Learning of Exploratory Behaviors for Object Recognition Using Reinforcement Learning

Closed-Loop Object Recognition Using Reinforcement Learning

Current computer vision systems whose basic methodology is open-loop or filter type typically use image segmentation followed by object recognition algorithms. These systems are not robust for most real-world applications. In contrast, the system presented here achieves robust performance by using reinforcement learning to induce a mapping from input images to corresponding segmentation paramet...

Reinforcement Learning of Active Recognition Behaviors

We show how a concise representation of active recognition behavior– what observations to make to detect a given object– can be derived from hidden-state reinforcement learning techniques. These learning techniques can solve decision process tasks which include perceptual observations, defined formally as Partially Observable Markov Decision Processes (POMDP). We define recognition within a POM...

Local reinforcement learning for object recognition

Jing Peng and Bir Bhanu College of Engineering University of California, Riverside, CA 92521 fjp,[email protected] Abstract Current computer vision systems whose basic methodology is open-loop or lter type typically use image segmentation followed by object recognition algorithms. These systems are not robust for most realworld applications. In contrast, the system presented here achieves r...

Automatic Selection of Object Recognition Methods Using Reinforcement Learning

Selecting which algorithms should be used by a mobile robot computer vision system is a decision that is usually made a priori by the system developer, based on past experience and intuition, not systematically taking into account information that can be found in the images and in the visual process itself to learn which algorithm should be used, in execution time. This paper presents a method ...

Reinforcement Learning and Visual Object Recognition