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International Journal of Digital Content Technology and its Applications 2012年第4期6卷 15-23页

作者： Wang, Hu Zhang, Wensheng Liu, Jin State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China State Key Laboratory of software Engineering Computer School Wuhan University Wuhan 430072 China

The histogram of gradients for three-dimensional images (HOG3D) has been widely used to generate descriptors of local features for the matching of salient interest blobs between volumetric medical images. The distinctiveness of descriptors is essential to match similar patterns as well as differentiate different patterns. However, most methods for HOG3D are based on the unequal azimuth-elevation-angle division of the three-dimensional orientations, leading to the reduction of the distinctiveness. This paper presents a description method to extract distinctive descriptors by using a novel histogram of gradients based on the maximum directional derivative (HOG3DMAX). The three-dimensional orientations are divided equally into 24 homogeneous regions for the bins of gradient histogram to improve the distinctiveness. In addition, the interpolation of gradients is applied in HOG3DMAX according to the relatively simple form of the homogeneous regions in order to avoid harmful effects from boundary in images. Experiments were performed on three sets with different extents of similarity. The cluster analysis and classification accuracy were utilized to validate the proposed method. Compared with the commonly used azimuth-elevation based method, the new method shows lower intra-cluster pairwise distances relative to inter-cluster pairwise distances, and achieved 5% higher classification accuracy. The results indicate the improvement of distinctiveness by using equally divided regions and the interpolation of gradients. We conclude that the HOG3DMAX is an effective description method for three-dimensional images.

关键词： Cluster analysis

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Simulating Plant Plasticity under Light Environment:A Source-Sink Approach

Simulating Plant Plasticity under Light Environment:A Source...

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2012 IEEE 4th International Symposium on Plant Growth Modeling,Simulation,Visualization and Applications(PMA 2012)

作者： Haoyu Wang Mengzhen Kang Jing Hua State LIAMA Institute of AutomationChinese Academy of Sciences Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

Simulation of plant structure competing for light source has mostly been done by directly modifying plant structure according to light ***-structural plant models,however,emphasize the influence of light interception on biomass production,and consequently plant *** this paper,we integrate a light distribution model with GreenLab model,which used Beer-Law in computing biomass *** replacing Beer-Law with a light interception model for biomass production,the combined model was able to simulate the effect of light condition on plant structure through source-sink *** positive and negative sides of this approach are discussed.

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Click frauds and price determination models

Click frauds and price determination models

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IEEE International Conference on Intelligence and Security Informatics, ISI

作者： Xiarong Li Daniel Zeng Lei Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China University of Arizona Tucson USA

Click fraud (CF) has become a serious problem in the online advertising, making the anti-CF issue quite important. In this paper, we analyze the effects of the price determination model on the CF situations in online advertising. Our theoretical results show that the flat-rate model can induce more click frauds than the real-time-bidding model. Our finding is validated with a real advertising dataset.

关键词： Advertising Biological system modeling Games Mathematical model Analytical models Filtering Nash equilibrium

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Semi-supervised Chinese Word Segmentation for CLP2012 2

Semi-supervised Chinese Word Segmentation for CLP2012

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2nd CIPS-SIGHAN Joint Conference on Chinese Language Processing, CLP 2012

作者： He, Sai-Ke He, Nan Cen, Song-Xiang Lu, Jun State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Co. Ltd China Baidu Inc Baidu Campus No. 10 Shangdi 10th Street Haidian District Beijing100085 China

Chinese word segmentation (CWS) lays the essential foundation for Mandarin Chinese analysis. However, its performance is always limited by the identification of unknown words, especially for short text such as Micro-blog. While local context are helpless in handling unknown words, global context do manifest enough contextual information, and could be used to guide CWS process. Based on this motivation, in this paper, we report our attempt toward building an integrated model in semi-supervised manner. Considering the complexity of model, we design a strategy to manipulate global and local contextual information asynchronously. Though the coverage of unknown words by such integrated model is still small, official results from CLP2012 present promising result. © 2021 CLP 2012 - 2nd CIPS-SIGHAN Joint Conference on Chinese Language Processing. All Rights Reserved.

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Leader-follower synchronization for complex dynamical networks via sampled-data control

Leader-follower synchronization for complex dynamical networ...

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Chinese control Conference

作者： Wangli He Feng Qian Qing-Long Han The Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Centre for Intelligent and Networked Systems Central Queensland University

ISBN: (纸本)9781467325813

This paper is concerned with leader-follower synchronization of complex dynamical networks with sampled-data control. By sampling the signal from the leader at some discrete time instants and using a zero-order hold function, synchronization is achieved between the network and a desired orbit, known as the leader. By applying Lyapunov functional approach and the property of the network topology matrix, a delay-dependent criterion is derived. It is shown that synchronization of N coupled dynamical systems with a leader can be recast into the stability of N decoupled systems, in which eigenvalues of the network topology matrix are involved. Finally, a chaotic neural network is used to illustrate the effectiveness of the proposed method.

关键词： Synchronization Sampled-data control complex networks

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A weighted pattern recognition algorithm for short-term traffic flow forecasting

A weighted pattern recognition algorithm for short-term traf...

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IEEE International Conference on Networking, Sensing and control

作者： Shuangshuang Li Zhen Shen Fei-Yue Wang State Key Laboratory of Management and Control for Complex Systems Beijing Engineering Research Center for Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China

The k-nearest neighbor (k-NN) nonparametric regression is a classic model for single point short-term traffic flow forecasting. The traffic flows of the same clock time of the days are viewed as neighbors to each other, and the neighbors with the most similar values are regarded as nearest neighbors and are used for the prediction. In this method, only the information of the neighbors is considered. However, it is observed that the “trends” in the traffic flows are useful for the prediction. Taking a sequence of consecutive time periods and viewing the a sequence of “increasing”, “equal” or “decreasing” of the traffic flows of two consecutive periods as a pattern, it is observed that the patterns can be used for prediction, despite the patterns are not from the same clock time period of the days. Based on this observation, a pattern recognition algorithm is proposed. Moreover, empirically, we find that the patterns from different clock time of the days can have different contributions to the prediction. For example, if both to predict the traffic flow in the morning, the pattern from the morning can lead to better prediction than same patterns from afternoon or evening. In one sentence, we argue that both the pattern and the clock time of the pattern contain useful information for the prediction and we propose the weighted pattern recognition algorithm (WPRA). We give different weights to the same patterns of different clock time for the prediction. In this way, we take both virtues of the k-NN method and the PRA method. We use the root mean square error (RMSE) between the actual traffic flows and the predicted traffic flows as the measurement. By applying the results to actual data and the simulated data, about 20% improvement compare with the PRA is obtained.

关键词： Clocks Data models Pattern recognition Prediction algorithms Mathematical model Meteorology Vectors

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A novel parking control algorithm for a car-like mobile robot

A novel parking control algorithm for a car-like mobile robo...

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IEEE International Conference on Networking, Sensing and control

作者： Yaru Zhang Shuming Tang Zhilin Zhang State Key Laboratory of Management and Control for Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China

This paper proposes a novel algorithm for parking motion of a Car-like mobile robot. The algorithm presented here addresses calculating equations for planning a parking path in real time. Moreover, by incorporating the constraints of the mechanical and kinematical characteristics of the car and the geometry of the parking lot in the path planning, we can turn a parking problem into solving algebraic equations. By tracking a planned path, the Car-like mobile robot can drive into the parking area without hitting any boundaries. The efficiency of the proposed algorithm is demonstrated by simulation.

关键词： Vehicles Mobile robots Algorithm design and analysis Mathematical model Turning Equations Signal processing algorithms

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A review on vision-based pedestrian detection in intelligent transportation systems

A review on vision-based pedestrian detection in intelligent...

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IEEE International Conference on Networking, Sensing and control

作者： Bo Li Qingming Yao Kunfeng Wang State Key Laboratory of Management and Control for Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China

Pedestrians are key participants in transportation systems, so pedestrian detection in video surveillance systems is of great significance to the research and application of Intelligent Transportation systems (ITS). We review some methods and models for vision-based pedestrian detection in recent years. In this paper, the pedestrian detection techniques are divided into macroscopic and microscopic according to different application in transportation systems. Macroscopic pedestrian detection aims to estimate crowd density without distinguishing each pedestrian, and microscopic pedestrian detection focuses on detection and recognition of individual pedestrians. The latter detection style is deeply studied, so it is presented in detail in this paper, especially for the feature-classifier-based detection method. Finally, the pedestrian detection algorithms are discussed and concluded from the viewpoint of video surveillance and ITS. Existing problems and future trends are presented in that section.

关键词： Feature extraction Shape Training Transportation Traffic control Video surveillance

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Rear lamp based vehicle detection and tracking for complex traffic conditions

Rear lamp based vehicle detection and tracking for complex t...

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IEEE International Conference on Networking, Sensing and control

作者： Ye Li Qingming Yao State Key Laboratory of Management and Control for Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China

In video surveillance system, detection and tracking of vehicles are two foundational and significant tasks. In this paper, a vehicle detection and tracking method based on rear lamp pairs is proposed. The proposed method combines color with motion information to perform vehicle detection. In order to adapt to different weather conditions like night, the rear lamps are divided into two categories: unlit lamps and lighted lamps. First, threshold segmentation are used to extract both the unlit and lighted lamp candidates in hue-saturation-value (HSV) color space and the thresholds are selected automatically by maximally stable extremal region (MSER) method. Then, all lamp candidates are tracked by using Kalman filter and lamp candidates with short-lived trajectories are removed to avoid disturbances. Next, two adjacent lamp candidates with similar speed are bound together as a region of interest (ROI), which represents a potential pair of lamps. Image cross-correlation symmetry analysis based on Gabor filter is utilized to find the ROIs with symmetrical texture and these symmetric ROIs can be regarded as pairs of lamps. The experimental results show that the proposed method can effectively deal with various illumination conditions and improve the accuracy and robustness of vehicle detection. In addition, this method can perform vehicle detection and tracking under complex traffic conditions and the Gabor filter based symmetry analysis can successfully suppress subtle difference between the left and right parts of a vehicle as well as environment noises.

关键词： Vehicles Image color analysis Vehicle detection Gabor filters Correlation Tracking Kalman filters

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A vehicle license plate recognition system based on analysis of maximally stable extremal regions

A vehicle license plate recognition system based on analysis...

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IEEE International Conference on Networking, Sensing and control

作者： Bo Li Bin Tian Qingming Yao Kunfeng Wang State Key Laboratory of Management and Control for Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China

Vehicle License Plate Recognition (VLPR) system is a core module in Intelligent Transportation systems (ITS). In this paper, a VLPR system is proposed. Considering that license plate localization is the most important and difficult part in VLPR system, we present an effective license plate localization method based on analysis of Maximally Stable Extremal Region (MSER) features. Firstly, MSER detector is utilized to extract candidate character regions. Secondly, the exact locations of license plates are inferred according to the arrangement of characters in standard license plates. The advantage of this license plate localization method is that less assumption of environmental illumination, weather and other conditions is made. After license plate localization, we continue to recognize the license plate characters and color to complete the whole VLPR system. Finally, the proposed VLPR system is tested on our own collected dataset. The experimental results show the availability and effectiveness of our VLPR system in locating and recognizing all the explicit license plates in an image.

关键词： Licenses Image color analysis Character recognition Lighting Histograms Feature extraction Image segmentation

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