Narrow Results

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.

Object recognition problems in computer vision are often based on single image data processing. In various applications this processing can be extended to a complete sequence of images, usually received passively. In contrast, we propose a method for active object recognition, where a camera is selectively moved around a considered object. Doing so, we aim at reliable classification results with a clearly reduced amount of necessary views by optimizing the camera movement for the access of new viewpoints (viewpoint selection). Therefore, the optimization criterion is the gain of class discriminative information when observing the appropriate next image.

We show how to apply an unsupervised reinforcement learning algorithm to that problem. Specifically, we focus on the modeling of continuous states, continuous actions and supporting rewards for an optimized recognition. We also present an algorithm for the sequential fusion of gathered image information and we combine all these components into a single framework.

The experimental evaluations are split into results for synthetic and real objects with one- or two-dimensional camera actions, respectively. This allows the systematic evaluation of the theoretical correctness as well as the practical applicability of the proposed method. Our experiments showed that the proposed combined viewpoint selection and viewpoint fusion approach is able to significantly improve the recognition rates compared to passive object recognition with randomly chosen views.

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.

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.

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.