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Your search keyword '"Hou, Beiping"' showing total 108 results
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108 results on '"Hou, Beiping"'

10. A Novel Classifier Using Subspace Analysis for Face Recognition

Author

Yu, Aihua, Li, Gang, Hou, Beiping, Wang, Hongan, Barbosa, Simone Diniz Junqueira, Series editor, Chen, Phoebe, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Yuan, Junsong, Series editor, Zhou, Lizhu, Series editor, Wang, Yongtian, editor, Wang, Shengjin, editor, Liu, Yue, editor, Yang, Jian, editor, Yuan, Xiaoru, editor, He, Ran, editor, and Duh, Henry Been-Lirn, editor

Published
2018
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12. ZUST Campus: A Lightweight and Practical LiDAR SLAM Dataset for Autonomous Driving Scenarios.

Author

He, Yuhang, Li, Bo, Ruan, Jianyuan, Yu, Aihua, and Hou, Beiping

Subjects
LIDAR, OPTICAL radar, POINT cloud
Abstract

This research proposes a lightweight and applicable dataset with a precise elevation ground truth and extrinsic calibration toward the LiDAR (Light Detection and Ranging) SLAM (Simultaneous Localization and Mapping) task in the field of autonomous driving. Our dataset focuses on more cost-effective platforms with limited computational power and low-resolution three-dimensional LiDAR sensors (16-beam LiDAR), and fills the gaps in the existing literature. Our data include abundant scenarios that include degenerated environments, dynamic objects, and large slope terrain to facilitate the investigation of the performance of the SLAM system. We provided the ground truth pose from RTK-GPS and carefully rectified its elevation errors, and designed an extra method to evaluate the vertical drift. The module for calibrating the LiDAR and IMU was also enhanced to ensure the precision of point cloud data. The reliability and applicability of the dataset are fully tested through a series of experiments using several state-of-the-art LiDAR SLAM methods. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Comprehensive Analysis of Cooperative Particle Swarm Optimization with Adaptive Mixed Swarm

Author

Jie, Jing, Hou, Beiping, Zheng, Hui, Wu, Xiaoli, Junqueira Barbosa, Simone Diniz, Series editor, Chen, Phoebe, Series editor, Cuzzocrea, Alfredo, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Ślęzak, Dominik, Series editor, Washio, Takashi, Series editor, Yang, Xiaokang, Series editor, Fei, Minrui, editor, Peng, Chen, editor, Su, Zhou, editor, Song, Yang, editor, and Han, Qinglong, editor

Published
2014
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20. A Novel Bidirectional Long Short-Term Memory Network With Weighted Attention Mechanism for Industrial Soft Sensor Development

Author

Zhang, Miao, Xu, Beike, Jie, Jing, Hou, Beiping, and Zhou, Le

Abstract

Accurate measurement of key quality variables is of great significance for evaluating product quality and ensuring production safety. How to extract useful dynamic latent features from complex process data for effective quality prediction is still a difficult and hot topic in soft sensing modeling. The existing soft sensing models based on long short-term memory (LSTM) do not consider the correlation between hidden states of different time steps when extracting dynamic nonlinear features. To solve these problems, a novel bidirectional LSTM network with weighted attention mechanism (WA-BiLSTM) is proposed in this article to improve the prediction performance for dynamic nonlinear industrial processes. In the WA-BiLSTM model, the bidirectional structure of BiLSTM is used to learn the dynamic information of the input sequence. A weighted attention mechanism layer is designed to dynamically extract the correlation information between the hidden states of different batches. In addition, the ratio between the historical hidden states and the current hidden state is adjusted reasonably by assigning adaptive parameters. Finally, the experimental results on a debutanizer column process and an IndPensim process demonstrate the effectiveness and superiority of the proposed method.

Published
2024
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21. Adaptive hand image function region segmentation for intelligent aided diagnosis

Author

Liu, Yang, Jie, Jing, Zheng, Hui, Hou, Beiping, and Li, Zhenglin

Abstract

Smart hand diagnosis simulates the expert knowledge of hand diagnosis in traditional Chinese medicine and makes a diagnosis through the hand image information. To ensure the correctness of the diagnosis, an adaptive hand image function region segmentation is developed. Firstly, a hand image is acquired by a mobile app and its contour is extracted by multi-threshold segmentation and edge detection after series preprocessing. Secondly, some key feature corner points in the hand contour are detected through the dynamic intersection method, and then the hand image is segmented into 14 function regions, referring to the nine palace figures of hand diagnosis, in which the feature of each region can indicate the sampled people's health status for a specific physical function. The experimental results show that the hand image function regions can be divided successfully and keep the pathological characteristics in good condition. All that prove the segmentation method has good robustness and high accuracy.

Published
2024
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22. A Novel Visual SLAM Based on Multiple Deep Neural Networks.

Author

Zhu, Bihong, Yu, Aihua, Hou, Beiping, Li, Gang, and Zhang, Yong

Subjects
ARTIFICIAL neural networks, VISUAL odometry, CONVOLUTIONAL neural networks, FEATURE extraction, ENVIRONMENTAL mapping
Abstract

The current visual simultaneous localization and mapping (SLAM) systems require the use of matched feature point pairs to estimate camera pose and construct environmental maps. Therefore, they suffer from poor performance of the visual feature matchers. To address this problem, a visual SLAM using deep feature matcher is proposed, which is mainly composed of three parallel threads: Visual Odometry, Backend Optimizer and LoopClosing. In the Visual Odometry, the deep feature extractor with convolutional neural networks is utilized for extracting feature points in each image frame. Then, the deep feature matcher is used for obtaining the corresponding feature landmark pairs. Afterwards, a fusion method based on the last and the reference frame is proposed for camera pose estimation. The Backend Optimizer is designed to execute local bundle adjustment for a part of camera poses and landmarks (map points). While LoopClosing, consisting of a lightweight deep loop closure detector and the same matcher as the one used in Visual Odometry is utilized for loop correction based on pose graph. The proposed system has been tested extensively on most of benchmark KITTI odometry dataset. The experimental results show that our system yields better performance than the existing visual SLAM systems. It can not only run in real-time at a speed of 0.08 s per frame, but also reduce estimation error by at least 0.1 m. [ABSTRACT FROM AUTHOR]

Published
2023
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27. Study on Wavelet-Based Fuzzy Multiscale Edge Detection Method

Author

Zhu, Wen, Hou, Beiping, Zhang, Zhegen, Zhou, Kening, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Wang, Lipo, editor, and Jin, Yaochu, editor

Published
2005
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49. Fast Human Detection Using Motion Detection and Histogram of Oriented Gradients

Author

Zhu Wen and Hou Beiping

Subjects
General Computer Science, Computer science, business.industry, Motion detection, Pattern recognition, Image (mathematics), Support vector machine, Svm classifier, Histogram of oriented gradients, Sliding window protocol, Histogram, Computer vision, Artificial intelligence, business
Abstract

This paper presents a real-time Human detection algorithm based on HOG (Histograms of Oriented Gradients) features and SVM (Support Vector Machine) architecture. Motion detection is used to extract moving regions, which can be scanned by sliding windows; detecting moving region can subtract unnecessary sliding windows of static background regions under the surveillance conditions, then detection efficiency can be improved. Every sliding window is regarded as an individual image region and HOG features are calculated as classified eigenvectors. At last, the detected video objects can be categorized into pre-defined groups of humans and other objects by using SVM classifier. Experimental results from real-time video are provided which demonstrate the effectiveness of the method.

Published
2011

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