Segmentation of bovine livestock images using GA and ASM in a two-step approach
In this work a system based on genetic algorithms (CA) and active shape models (ASM) is presented that, in a two-step approach, is able to accurately find a particular object within the image. The aim of the first stage is to approximately locate the object in order to serve as an initialization for the second. Following a method similar to that used by other authors, a model of the desired shape (cows in lateral position) is constructed using PDM, and later the search within the image is carried out based on instances of that model, and using genetic algorithm techniques, for which several objective functions are suggested. In the second stage we attempt to make a fine adjustment of our instance to the object in the image, starting from the result of the previous process, Consequently, we propose two different techniques compared afterwards: deformable contours (specifically active shape models) and a restricted genetic search. Finally, the system is tested over a database of 309 animal images, and results are presented.
segmentation bovine livestock images genetic algorithms active shape models objective functions point distribution models