Recognizing Plants using Stochastic L-Systems

Recognizing naturally occurring objects has been a difficult task in computer vision. One of the keys to recognizing objects is the development of a suitable model. One type of model, the frattal, has been used successfully to model complex Fractals have also been used in computer vision, although not as widely, to model and recognize natural scenes [5, 6, 71. However, the use of fractal models for recognition of single osjects, such as plants and flowers, has been relativcly unexplored. natural objects. A class of fractals, the L-system, has not only been used to model natural plants, but has also aided in their recognition[l]. This research extends the work in plant recognition using L-systems in two ways. Stochastic L-systems are used to model and generate more realistic plants. Furthermore, to handle the complexity of recognition, a learning system is used that automatically generates a decision tree for classification. Results indicate that the approach used here has great potential as a method for recognition of natural objects.