Wireless video capsules can now carry out gastroenterological examinations. The images make it possible to analyze some diseases during postexamination, but the gastroenterologist could make a direct diagnosis if the video capsule integrated vision algorithms. The first step toward in situ diagnosis is the implementation of 3-D imaging techniques in the video capsule. By transmitting only the diagnosis instead of the images, the video capsule autonomy is increased. This paper focuses on the Cyclope project, an embedded active vision system that is able to provide 3-D and texture data in real time. The challenge is to realize this integrated sensor with constraints on size, consumption, and processing, which are inherent limitations of the video capsule. We present the hardware and software development of a wireless multispectral vision sensor which enables the transmission of the 3-D reconstruction of a scene in real time. An FPGA-based prototype has been designed to show the proof of concept. Experiments in the laboratory, in vitro, and in vivo on a pig have been performed to determine the performance of the 3-D vision system. A roadmap towardthe integrated system is set out.

International audienceIn this paper we focus on the recognition of threedimensional objects captured by an active stereo vision sensor. The study is related to our research project Cyclope, this embedded sensor based on active stereo-vision approach allows real time 3D objects reconstruction. Our medical application requires differentiation between hyperplastic and adenomatous polyps during 3D endoscopic imaging. The detection algorithm consists of SVM classifier trained on robust feature descriptors of a surfacic 3D point cloud extracted from the surface of studied object. We compared our feature extraction method with others. Experimental results were encouraging and show correct classification rate of approximately 97%. The work contains many techniques concerning image processing and system calibration and provides detailed statistics about the detection rate and the computing complexity

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