Ms. Huiying Cai Profile

Huiying Cai

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Publications (3)

Proceedings Article | 21 July 2017 Paper

Signature of position angles histograms for 3D object recognition

Rongrong Lu, Feng Zhu, Yingming Hao, Huiying Cai, Yinshuai Zhao

Proceedings Volume 10420, 1042045 (2017) https://doi.org/10.1117/12.2281542

KEYWORDS: Object recognition

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Proceedings Article | 25 October 2016 Paper

Shape matching under affine transformation using normalization and multi-scale area integral features

Huiying Cai, Feng Zhu, Yingming Hao, Rongrong Lu

Proceedings Volume 10157, 101571W (2016) https://doi.org/10.1117/12.2246696

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Proceedings Article | 24 November 2014 Paper

A new template matching method based on contour information

Huiying Cai, Feng Zhu, Qingxiao Wu, Sicong Li

Proceedings Volume 9301, 930109 (2014) https://doi.org/10.1117/12.2065931

KEYWORDS: Image processing, Machine vision, Computer vision technology, Process modeling, Pattern recognition, 3D vision, Object recognition, Hough transforms, Evolutionary algorithms, Neodymium

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Template matching is a significant approach in machine vision due to its effectiveness and robustness. However, most of the template matching methods are so time consuming that they can’t be used to many real time applications. The closed contour matching method is a popular kind of template matching methods. This paper presents a new closed contour template matching method which is suitable for two dimensional objects. Coarse-to-fine searching strategy is used to improve the matching efficiency and a partial computation elimination scheme is proposed to further speed up the searching process. The method consists of offline model construction and online matching. In the process of model construction, triples and distance image are obtained from the template image. A certain number of triples which are composed by three points are created from the contour information that is extracted from the template image. The rule to select the three points is that the template contour is divided equally into three parts by these points. The distance image is obtained here by distance transform. Each point on the distance image represents the nearest distance between current point and the points on the template contour. During the process of matching, triples of the searching image are created with the same rule as the triples of the model. Through the similarity that is invariant to rotation, translation and scaling between triangles, the triples corresponding to the triples of the model are found. Then we can obtain the initial RST (rotation, translation and scaling) parameters mapping the searching contour to the template contour. In order to speed up the searching process, the points on the searching contour are sampled to reduce the number of the triples. To verify the RST parameters, the searching contour is projected into the distance image, and the mean distance can be computed rapidly by simple operations of addition and multiplication. In the fine searching process, the initial RST parameters are discrete to obtain the final accurate pose of the object. Experimental results show that the proposed method is reasonable and efficient, and can be used in many real time applications.

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