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Your search keyword '"Dou, Hao"' showing total 12 results
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12 results on '"Dou, Hao"'

4. A method of using standard flat objects to calibrate the spatial position of the sensor

Author

Dou Hao, Fan Xinxin, Sheng Lin, Yunyong Cheng, and Wenguang Ye

Subjects
Computer science, Plane (geometry), System of measurement, Acoustics, Coordinate system, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, 021001 nanoscience & nanotechnology, 01 natural sciences, 010309 optics, Distortion, 0103 physical sciences, Calibration, Reflection (physics), Measurement uncertainty, Specular reflection, 0210 nano-technology, ComputingMethodologies_COMPUTERGRAPHICS
Abstract

Determining the relationship between coordinate systems during measurement is one of the key works to ensure the accuracy of the measurement system. According to the traditional calibration method of standard spherical surface, because of the problem of measuring angle of surface features such as spherical surface, and the interference of specular reflection is easy to occur, the measurement of spherical surface by structural light often cannot fully describe the calibration feature or produce errors. In this paper, a unified calibration method based on the standard plane is proposed. Because plane features are easier to obtain and easier to control the appearance of specular reflection interference, and the calibration of plane geometry features used in this paper can completely avoid the distortion caused by the pending calibration measurement system. Therefore, the method can fully meet the measurement accuracy requirements of the measurement system.

Published
2021

7. Maximal good step graph methods for reducing the generation of the state space

Author

Dou, Hao, Barkaoui, Kamel, Boucheneb, Hanifa, Jiang, Xiaoning, Wang, Shouguang, Dou, Hao, Barkaoui, Kamel, Boucheneb, Hanifa, Jiang, Xiaoning, and Wang, Shouguang

Abstract

This paper proposes an effective method based on the two main partial order techniques which are persistent sets and covering step graph techniques, to deal with the state explosion problem. First, we introduce a new definition of sound steps, the firing of which enables to extremely reduce the state space. Then, we propose a weaker sufficient condition about how to find the set of sound steps at each current marking. Next, we illustrate the relation between maximal sound steps and persistent sets, and propose a concept of good steps. Based on the maximal sound steps and good steps, a construction algorithm for generating a maximal good step graph (MGSG) of a Petri net (PN) is established. This algorithm first computes the maximal good step at each marking if there exists one, otherwise maximal sound steps are fired at the marking. Furthermore, we have proven that an MGSG can effectively preserve deadlocks of a Petri net. Finally, the change performance evaluation is made to demonstrate the superiority of our proposed method, compared with other related partial order techniques.

Published
2019

11. An Approach for Calculating Semantic Similarity between Words Using WordNet

Author

Dou Hao, Fengling He, Wanli Zuo, and Tao Peng

Subjects
Computer science, business.industry, Word processing, Similarity heuristic, WordNet, Semantics, computer.software_genre, Set (abstract data type), Semantic similarity, Similarity (network science), Artificial intelligence, business, computer, Word (computer architecture), Natural language processing
Abstract

This paper presents a new edge-counting based method using Word Net to compute the similarity. The method achieves a similarity that perfectly fits with human rating and effectively simulate the human tHought process that is people prefer to consider more differences when the semantic distance between two word is closer, and vice versa. At last, we weigh up our model against a benchmark set by human similarity judgment, and obtain a much improved result compared with other methods.

Published
2011

12. Object-Based Visual Saliency via Laplacian Regularized Kernel Regression.

Author

Dou, Hao, Ming, Delie, Yang, Zhi, Pan, Zhihong, Li, Yansheng, and Tian, Jinwen

Abstract

Saliency object detection has been a very active research topic recently, due to its extensive applications in image compression, scene understanding, image retrieval, and so forth. The overwhelming majority of existing computational models are designed based on computer vision techniques by using a lot of image cues and priors. In fact, salient object detection is derived from the biological perceptual mechanism, and biological evidence shows that the object-based saliency stems from the spread of the spatial attention. Inspired by this, we attempt to utilize the emerging spread mechanism of object attention to construct a new computational model. A novel Laplacian regularized kernel regression diffusion model is proposed to fulfill the spread process. The proposed diffusion model, which is able to fully capture both global and local structures of the image, thereby allows for effective propagation of spatial attention with visual grouping cues, yielding a well-structured object-based saliency map. Experimental results demonstrate that our method can achieve encouraging performance in comparison with the state-of-the-art methods. [ABSTRACT FROM PUBLISHER]

Published
2017
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