Recognition of plants using a stochastic L-system model

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

Obtaining L-systems Rules from Strings

This paper presents a proposal to solve the Inverse Problem of Lindenmayer in the deterministic and free-context L-system grammar class. The proposal of this paper is to show a methodology that can obtain an L-system rule from a string representing the development stage of any object. The strings used in the tests were obtained from known grammars. However, they are dealt with as of having an unknown origin to assure the impartiality of the methodology. The idea presented here consists in the regression of growth of the string analyzed by an algorithm built based on relations of growth obtained from string generated by known deterministic grammars. In the tests carried out, all the strings submitted to the proposed algorithm could be reverted to an L-system rule identical to the original rule used in the synthesis of the string. It is also interesting to observe that the obtaining of these rules occurred practically in real time with tested grammars.Este artigo apresenta uma proposta para solucionar o Problema Inverso de Lindenmayer nas classes de gramáticas L-systems livres de contexto e determinísticas. A abordagem desta proposta pretende mostrar uma metodologia que consegue obter uma regra L-system a partir de uma cadeia de caracteres representante do estágio de desenvolvimento de um objeto qualquer. As cadeias utilizadas nos testes são sintetizadas a partir de gramáticas conhecidas, porém são tratadas como de origem desconhecida para assegurar a imparcialidade da metodologia. A idéia aqui apresentada consiste na regressão do crescimento da cadeia analisada por um algoritmo construído com base nas relações de crescimento obtidas a partir de cadeias geradas por gramáticas determinísticas conhecidas. Nos testes realizados todas as cadeias sub-metidas no algoritmo puderam ser revertidas em uma regra L-system idêntica à regra original utilizada na síntese da cadeia. Também é interessante notar que a obtenção destas regras ocorreu praticamente em tem-po real para gramáticas testadas

Recognition of plants using a stochastic L-system model

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