Biological vision systems explore their environment via allocating their visual resources to only the interesting parts of a scene. This is achieved by a selective attention mechanism that controls eye movements. The data thus generated is a sequence of subimages of different locations and thus a sequence of features extracted from those images - referred to as attentional sequence. In higher level visual processing leading to scene cognition, it is hypothesized that the information contained in attentional sequences are combined and utilized by special mechanisms - although still poorly understood. However, developing models of such mechanisms prove out to be crucial - if we are to understand and mimic this behavior in robotic systems. In this paper, we consider the recognition problem and present two approaches to using attentional sequences for recognition: Markovian and evidential reasoning. Experimental results with our mobile robot APES reveal that simple shapes can be modeled and recognized by these methods - using as few as ten fixations and very simple features. For more complex scenes, longer attentional sequences or more sophisticated features may be required for cognition.

Robot vision systems inspired by human vision are required to employ mechanisms similar to those which are known to play key roles in human visual performance. Some of the more crucial properties are: 1) ability to actively select and filter visual input by changing its focus of attention both mechanically and mentally; 2) a retina model which will reduce computational complexity like in the human eye; 3) levels of representation; 4) serial processing of incoming stream of visual data; and 5) memory mechanisms that will keep track of eye movements and store functional models of the environment. APES is a mobile robot developed in our laboratory to meet these requirements simultaneously and to act as a test-bed for biologically motivated robot vision research. In this paper we describe APES and the original approaches taken to realize the above properties of biological vision.

Selectively perceiving systems explore their environment by fixating on and analyzing potentially interesting parts of a scene in a continual manner. During this process, a sequence of snapshots, each containing visual data with distinct spatio-temporal properties, is collected. We propose a visual memory model that enables the integration of this spatio-temporal data. This memory model, referred to as the bubble model, uses a spherical surface attached to the viewpoint in order to store and manipulate information collected during selective perception process. The bubble surface can be deformed by a number of control points to represent visual information obtained from different fixation points. Bubbles are indexed by the viewpoint vectors so that they can be recalled or updated as necessary. Different bubbles attached to the same viewpoint can store multiple simple and complex features. Using time stamps and parametric surface representations the model can also be used to extract changes and similarities in the environment.

The ability to combine vision and behaviour is vital for a robot to achieve real time performance on a variety of tasks in a complex and dynamic environment. The design and development of such systems have been motivated by physiological and psychological studies on biological systems. These studies indicate that perception systems must have focusing, oculomotion and multi-level processing capabilities. This paper describes a mobile robot having a biologically motivated vision system. The robot-APES-is designed to have both physical and mental attention capabilities, end selective perceptual processing thus achieved can result in real-time behavior in simple tasks involving recognition.

We extend a method presented previously, which considers the problem of the semicausal autoregressive (AR) parameter identification for images degraded by observation noise only. We propose a new approach to identify both the causal and semicausal AR parameters and blur parameters without a priori knowledge of the observation noise power and the PSF of the degradation. We decompose the image into 1-D independent complex scalar subsystems resulting from the vector state-space model by using the unitary discrete Fourier transform (DFT). Then, by applying the expectation-maximization (EM) algorithm to each subsystem, we identify the AR model and blur parameters of the transformed image. The AR parameters of the original image are then identified by using the least squares (LS) method. The restored image is obtained as a byproduct of the EM algorithm.

The method presented by T. Katayama and T. Hirai (1990), who considered the problem of semicausal autoregressive (AR) parameter identification for images degraded by observation noise, is extended. In particular, an approach to identifying both the causal and semicausal AR parameters without a priori knowledge of the observation noise power is proposed. The image is decomposed into 1-D independent complex scalar subsystems resulting from the vector state-space model, using the unitary discrete Fourier transform (DFT). Then the expectation-maximization algorithm is applied to each subsystem to identify the AR parameters of the transformed image. The AR parameters of the original image are then identified using the least-square method. The restored image is obtained as a byproduct of the EM algorithm.

The performance of the maximum likelihood spectral estimate is compared vis-à-vis the classical Bartlett spectrum. Special cases such as single tone harmonic analysis and asymptotic behaviour have been considered.