Mr. Anbo Jiao Profile

Anbo Jiao

at Shenyang Institute of Automation

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Proceedings Article | 12 December 2018 Paper

Sub-blocks segmentation based on multi-feature fusion

Hongyu Chen, Haibo Luo, Zheng Chang, Bin Hui, Anbo Jiao

Proceedings Volume 10846, 108460Q (2018) https://doi.org/10.1117/12.2503909

KEYWORDS: Image segmentation, Detection and tracking algorithms, Visualization, Volume rendering, Visual process modeling, Target detection, Computer vision technology, Evolutionary algorithms

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Target tracking is one of the most topic-active research and also the most important part in the field of computer vision. The typical deformable model target tracking algorithm decomposes each target into multi-sub-blocks, and computes the similarity of both the local areas of each target and the spatial location among each sub-block. However, these algorithms define the area and the number of sub-blocks manually. In the practical application, the tracking system can provide the interaction to select the tracking target real-timely. But it’s difficult to provide the interaction to select the sub-blocks. It means the selection of sub-blocks manually has limitation in the practical application. Aimed at the problems mentioned, this paper presents a method for automatic sub-blocks segmentation. The proposed method integrates the local contrast and the richness of texture details to get a measure function of sub-blocks. Saliency detection based on visual attention model was used to extract salient local contrast. The edge direction dispersion has been used to describe the richness of texture details. Then, the discrimination of each pixel in the target will be computed by the mentioned methods above. Finally, sub-blocks with high discrimination will be chosen for tracking. Experimental results show that the method proposed can achieve more tracking precision compared with the current deformable target tracking algorithm which selected the sub-blocks manually

Proceedings Article | 24 November 2014 Paper

A method of plane geometry primitive presentation

Anbo Jiao, Haibo Luo, Zheng Chang, Bin Hui

Proceedings Volume 9301, 930131 (2014) https://doi.org/10.1117/12.2073114

KEYWORDS: Image segmentation, Sensors, Optoelectronics, Machine vision, Computer vision technology, Object recognition, Remote sensing, Data processing, Image understanding, Feature extraction

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Point feature and line feature are basic elements in object feature sets, and they play an important role in object matching and recognition. On one hand, point feature is sensitive to noise; on the other hand, there are usually a huge number of point features in an image, which makes it complex for matching. Line feature includes straight line segment and curve. One difficulty in straight line segment matching is the uncertainty of endpoint location, the other is straight line segment fracture problem or short straight line segments joined to form long straight line segment. While for the curve, in addition to the above problems, there is another difficulty in how to quantitatively describe the shape difference between curves. Due to the problems of point feature and line feature, the robustness and accuracy of target description will be affected; in this case, a method of plane geometry primitive presentation is proposed to describe the significant structure of an object. Firstly, two types of primitives are constructed, they are intersecting line primitive and blob primitive. Secondly, a line segment detector (LSD) is applied to detect line segment, and then intersecting line primitive is extracted. Finally, robustness and accuracy of the plane geometry primitive presentation method is studied. This method has a good ability to obtain structural information of the object, even if there is rotation or scale change of the object in the image. Experimental results verify the robustness and accuracy of this method.

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