Active Vision

This comprehensive reference provides easy access to relevant information on all aspects of Computer Vision. An A-Z format of over 240 entries offers a diverse range of topics for those seeking entry into any aspect within the broad field of Computer Vision. Over 200 Authors from both industry and academia contributed to this volume.

Computer vision entails both passive and active illumination techniques. Whereas passive techniques observe the scene statically and analyse it as is, by contrast active techniques give the scene some actions and try to facilitate the analysis. In particular, active illumination techniques project specific light, for which the characteristics are known beforehand, to a target scene to enable stable and accurate analysis of the scene.

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This work describes a multi-sensor system for robotic object recogni- tion tasks that integrates passive stereo vision and active, exploratory tactile sensing. The complementary nature of these sensing modalities allows the system to discover the underlying 3-D structure of the objects to be recog- nized. This structure is embodied in rich, hierarchical, viewpoint- independent 3-D models of the objects which include curved surfaces, con- cavities and holes. The vision processing provides sparse 3-D data about regions of interest that are then actively explored by the tactile sensor which is mounted on the end of a robotic manipulator. A robust hierarchical pro- cedure has been developed to integrate the visual and tactile data into accu- rate 3-D surface and feature primitives. This integration of vision and touch provides geometric measures of the surfaces and features that are used in a matching phase to find model objects that are consistent with the sensory data. Methods for verification of the hypothesis are presented, including the sensing of visually occluded areas with the tactile sensor. A number of experiments have been performed using real sensors and real, noisy data to demonstrate the utility of these methods and the ability of such a system to recognize objects that would be difficult for a system using vision alone.

This work describes a multi-sensor system for robotic object recogni- tion tasks that integrates passive stereo vision and active, exploratory tactile sensing. The complementary nature of these sensing modalities allows the system to discover the underlying 3-D structure of the objects to be recog- nized. This structure is embodied in rich, hierarchical, viewpoint- independent 3-D models of the objects which include curved surfaces, con- cavities and holes. The vision processing provides sparse 3-D data about regions of interest that are then actively explored by the tactile sensor which is mounted on the end of a robotic manipulator. A robust hierarchical pro- cedure has been developed to integrate the visual and tactile data into accu- rate 3-D surface and feature primitives. This integration of vision and touch provides geometric measures of the surfaces and features that are used in a matching phase to find model objects that are consistent with the sensory data. Methods for verification of the hypothesis are presented, including the sensing of visually occluded areas with the tactile sensor. A number of experiments have been performed using real sensors and real, noisy data to demonstrate the utility of these methods and the ability of such a system to recognize objects that would be difficult for a system using vision alone.

Digital image processing and machine vision have grown considerably during the last few decades. Of the various techniques, developed so far splines play a positive and significant role in many of them. Strong mathematical theory and ease of implementations is one of the keys of their success in many research issues.