Multiresolution statistical analysis of computer-generated fractal imagery

Jones, J. G.; Thomas, Renjith; Earwicker, P. G.; Addison, Stephen R.

doi:10.1016/1049-9652(91)90038-l

Cited by 10 publications

(1 citation statement)

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“…Fractals have already been used in computer vision to model and recognize natural shapes, [13][14][15][16][17][18] terrain modeling and target detection 19,20 and texture analysis. [21][22][23][24] However, the use of fractal models for recognition of single objects, such as plants and flowers, has been relatively unexplored.…”

Section: Previous Workmentioning

confidence: 99%

Recognition of plants using a stochastic L-system model

Samal

Peterson

Holliday

2002

J. Electron. Imaging

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Recognition of natural shapes like leaves, plants, and trees, has proven to be a challenging problem in computer vision. The members of a class of natural objects are not identical to each other. They are similar, have similar features, but are not exactly the same. Most existing techniques have not succeeded in effectively recognizing these objects. One of the main reasons is that the models used to represent them are inadequate themselves. In this research we use a fractal model, which has been very effective in modeling natural shapes, to represent and then guide the recognition of a class of natural objects, namely plants. Variation in plants is accommodated by using the stochastic L-systems. A learning system is then used to generate a decision tree that can be used for classification. Results show that the approach is successful for a large class of synthetic plants and provides the basis for further research into recognition of natural plants.

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