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Hengyu Li

Researcher at Shanghai University

Publications -  79
Citations -  475

Hengyu Li is an academic researcher from Shanghai University. The author has contributed to research in topics: Mobile robot & Giant magnetoimpedance. The author has an hindex of 8, co-authored 79 publications receiving 296 citations.

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Sonar image splicing method

TL;DR: In this article, the authors proposed a sonar image splicing method, which comprises the steps of removing noise in order to increase the contrast ratio through image preprocessing, transforming a scaling parameter and a rotation parameter into translation parameters through carrying out log-polar transformation to an image amplitude frequency spectrum, and carrying out algorithm analysis based on phase correlation; filtering scale and rotation factors through image geometric transformation; working out the translation parameters via an algorithm based on the phase correlation, and carry out coordinate mapping through the scaling parameter, the rotation parameter and the translation parameter which are worked out,
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Research on path planning of three-neighbor search A* algorithm combined with artificial potential field:

TL;DR: In this paper, a three-neighbor search A* algorithm is proposed to solve the problem of many search nodes in path planning and the calculation time is long, which is the main drawback of the A * algorithm.
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Stitching of depth and color images from multiple RGB-D sensors for extended field of view

TL;DR: A method to extend the field of view by stitching depth and color images from multiple RGB-D sensors to form a depth and a color panorama and a textured 3-D points to a compositing surface is presented.
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Interactive Micromanipulation of Picking and Placement of Nonconductive Microsphere in Scanning Electron Microscope

TL;DR: The experimental results demonstrate that the proposed contact detection method can efficiently protect the end-tip from damage, regardless of operator skills in initial positioning operations.
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A sea-sky-line detection method based on Gaussian mixture models and image texture features:

TL;DR: The Gaussian mixture model is introduced as the probability distribution model for the marine image and the gray level co-occurrence matrix is referenced to initialize the Gaussian components.