M
Max Q.-H. Meng
Researcher at Southern University of Science and Technology
Publications - 667
Citations - 12901
Max Q.-H. Meng is an academic researcher from Southern University of Science and Technology. The author has contributed to research in topics: Computer science & Robot. The author has an hindex of 49, co-authored 617 publications receiving 9695 citations. Previous affiliations of Max Q.-H. Meng include Harbin Institute of Technology & Stanford University.
Papers
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Improving RGB-D SLAM in dynamic environments: A motion removal approach
TL;DR: The proposed novel RGB-D data-based motion removal approach acted as a pre-processing stage to filter out data that were associated with moving objects in the traversed environments during the SLAM process.
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An efficient neural network approach to dynamic robot motion planning
Simon X. Yang,Max Q.-H. Meng +1 more
TL;DR: A biologically inspired neural network approach to real-time collision-free motion planning of mobile robots or robot manipulators in a nonstationary environment is proposed and is guaranteed by qualitative analysis and the Lyapunov stability theory.
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Computer-Aided Detection of Bleeding Regions for Capsule Endoscopy Images
Baopu Li,Max Q.-H. Meng +1 more
TL;DR: A new computer-aided system aimed for bleeding region detection in CE images is proposed, which exploits color texture feature, an important clue used by physicians, to analyze status of gastrointestinal tract.
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A Cubic 3-Axis Magnetic Sensor Array for Wirelessly Tracking Magnet Position and Orientation
TL;DR: In this paper, the authors proposed a magnetic localization and orientation system for medical applications, which uses a small magnet enclosed in the object to serve as excitation source, so it does not require the connection wire and power supply for the excitation signal.
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Neural RRT*: Learning-Based Optimal Path Planning
TL;DR: A novel optimal path planning algorithm based on the convolutional neural network (CNN), namely the neural RRT* (NRRT*), which utilizes a nonuniform sampling distribution generated from a CNN model and achieves better performance.