Dr. Haifeng Hu Profile

Dr. Haifeng Hu

at Sun Yat-Sen Univ

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Publications (3)

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Proceedings Article | 12 February 2001 Paper

Three-dimensional shape measurement in neurovision system

Yingen Xiong, Haifeng Hu

Proceedings Volume 4189, (2001) https://doi.org/10.1117/12.417196

KEYWORDS: Neural networks, 3D metrology, Calibration, 3D image processing, Imaging systems, Cameras, Manufacturing, Stereoscopic cameras, Machine vision, 3D vision

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In this paper, a new on-line measurement and accuracy analysis method for part configuration and surface is presented by combining computer vision and neural networks. Different from conventional contact measurement, it is non-contact measurement method, and it can operate on-line. In this method, the 3D configuration and surface of part are reconstructed from stereo image pair taken by computer vision system. The architecture for parallel implementation of part measurement system is developed using neural networks. Several relevant approaches including system calibration, stereo matching, and 3D reconstruction are constructed using neural networks. Instead of conventional system calibration method that needs complicated iteration calculation process, the new system calibration approach is presented using BP neural network. The 3D coordinates of part surface are obtained from 2D points on images by several BP neural networks. Based on the above architecture and the approaches, the part measurement and accuracy analysis system for intelligent manufacturing is developed by making fall use of the advantages of neural networks. The experiments and application research for this system is also presented in this paper. It is proved through the actual application that the method presented in this paper can meet the needs of on-line measurement for parts in intelligent manufacturing. It has important value especially for on-line measurement of parts that have complicated surface.

Proceedings Article | 10 October 2000 Paper

Stereoscopic neurovision system for part recognition in intelligent assembly system

Yingen Xiong, Haifeng Hu

Proceedings Volume 4222, (2000) https://doi.org/10.1117/12.403909

KEYWORDS: Neural networks, Intelligence systems, 3D image processing, Object recognition, 3D image reconstruction, Image processing, Neurons, 3D modeling, 3D acquisition, Silicon

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In this paper, a new 3D recognition method for intelligent assembly system is presented. In this method neural network technology is used to provide new methodologies for solving difficult computational problems in 3D recognition processes. The method can be divided into two parts. In the first part, phase based stereo matching techniques are used to find the correspondence between left and right image in stereo image pair. The Hopfield neural network is established, so that the computation can be implemented efficiently in parallel. A 3D object reconstruction neural network is constructed by using BP neural network. With the results of stereo matching, the 3D configuration and shape can be reconstructed. In the second part, the feature vector of 3D object is constructed by using 3D moment and its invariant. With the results obtained in first parts, ART2 neural network is adopted for neural network classifier. With the ART2 neural network classifier, the 3D objects can be recognized and classified. The method is tested with both synthetic and real parts in intelligent assembly system. Good results are obtained. It is proved through the experiments and actual applications that the method presented in this paper is correct and reliable. It is very suitable for intelligent assembly system.

Proceedings Article | 9 October 2000 Paper

3D reconstruction approach in neurovision system

Yingen Xiong, Haifeng Hu

Proceedings Volume 4221, (2000) https://doi.org/10.1117/12.402650

KEYWORDS: Neural networks, 3D modeling, Image processing, 3D image processing, Intelligence systems, Fourier transforms, Image filtering, Object recognition, Feature extraction, Neurons

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In this paper, a new 3D reconstruction approach for 3D object recognition in neuro-vision system is presented. First, a phase based stereo matching using Hopfield neural network approach is presented. The stereo matching problems are treated in frequency domain by using local phase, instead of matching feature or texture of images, the stereo matching process is performed by using local phases of left image and right image in stereo image pair. By using the windowed Fourier transform, the windowed Fourier phases can be calculated. Through the variable window Gabor filter, the local phases of image can also be obtained. The Hopfield neural network is adopted to implement the stereo matching process. A suitable architecture of neural network is established, so that the computation can be implemented efficiently in parallel. A suitable matching function is created by using the local phase property. The energy function for neural network is constructed with satisfying some necessary constraints. The stereo matching process then is carried to find the minimum energy corresponding to the solution of the problem. Second, a 3D object reconstruction neural network is constructed by using BP neural network. So the 3D configuration and shape can be reconstructed by this neural network. With multiple neural networks the 3D Reconstruction processes can be performed in parallel. The examples for both synthetic and real images are shown in the experiment, and good results are obtained.

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