In this paper, an active vision system is developed which is based on image strategy. The image based control structure uses the optical flow algorithm for motion detection of an object in a visual scene. Because the optical flow is very sensitive to changes in illumination or to the quality of the video, it was necessary to use median filtering and erosion and dilatation morphological operations for the decrease of erroneous blobs residing in individual frames. Since the image coordinates of the object are subjected to noise, the Kalman filtering technique is adopted for robust estimation. A fuzzy controller based on the fuzzy condensed algorithm allows real time work for each captured frame. Finally, the proposed active vision system has been simulated in the development/simulation environment Matlab/Simulink.

One of the series in Machine Perception and Artificial Intelligence, this book covers subjects including the Harvard binocular head; heads, eyes, and head-eye systems; a binocular robot head with torsional eye movements; and escape and dodging behaviours for reactive control.

Intelligent robotics has become the focus of extensive research activity. This effort has been motivated by the wide variety of applications that can benefit from the developments. These applications often involve mobile robots, multiple robots working and interacting in the same work area, and operations in hazardous environments like nuclear power plants. Applications in the consumer and service sectors are also attracting interest. These applications have highlighted the importance of performance, safety, reliability, and fault tolerance. This volume is a selection of papers from a NATO Advanced Study Institute held in July 1989 with a focus on active perception and robot vision. The papers deal with such issues as motion understanding, 3-D data analysis, error minimization, object and environment modeling, object detection and recognition, parallel and real-time vision, and data fusion. The paradigm underlying the papers is that robotic systems require repeated and hierarchical application of the perception-planning-action cycle. The primary focus of the papers is the perception part of the cycle. Issues related to complete implementations are also discussed.

Visual perception is one of the most important sources of information for both humans and robots. A particular challenge is the acquisition and interpretation of complex unstructured scenes. This work contributes to active vision for humanoid robots. A semantic model of the scene is created, which is extended by successively changing the robot's view in order to explore interaction possibilities of the scene.

Robotic systems consist of object or scene recognition, vision-based motion control, vision-based mapping, and dense range sensing, and are used for identification and navigation. As these computer vision and robotic connections continue to develop, the benefits of vision technology including savings, improved quality, reliability, safety, and productivity are revealed. Robotic Vision: Technologies for Machine Learning and Vision Applications is a comprehensive collection which highlights a solid framework for understanding existing work and planning future research. This book includes current research on the fields of robotics, machine vision, image processing and pattern recognition that is important to applying machine vision methods in the real world.

This unique book explores the important issues in studying for active visual perception. The book’s eleven chapters draw on recent important work in robot vision over ten years, particularly in the use of new concepts. Implementation examples are provided with theoretical methods for testing in a real robot system. With these optimal sensor planning strategies, this book will give the robot vision system the adaptability needed in many practical applications.

In 1991, Ballard described the implications of having a visual system that could actively position the camera coordinates in response to physical stimuli. In humanoid robotic systems, or in any animate vision system that interacts with people, social dynamics provide additional levels of constraint and provide additional opportunities for processing economy. In this paper, we describe an integrated visual-motor system that has been implemented on a humanoid robot to negotiate the robot's physical constraints, the perceptual needs of the robot's behavioral and motivational systems, and the social implications of motor acts.