AN INTELLIGENT LEARNING APPROACH TO L-GRAMMAR EXTRACTION FROM IMAGE SEQUENCES OF REAL PLANTS

作者：Qu Hongchun^*; Zhu Qingsheng; Guo Mingwei; Lu Zhonghua

来源：International Journal on Artificial Intelligence Tools, 2009, 18(6): 905-927.

DOI：10.1142/S0218213009000457

• Plant branching pattern
• L-grammar extraction
• unfoliaged plants
• machine learning
• image processing
• statistical model

In this paper, we propose an automatic analyzing and transforming approach to L-system grammar extraction from real plants. Instead of using manually designed rules and cumbersome parameters, our method establishes the relationship between L-system grammars and the iterative trend of botanical entities, which reflect the endogenous factors that caused the plant branching process. To realize this goal, we use a digital camera to take multiple images of unfoliaged (leafless) plants and capture the topological and geometrical data of plant entities using image processing methods. The data then stored into specific data structures. A Hidden Markov based statistical model is then employed to reveal the hidden relations of plant entities which have been classified into categories based on their statistical properties extracted by a classic EM algorithm, the hidden relations have been integrated into the target L-system as grammars. Results show that our method is capable of automatically generating L-grammars for a given unfoliaged plant no matter what branching type it is belongs to.

• 出版日期2009-12
• 单位重庆大学