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Active Sensor Planning for Multiview Vision Tasks by Shengyong Chen, Y. F. Li, Jianwei Zhang, Wanliang Wang

By Shengyong Chen, Y. F. Li, Jianwei Zhang, Wanliang Wang

This special e-book explores the real concerns in learning for energetic visible belief. The book’s 11 chapters draw on contemporary very important paintings in robotic imaginative and prescient over ten years, fairly within the use of recent options. Implementation examples are supplied with theoretical equipment for checking out in a true robotic process. With those optimum sensor making plans thoughts, this publication will supply the robotic imaginative and prescient procedure the adaptability wanted in lots of sensible purposes.

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7. Stereo pose determination for dimensional measurement (Rivera-Rios 2005) The general automatic sensor planning system (GASP) reported by Trucco et al. (1997) is to compute optimal positions for inspection tasks using a known imaging sensor and feature-based object models. This exploits a feature inspection representation (FIR) which outputs an explicit solution off-line for the sensor position problem. A generalized sensor (GS) model was defined with both the physical sensor and the particular software module.

G. a photometric stereo. It is represented as a sensor composition tree (SC tree), as in Fig. 2. Finally, the appearance of object surfaces is predicted by applying the SC tree to the object and is followed by the action of sensor planning. 3 Fundamentals of Sensor Modeling and Planning 45 Fig. 2. The photometric stereo sensor and its SC tree (Ikeuchi and Robert 1991) In some other typical works on constraint formulation, Remagnino et al. (1995) proposed to set the viewpoint, look direction, and focal length of a camera.

Borotschnig et al. (2000) also presented an active vision system for recognizing objects which are ambiguous from certain viewpoints. The system repositions the camera to capture additional views and uses probabilistic object classifications to perform view planning. Multiple observations lead to a significant increase in recognition rate. The view planning consists in attributing a score to each possible movement of the camera. The movement obtaining the highest score will be selected next (Fig.

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