A robotic arm automatically guidedto handle the camera in laparoscopic surgery is presented. The goal of this work is to generate the adequate camera control strategies to track the working scene during a surgical procedure. The system is based on the computer vision analysis of the laparoscopic image that allows to identi& either a scene's relevant point, or the surgical instruments.

We propose a probabilistic object classifier for outdoor scene analysis as a first step in solving the problem of scene context generation. The method begins with a top-down control, which uses the previously learned models (appearance and absolute location) to obtain an initial pixel-level classification. This information provides us the core of objects, which is used to acquire a more accurate object model. Therefore, their growing by specific active regions allows us to obtain an accurate recognition of known regions. Next, a stage of general segmentation provides the segmentation of unknown regions by a bottom-strategy. Finally, the last stage tries to perform a region fusion of known and unknown segmented objects. The result is both a segmentation of the image and a recognition of each segment as a given object class or as an unknown segmented object. Furthermore, experimental results are shown and evaluated to prove the validity of our proposal.

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