TY - JOUR
T1 - Recognition of plants using a stochastic L-system model
AU - Samal, Ashok
AU - Peterson, Brian
AU - Holliday, David J.
N1 - Funding Information:
This work is supported in part by NSF Grant Nos. CDA-9022445 and USE-9152764.
Funding Information:
Brian Peterson received his BS from NW Missouri State University in 1996. This research was conducted during a summer program for undergraduate research in digital image processing and computer vision as a part of research experience for undergraduates, sponsored by NSF.
PY - 2002/1
Y1 - 2002/1
N2 - 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.
AB - 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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U2 - 10.1117/1.1426081
DO - 10.1117/1.1426081
M3 - Article
AN - SCOPUS:0036205427
SN - 1017-9909
VL - 11
SP - 50
EP - 58
JO - Journal of Electronic Imaging
JF - Journal of Electronic Imaging
IS - 1
ER -