Despite the recent successes in robotics, artificial intelligence and computer vision, a complete artificial agent necessarily must include active perception. A multitude of ideas and methods for how to accomplish this have already appeared in the past, their broader utility perhaps impeded by insufficient computational power or costly hardware. The history of these ideas, perhaps selective due to our perspectives, is presented with the goal of organizing the past literature and highlighting the seminal contributions. We argue that those contributions are as relevant today as they were decades ago and, with the state of modern computational tools, are poised to find new life in the robotic perception systems of the next decade.

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Revisiting active perception.

Revisiting Active Perception

Concept learning depends on data character. To discover how, some researchers have used theoretical analysis to relate the behavior of idealized learning algorithms to classes of concepts. Others have developed pragmatic measures that relate the behavior of empirical systems such as ID3 and PLS1 to the kinds of concepts encountered in practice. But before learning behavior can be predicted, concepts and data must be characterized. Data characteristics include their number, error, “size,” and so forth. Although potential characteristics are numerous, they are constrained by the way one views concepts. Viewing concepts as functions over instance space leads to geometric characteristics such as concept size (the proportion of positive instances) and concentration (not too many “peaks”). Experiments show that some of these characteristics drastically affect the accuracy of concept learning. Sometimes data characteristics interact in non-intuitive wayss for example, noisy data may degrade accuracy differently depending on the size of the concept. Compared with effects of some data characteristics, the choice of learning algorithm appears less important: performance accuracy is degraded only slightly when the splitting criterion is replaced with random selection. Analyzing such observations suggests directions for concept learning research.

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Empirical Learning as a Function of Concept Character

We have developed a prototype miniaturized active vision system whose sensor architecture is based on a logarithmically structured space-variant pixel geometry. A space-variant image's resolution changes across the image. Typically, the central part of the image has a very high resolution, and the resolution falls off gradually in the periphery. Our system integrates a miniature CCD-based camera, pan-tilt actuator, controller, general purpose processors and display. Due to the ability of space-variant sensors to cover large work-spaces, yet provide high acuity with an extremely small number of pixels, space-variant active vision system architectures provide the potential for radical reductions in system size and cost. We have realized this by creating an entire system that takes up less than a third of a cubic foot. In this thesis, we describe a prototype space-variant active vision system (Cortex-I) which performs such tasks as tracking moving objects and license plate reading, and functions as a video telephone.
We report on the design and construction of the camera (which is 8 x 8 x 8mm), its readout, and a fast mapping algorithm to convert the uniform image to a space-variant image. We introduce a new miniature pan-tilt actuator, the Spherical Pointing Motor (SPM), which is 4 x 5 x 6cm. The basic idea behind the SPM is to orient a permanent magnet to the magnetic field induced by three orthogonal coils by applying the appropriate ratio of currents to the coils. Finally, we present results of integrating the system with several applications. Potential application domains for systems of this type include vision systems for mobile robots and robot manipulators, traffic monitoring systems, security and surveillance, telerobotics, and consumer video communications.
The long-range goal of this project is to demonstrate that major new applications of robotics will become feasible when small low-cost machine vision systems can be mass-produced. This notion of "commodity robotics" is expected to parallel the impact of the personal computer, in the sense of opening up new application niches for what has until now been expensive and therefore limited technology.

A miniaturized space-variant active vision system: Cortex-I

The paper concerns the recognition of textured objects, such as stained wooden parts, using color images. Many existing color classification systems utilize histogram-based similarity measures to compare an observed image with models from a database. Although the performance of these systems depends heavily on proper quantization of the color space, most quantization methods are based on traditional clustering or thresholding operations. The authors describe a novel approach to color space quantization in which the intersection of meaningful representations results in a partition of the color space. The color descriptions are chosen adaptively, using a set of training images. The resulting partition serves as the domain for histograms of models and of observed images and information-theoretic similarity measures are used to perform recognition. The motivation for this system is to achieve high recognition accuracy in an industrial setting. Laboratory tests have demonstrated a high level of accuracy for this technique, even though the objects of interest exhibit large variations of texture and color.

Adaptive quantization of color space for recognition of finished wooden components

This paper describes an active vision system which employs two high-resolution cameras for image acquisition. The system is capable of automatically directing movements of the cameras so that camera positioning and image acquisition are tightly coupled with visual processing. The system was developed as a research tool and is largely based on off-the-shelf components. A central workstation controls imaging parameters, which include five degrees of freedom for camera positioning (tilt, pan, translation, and independent vergence) and six degrees of freedom for the control of two motorized lenses (focus, aperture, and zoom). This paper is primarily concerned with describing the hardware of the system, the imaging model, and the calibration method employed. A brief description of system software is also given.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

University of Illinois active vision system

According to Webster, cohesion is “the act or process of sticking together tightly.” Here the term represents the underlying forces that drive the formation of classes during inductive learning. This paper considers several numerical and conceptual induction algorithms, and compares their methods of cohesion. While these algorithms represent several different methods, they also exhibit some significant commonalities.



Selon le dictionnaire, coherence se definit comme ≪l'union etroite des divers elements d'un corps≫. Ici, le terme represente les forces sous-jacentes qui entrainent la formation de classes durant l'apprentissage inductif. Cet article examine plusieurs algorithmes d'induction numeriques et conceptuels et compare leurs methodes de coherence. Bien que ces algorithmes representent plusieurs methodes differentes, ils ont egalement certains points en commun dont l'importance n'est pas a negliger.

A. Lynn Abbott

Papers

University of Illinois active vision system

Cohesion methods in inductive learning