Dr. Wang Pan Profile

Dr. Wang Pan

at Shenyang Institute of Automation

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Proceedings Article | 12 December 2018 Paper

A novel ellipse detection method for real-time applications

Limin Zhang, Feng Zhu, Yingming Hao, Wang Pan

Proceedings Volume 10846, 1084626 (2018) https://doi.org/10.1117/12.2505381

KEYWORDS: Detection and tracking algorithms, Distance measurement, Machine vision, Computer vision technology, Digital image processing

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The detection of ellipses in digital image data is an important task in vision-based systems, since elliptical shapes are very common in nature and in man-made objects. Ellipse detection in real images is technically a very challenging problem in detection effectiveness and execution time. We propose an improved ellipse detection method for real-time performance on real world images. We extract arcs from the edge mask and classify them in four classes according to edge direction and convexity. By developing arc selection strategy, we select a combination of arcs possibly belonging to the same ellipse, and then estimate its parameters via the least squares fitting technique. Candidate ellipses are validated according to the fitness of the estimation with the actual edge pixels. Our method has been tested on three real images datasets and compared with two state-of-the-art methods. Our method performs superior than the compared methods. The results also show that the proposed method is suitable for real-time applications.

Proceedings Article | 19 October 2016 Paper

Combining depth and gray images for fast 3D object recognition

Wang Pan, Feng Zhu, Yingming Hao

Proceedings Volume 10155, 101553C (2016) https://doi.org/10.1117/12.2247370

KEYWORDS: 3D image processing, Object recognition, Machine vision, Robotics

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Reliable and stable visual perception systems are needed for humanoid robotic assistants to perform complex grasping and manipulation tasks. The recognition of the object and its precise 6D pose are required. This paper addresses the challenge of detecting and positioning a textureless known object, by estimating its complete 6D pose in cluttered scenes. A 3D perception system is proposed in this paper, which can robustly recognize CAD models in cluttered scenes for the purpose of grasping with a mobile manipulator. Our approach uses a powerful combination of two different camera technologies, Time-Of-Flight (TOF) and RGB, to segment the scene and extract objects. Combining the depth image and gray image to recognize instances of a 3D object in the world and estimate their 3D poses. The full pose estimation process is based on depth images segmentation and an efficient shape-based matching. At first, the depth image is used to separate the supporting plane of objects from the cluttered background. Thus, cluttered backgrounds are circumvented and the search space is extremely reduced. And a hierarchical model based on the geometry information of a priori CAD model of the object is generated in the offline stage. Then using the hierarchical model we perform a shape-based matching in 2D gray images. Finally, we validate the proposed method in a number of experiments. The results show that utilizing depth and gray images together can reach the demand of a time-critical application and reduce the error rate of object recognition significantly.

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