An improved region-growth algorithm for dense matching

• Autorzy: Xing Y. J. , Meng J. J. , Sun J. , Wang Z. Q.
• Języki publikacji: EN

Abstrakty

EN

Purpose: Improve the accuracy and speed of the region-growth algorithm between two 2D images. Design/methodology/approach: The algorithm includes two parts: the selection of seeds points and propagation. Some improvements are made in each one. For the first part, the best-first strategy is used to assure the accuracy of seeds. The epipolar line constraint and continuity constraint reduce the double phase matching course into single phase matching. For the second one, a dynamic and adaptive window is adopted instead of the large window. Findings: In the first section, the process of searching and the computational duties are decreased in large extent. And in the second one, the adaptive window makes the searching course more efficient in time and space. It is really difficult to get the most suitable window to search for the points as soon as possible. If it can be easily got, it will advance the efficiency of search. It is the future work. Practical implications: The method can be used in many different images, such as the structural images and the facial images. Originality/value: The original value is the region-growth algorithm, and in this paper I made some betterments to advance the efficiency and accuracy.

Słowa kluczowe

EN

artificial intelligence method; region-growth; seed point; single matching phase

PL

metody sztucznej inteligencji; rozwój regionu; dopasowanie fazowe

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2006
• Tom: Vol. 18, nr 1-2
• Strony: 323--326
• Opis fizyczny: Bibliogr. 6 poz., rys., wykr.

Twórcy

autor

Xing Y. J.

• Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, 116024 P.R. China

autor

Meng J. J.

• Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, 116024 P.R. China

autor

Sun J.

• Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, 116024 P.R. China

autor

Wang Z. Q.

• Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, 116024 P.R. China

Bibliografia

• [1] M. Lhuillier and L. Quan, Quasi-Dense Reconstruction from Image Sequence, Proc. Seventh European Conf. Computer Vision 2 (2002) 125-139.
• [2] Li Tang, Chengke Wu, Zezhi Chen, Image dense matching based on region growth with adaptive window, Patten Recognition Letters 23(2002) 1169-1178.
• [3] C. Schmid, R. Mohr and C. Bauckhage. Comparing and Evaluating Interest Points. In ICCV98, 1998.
• [4] Luigi Di Stefano, Massimiliano Marchionni, Stefano Mattoccia, A fast area-based stereo matching algorithm, Image and Vision Computing 22(2004) 983-1005.
• [5] P. Fua, Combining stereo and monocular information to compute dense depth maps that preserve depth discontinuities, Proceedings of 12th International Joint Conference on Artificial Intelligence, 1991, pp. 1292-1298.
• [6] Yunde Zhong, Huaifeng Zhang, Control Points Based Semi-Dense Matching, ACCV2002:The 5th Asian Conference on Computer Vision, Melbourne Australia, 23-25 January 2002.