Hui-jun Yang
College of Mechanical and Electronic Engineering, Northwest A and F University, Yang ling, Shaan xi, China
Dong-jian He
College of Mechanical and Electronic Engineering, Northwest A and F University, Yang ling, Shaan xi, China
Shao-Hua Jiang
Institute of Image Recognition and Computer Vision, Hunan Normal University, Changsha, Hunan, China
Hua Wang
College of Infortion Engineering, Northwest A and F University, Yang ling, Shaan xi, China
ABSTRACT
Expensive computation, low efficiency and inaccuracy remain a long-standing challenge in extraction of fruit shape from massive background points. Towards these objectives, an improved K-neighborhood adaptive subdivision algorithm is proposed to simplify point clouds for reduction of calculation; an MLS-based local surface fitting is employed for more accurate geometric attributes and a Geometric Similarity-based Augmenter (GSA) recursive process is used for rapid extraction of apple shape. As a result, the optimal subdivision parameters help to reduce the number of apple cells to 3.0% of the original points, a 25% down than the K (4%) neighborhood, the GSA extraction algorithm reduce the number of extracted pear cells by 49.5%.
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How to cite this article
Hui-jun Yang, Dong-jian He, Shao-Hua Jiang and Hua Wang, 2013. Research on Siplification and Extraction Technology of Apple Point Cloud. Journal of Applied Sciences, 13: 3684-3690.
DOI: 10.3923/jas.2013.3684.3690
URL: https://scialert.net/abstract/?doi=jas.2013.3684.3690
DOI: 10.3923/jas.2013.3684.3690
URL: https://scialert.net/abstract/?doi=jas.2013.3684.3690
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