robot can perceive the kidnapped problem, and then relocate the position to recover from global localization failures. The experimental results show that compared with appointing the initial position and orientation manually, it costs seconds in startup stage to figure out the robot's initial pose in the map, and the system can update the robot's position in motion in real time.

关键词： Robot sensing systems Robot kinematics Laser radar Two dimensional displays Mobile robots Monte Carlo methods

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Feature Selection Algorithm Based on K-means Clustering

Feature Selection Algorithm Based on K-means Clustering

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IEEE International Conference on Cyber technology in Automation, Control, and Intelligent Systems

作者： Xue Tang Min Dong Sheng Bi Maofeng Pei Dan Cao Cheche Xie Sunhuang Chi School of Computer Science&Engineering South China University of Technology GuangZhou

In order to improve the performance of the feature selection algorithm, a feature selection algorithm based on K-means clustering is designed. The algorithm makes use of the idea of K-means clustering based on cosine distance to cluster the features, so that the obtained feature subset has strong correlation and no redundancy. The experimental results show that the feature selection algorithm based on K-means clustering has high efficiency for classification tasks and has short running time, so the algorithm has strong practicability for feature selection.

关键词： Classification algorithms Heuristic algorithms Feature extraction Clustering algorithms Dynamic programming Machine learning algorithms Decision trees

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Data processing method for smart streetlamp energy consumption analysis systems based on data mining

Data processing method for smart streetlamp energy consumpti...

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IEEE International Conference on Cyber technology in Automation, Control, and Intelligent Systems

作者： Maofeng Pei Min Dong Sheng Bi Xue Tang Haoxi Liu Sunhuang Chi Cheche Xie School of Computer Science&Engineering South China University of Technology GuangZhou

Smart streetlamp as an important part of smart cities aiming at constructing cities with a higher level of intelligence, automation, information and network has been applied in urban construction widely and successfully. This paper presents a data processing method for smart streetlamp energy consumption analysis systems based on data mining, which is not only used to classify, recognize and correct problem data in energy consumption data, but also used in smart streetlamp energy consumption analysis systems for abnormal alarm, troubleshooting advice, energy consumption prediction, etc. It is necessary to process energy consumption data before energy consumption analysis, because the problem data might interfere the analysis and cause deviations and errors. Moreover, the method proposed is used to realize the functions of processing historical data and monitoring in this paper. Finally, the experimental result proves the availability and reliability of the method.

关键词： Energy consumption Data processing Data mining Monitoring Regression tree analysis Buildings Energy measurement

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Obstacle avoidance of aerial vehicle based on monocular vision

Obstacle avoidance of aerial vehicle based on monocular visi...

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IEEE International Conference on Cyber technology in Automation, Control, and Intelligent Systems

作者： Weiheng Qiu Sheng Bi Cankun Zhong Yi Luo Jieming Li Boyu Sun School of Computer Science&Engineering South China University of Technology GuangZhou

ISBN: (纸本)9781538604915

Collision-free autonomous navigation is extremely important for quadcopter and other flying robots. The implementation of autonomic moving capabilities can contribute significantly to their promotion and usage in fields such as goods delivering, aerial photos shooting, and monitoring. In order to realize the autonomous flight without crash, the obstacle avoidance problem demands a prompt solution. Also, with the concern of cost and endurance, using only single camera to perform this task would be a better choice for low-cost flying robots. Thus, this paper focuses on achieving quadcopter's collision avoidance in unknown stable (rarely changes, such as high sky or inner room) environment only by single camera. The algorithm proposed by this paper is composed of PTAM (Parallel Tracking and Mapping), DTAM (Dense Tracking and Mapping in Real-Time) algorithm and CNN (Convolutional Neural Network). PTAM is used to create the 3D map of the environment. DTAM is used to obtain the depth map of those image frames. And the CNN is used to train and get a model used for automatically avoidance. Finally, this algorithm is proved to be valid by an experiment.

关键词： Cameras Feature extraction Tracking Robot vision systems Three-dimensional displays Image edge detection

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Dart: Domain-adversarial residual-transfer networks for unsupervised cross-domain image classification

arXiv

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arXiv 2018年

作者： Fang, Xianghong Bai, Haoli Guo, Ziyi Shen, Bin Hoi, Steven Xu, Zenglin SMILE Lab Univ. of Electronic Sci and Tech.of China Chengdu Sichuan611731 China Dept Computer Science and Engineering Chinese University of Hong Kong Shatin N. T. Hong Kong SMILE Lab Sch. of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan611731 China Google Inc. Singapore Management University

The accuracy of deep learning (e.g., convolutional neural networks) for an image classification task critically relies on the amount of labeled training data. Aiming to solve an image classification task on a new domain that lacks labeled data but gains access to cheaply available unlabeled data, unsupervised domain adaptation is a promising technique to boost the performance without incurring extra labeling cost, by assuming images from different domains share some invariant characteristics. In this paper, we propose a new unsupervised domain adaptation method named Domain-Adversarial Residual-Transfer (DART) learning of Deep Neural Networks to tackle cross-domain image classification tasks. In contrast to the existing unsupervised domain adaption approaches, the proposed DART not only learns domain-invariant features via adversarial training, but also achieves robust domainadaptive classification via a residual-transfer strategy, all in an end-to-end training framework. We evaluate the performance of the proposed method for cross-domain image classification tasks on several well-known benchmark data sets, in which our method clearly outperforms the state-of-the-art approaches. Copyright © 2018, The Authors. All rights reserved.

关键词： Image classification

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Detection of Breast Tumour Tissue Regions in Histopathological Images using Convolutional Neural Networks

Detection of Breast Tumour Tissue Regions in Histopathologic...

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International Image Processing, Applications and Systems Conference (IPAS)

作者： Yibao Sun Zhaoyang Xu Carina Strell Carlos Fernández Moro Fredrik Wärnberg Le Dong Qianni Zhang EECS QMUL London UK Dept. of Oncology-Pathology Karolinska Institute Stockholm Sweden Dept. of Clinical Pathology/Cytology Karolinska University Hospital Stockholm Sweden Dept. of Surgical Sciences Uppsala Academic Hospital Uppsala Sweden Sch. of Computer Science and Engineering Univ. of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781728102481;9781728102474

Ductal carcinoma in situ (DCIS) is considered a pre-invasive breast cancer and sometimes it can develop into an invasive ductal carcinoma. The analysis of histopathological images to detect tumour border of DCIS could provide important information for better diagnosis of patients. We present a deep learning based system to automatically identify DCIS in histopathological images. Specifically, a convolutional neural network (CNN) is first trained to predict labels of small patches cropped out of a histopathological whole slide image. Next, a sliding window method is used to produce a probability map of DCIS. Finally, given the probability map, a tumor border of DCIS is produced and delineated with the method of Marching Cubes to facilitate pathologists' review and assessment. Evaluation of cross validation demonstrates that the CNN model of GoogleNet performs well in histology image patch classification with an overall accuracy of (98.46±0.40)% and identifies the DCIS tissue patches with a F1-score of (97.40±1.18)% (mean±variance). Moreover, around 95.6% tumour tissue within the enclosed tumour regions can be identified by our developed method. Finally, the goal of tumor border detection can be well achieved with a few post-processing steps.

关键词： Tumors Training Image segmentation Data mining Feature extraction Conferences

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Real-time gesture recognition system based on Camshift algorithm and Haar-like feature

Real-time gesture recognition system based on Camshift algor...

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IEEE International Conference on Cyber technology in Automation, Control, and Intelligent Systems

作者： Yushan Yu Sheng Bi Yaoyang Mo Weiheng Qiu School of Computer Science&Engineering South China University of Technology

ISBN: (纸本)9781509027347

This paper propose a real-time combined method of Camshift [1] algorithm and Haar-like feature detection [2] for tracking and recognizing hand gesture in images acquired by a possibly moving camera. A Haar-like classifier is used during the initializing of the system to acquire the user's hand color. Camshift algorithm is applied with the acquired color to track the position of the hand, accompanied with a two-dimensional Kalman filter to prevent target occlusion. An offline training with Adaboost learning algorithm [3] of various hand gestures is employed to allow the classifier to recognize static hand gestures. With the information provided by Haar-like classifier, Tracking with Camshift will have more robust performance and less likely to lost targets. As the experimental result shows, the method we proposed have better performance than simply applying any of the two methods, especially in some complicate background conditions where skin-color disturbances or occlusion exist.

关键词： Image color analysis Skin Gesture recognition Feature extraction Classification algorithms Real-time systems Cameras

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Multi-feature gesture recognition based on Kinect

Multi-feature gesture recognition based on Kinect

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IEEE International Conference on Cyber technology in Automation, Control, and Intelligent Systems

作者： Yue Zhao Yunda Liu Min Dong Sheng Bi School of Computer Science&Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9781509027347

Human computer Interaction (HCI) has been a popular research area during the last few years. Compared with the tradition HCI methods such as using a keyboard or mouse, people prefer to have their tasks done in a more natural way. As an essential form of non-verbal communication in daily life, gesture is a good choice to turn the ideas into reality. Although various recognition methods are proposed to solve the problem, these methods are time-tensed, space-tensed or miscellaneous. This paper introduced a new method to recognize the hand gesture correctly and efficiently. The recognition is done through two phases: the skeleton phase concerning capturing and processing skeleton feature of the hand gesture, and the hand phase focusing on extracting hand contour feature of the hand gesture. Experimental results confirm an overall 94% accuracy in recognizing and matching the pre-defined templates and robustness to backgrounds.

关键词： Skeleton Feature extraction Human computer interaction Sensors Heuristic algorithms computers Gesture recognition

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Double Constrained NMF for Partial Multi-View Clustering

Double Constrained NMF for Partial Multi-View Clustering

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Bin Qian Xiaobo Shen Yanyang Gu Zhenmin Tang Yuhua Ding School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Sch. of Inf. & Commun. Technol. Griffith Univ. Nathan Griffith NSW Australia

Many computer vision problems involve data with multiple views, where each view corresponds to a certain type of feature. To integrate information from multiple views in the unsupervised setting, multi-view clustering methods have been developed to cluster multiple views simultaneously. Most existing methods only consider the case that each example appears in all the views. However, data with partial views is often occurred in real applications. For example, several certain sensors sometimes have faults, then the data may not be captured completely, which will lead to the case that only partial views are available. In this paper, we propose a nonnegative matrix factorization (NMF) method for partial multi-view clustering, which incorporates the cluster similarity and manifold preserving constraints into an unified framework. The basic idea of the proposed method, named Double Constrained NMF (DCNMF), is to push clustering solutions of different views from the same examples towards a common membership matrice, and to maintain the latent geometric structure of the views simultaneously. Moreover, we develop an efficient optimization sch.me for the proposed method. Experiments on several two-view datasets demonstrate the advantages of the proposed method on partial multi-view clustering tasks.

关键词： Manifolds Clustering methods Optimization Feature extraction Subspace constraints Loss measurement computer science

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Clonal Selection-Based Surveillance Algorithm for Transmission Control Protocol Anomaly Network Behavior

Clonal Selection-Based Surveillance Algorithm for Transmissi...

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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Yan Zhang Caiming Liu Qin Li Zhonghua Li Hongying Qin Jinquan Zeng School of Computer Science Leshan Normal University Leshan China Sichuan Provincial Education Department Leshan Normal University Leshan China Sch. of Comput. Sci. Leshan Normal Univ. School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

The dynamic nature and complexity of Internet make the surveillance of network behavior more and more difficult. To effectively survey anomaly network behaviors, the clonal selection principle is used to construct a surveillance algorithm for transmission control protocol (TCP) anomaly network behaviors in this paper. In the proposed algorithm, TCP network data are collected. Key features of network data are extracted to be the source data for the surveillance of network behavior. Network feature data are pretreated to create the data set of network operation. Common characteristics of network operations are estimated to create the data set of network behavior. The normal network behavior library and anomaly network behavior library are constructed to provide fundamental training data for the surveillance of network behavior. Essential mechanisms of the clonal selection principle are simulated. The data sets of monitor are defined. Three stages of dynamic evolution of monitors are simulated. The match method for monitor and network behavior are constructed to recognize different kinds of anomaly network behavior to survey TCP anomaly network behaviors.

关键词： Surveillance Ports (computers) IP networks Libraries Security Heuristic algorithms

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