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The historical development of the physiology and psychology of perception and the technical area of “computer vision” periodically overlap, and both areas are mutually enriched by conceptual and model representations. In particular, retina-like sensors appeared in computer vision, which, as it turned out, not only model the features of the structural organization of the organs of vision but are also effective for the technical description and recognition of fragments of visual scenes. On the other hand, models created as a result of the generalization and systematization of physiological and psychological knowledge, receive technical validation are implemented as computer vision algorithms. One of the concepts — active perception, which appeared in psychology, was effectively used in technical vision. Within the framework of this concept, perception is treated not as a passive act, conditioned by reaction to events in the environment, but as an active behavior initiated by the subject of perception, conditioned by internal motivation, and affected by the situational context. The use of such a concept in technical vision gives an effective, biologically-inspired solution to the issue of interpreting ambiguous sensory information when such ambiguity is reduced due to the actions of the observer. The implementation of the paradigm of active perception from a technical point of view leads to a certain structural organization of sensor models, as well as ways of processing and aggregating visual information. In this chapter, we consider models of active vision, models of visual sensors, algorithms for the lower and upper levels of visual information processing, as well as application of active vision models for various technical problems.

Visual inspection plays an important role in computer aided and integrated manufacturing. A good inspection planning reduces the effort and improves the accuracy in inspection. In this chapter, we discuss the representation of three-dimensional parts using object-oriented representation and viewer-centered representation. Such representations support the planning of active visual inspection. In order to maximize the accuracy of active visual inspection, the quantization errors and displacement of active vision sensors are analyzed. The derivation of the probabilistic analysis is presented. Applications of such analysis are discussed.

This paper is aimed at 3D object understanding from 2D images, including articulated objects in active vision environment, using interactive, and internet virtual reality techniques. Generally speaking, an articulated object can be divided into two portions: main rigid portion and articulated portion. It is more complicated that “rigid” object in that the relative positions, shapes or angles between the main portion and the articulated portion have essentially infinite variations, in addition to the infinite variations of each individual rigid portions due to orientations, rotations and topological transformations. A new method generalized from linear combination is employed to investigate such problems. It uses very few learning samples, and can describe, understand, and recognize 3D articulated objects while the objects status is being changed in an active vision environment.

Much research is currently going on about the processing of one or two-camera imagery, possibly combined with other sensors and actuators , in view of achieving attentive vision, i.e. processing selectively some parts of a scene possibly with another resolution. Attentive vision in turn is an element of active vision where the outcome of the image processing triggers changes in the image acquisition geometry and/or of the environment. Almost all this research is assuming classical imaging, scanning and conversion geometries, such as raster based scanning and processing of several digitized outputs on separate image processing units.

A consortium of industrial companies comprising Digital Equipment Europe, Thomson CSF, and a few others, have taken a more radical view of this. To meet active vision requirements in industry, an intelligent camera is being designed and built, comprised of three basic elements:

– a unique Thomson CSF CCD sensor architecture with random addressing

– the DEC Alpha 21064 275MHz processor chip, sharing the same internal data bus as the digital sensor output

– a generic library of basic image manipulation , control and image processing functions, executed right in the sensor-internal bus-processor unit , so that only higher level results or commands get exchanged with the processing environment.

Extensions to color imaging (with lower spatial resolution), and to stereo imaging, are relatively straightforward. The basic sensor is 1024*1024 pixels with 2*10 bits addresses, and a 2. 5 ms (400 frames/second) image data rate compatible with the Alpha bus and 64 bits addressing. For attentive vision, several connex fields of max 40 000 pixels, min 5*3 pixels, can be read and addressed within each 2 .5 ms image frame. There is nondestructive readout, and the image processing addressing over 64 bits shall allow for 8 full pixel readouts in one single word.

The main difficulties have been identified as the access and reading delays, the signal levels, and dimensioning of some buffer arrays in the processor.

The commercial applications targeted initially will be in industrial inspection, traffic control and document imaging. In all of these fields, selective position dependent processing shall take place, followed by feature dependent processing.

Very large savings are expected both in terms of solutions costs to the end users, development time, as well as major performance gains for the ultimate processes. The reader will appreciate that at this stage no further implementation details can be given.

The design of a robot head for active computer vision tasks is described. The stereo head/eye platform uses a common elevation configuration and has four degree-of-freedom. The joints are driven by DC servo motors coupled with incremental optical encoders and backlash minimizing gearboxes. The details of mechanical design, head controller design, the architecture of the system, and the design criteria for various specifications are presented.

Recent related approaches in the areas of vision, motor control and planning are attempting to reduce the computational requirements of each process by restricting the class of problems that can be addressed. Active vision, differential kinematics and reactive planning are all characterized by their minimal use of representations, which simplifies both the required computations and the acquisition of models. This paper describes an approach to visually-guided motor control that is based on active vision and differential kinematics, and is compatible with reactive planning. Active vision depends on an ability to choose a region of the visual environment for task-specific processing. Visual attention provides a mechanism for choosing the region to be processed in a task-specific way. In addition, this attentional mechanism provides the interface between the vision and motor systems by representing visual position information in a 3-D retinocentric coordinate frame. Coordinates in this frame are transformed into eye and arm motor coordinates using kinematic relations expressed differentially. A real-time implementation of these visuomotor mechanisms has been used to develop a number of visually-guided eye and arm movement behaviors.

A vision system was built using a behavior-based model, the subsumption architecture. The so-called active eye moves the camera's axis through the environment, detecting areas with high concentration of edges, with the help of a kind of saccadic movement. The design and implementation process is detailed in the article, paying particular attention to the fovea-like sensor structure which enables the active eye to efficiently use local information to control its movements. Numerical measures for the eye's behavior were developed, and applied to evaluate the incremental building process and the effects of the saccadic movements on the whole system. A higher level behavior was also implemented, with the purpose of detecting long straight edges in the image, producing pictures similar to hand drawings. Robustness and efficiency problems are addressed at the end of the paper. The results seem to prove that interesting behaviors can be achieved using simple vision methods and algorithms, if their results are properly interconnected and timed.

Active vision systems can be considered as systems that integrate visual sensing and action. Sensing includes detection of actions/events and results also in specific actions/manipulations.

This paper mainly addresses the basic issues in the design of a head-eye system for the study of active-purposive vision. The design complexity of such a head is defined by the activeness of the visual system. Although we have not had the motivation to exactly reproduce the biological solutions in a robot, we claim that the designer should carefully consider the solutions offered by evolution.

The flexibility of the behavioral pattern of the system is constrained by the mechanical structure and the computational architecture used in the control system of the head. The purpose of the paper is to describe the mechanical structure as well as the computational architecture of the KTH-head from this perspective.

We present the design of a controllable stereo vision head. TRISH (The Toronto IRIS Stereo Head) is a binocular camera mount, consisting of two fixed focal length color cameras with automatic gain control forming a verging stereo pair. TRISH is capable of version (rotation of the eyes about the vertical axis so as to maintain a constant disparity), vergence (rotation of each eye about the vertical axis so as to change the disparity), pan (rotation of the entire head about the vertical axis), and tilt (rotation of each eye about the horizontal axis). One novel characteristic of the design is that each camera can rotate about its own optical axis (torsion). Torsional movement makes it possible to minimize the vertical component of the two-dimensional search which is associated with stereo processing in verging stereo systems.

Active vision involving the exploitation of controllable cameras and camera heads is an area which has received increased attention over the last few years. At LIA/AUC a binocular robot camera head has been constructed for use in geometric modelling and interpretation. In this manuscript the basic design of the head is outlined and a first prototype is described in some detail. Detailed specifications for the components used are provided together with a section on lessons learned from construction and initial use of this prototype.

This paper presents the design and development of a real-time eye-in-hand stereovision system to aid robot guidance in a manufacturing environment. The stereo vision head comprises a novel camera arrangement with servo-vergence, focus, and aperture that continuously provides high-quality images to a dedicated image processing system and parallel processing array. The stereo head has four degrees of freedom but it relies on the robot end-effector for all remaining movement. This provides the robot with exploratory sensing abilities allowing it to undertake a wider variety of less constrained tasks. Unlike other stereo vision research heads, the overriding factor in the Surrey head has been a truly integrated engineering approach in an attempt to solve an extremely complex problem. The head is low cost, low weight, employs state-of-the-art motor technology, is highly controllable and occupies a small-sized envelope. Its intended applications include high-accuracy metrology, 3-D path following, object recognition and tracking, parts manipulation and component inspection for the manufacturing industry.

This paper describes a layered control system for a binocular stereo head. It begins by a discussion of the principles of layered control. It then describes the mechanical configuration for a binocular camera head with six degrees of freedom. A device level controller is presented which permits an active vision system to command the position of a binocular gaze point in the scene. The final section describes the design of perceptual actions which exploit this device level controller.