Abstract
In this paper, we present the development of an active object recognition system. Our system uses a mutual information framework in order to choose an optimal sensor configuration for recognizing an unknown object. System builds a conditional probability density functions database for some observed features over a discrete set of sensor configurations for a set of interesting objects. Using a sequential decision making process, our system determines an optimal action (sensor configuration) that augments discrimination between objects in our database. We iterate this procedure until a decision about the class of the unknown object can be made. Actions include pan, tilt and zoom values for an active camera. Features include the color patch mean over a region in our image. We have tested on a set composed of 8 different soda bottles and we have obtained a recognition rate of about 95 %. Sequential decision length was of 4 actions in the average for a decision to be made.
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Trujillo-Romero, F., Ayala-Ramírez, V., Marín-Hernández, A., Devy, M. (2004). Active Object Recognition Using Mutual Information. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_69
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DOI: https://doi.org/10.1007/978-3-540-24694-7_69
Publisher Name: Springer, Berlin, Heidelberg
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