An original correlation and data fusion based approach to detect a reap limit into a gray level image

Abstract

Real time vision systems in an outdoor environment involve an increase of the algorithm complexity in order to solve the application with an acceptable autonomy. In our application of help guidance for agricultural mobile machines, the vehicle has to follow the limit between a mowed and an unmowed natural zone. After the extraction of the image by a CCD camera, a low level algorithm computes some luminance and texture parameters for each elementary site. We define a correlation function for each parameter. The original feature of our work is a geometrical characterization of the correlation functions, as well as performs belief functions associated with each parameter. The uncertainty notion, expressed by the use of the theory of evidence allows the control of the reliability of the estimation. This approach is very important in outdoor environment where the system can be confronted in many situations.