Relative depth for behavior based recognition

The authors propose to study object recognition by considering it in the context of an agent performing it in an environment, where the agent´s intentions translate into a set of behaviors. The problem becomes a problem of action from intensity functions. In accomplishing a behavior, the next step of action from the images is determined. Acquiring the information for action is a solution for a recognition task. The recognition task is agent and behavior dependent and can use the output of different visual modules. The implementation of one visual module and its use for purposive recognition are described. It is shown how to robustly extract relative depth from a stereo setup without correspondence and calibration, and how this visual module can be used under some intentions and behaviors. For recognition under navigation behavior, relative depth was used to recognize obstacles by isolating unexpected objects in close range. For grasping behavior relative depth was used to recognize the different stages in the process