Abstract not found

An important approach in visual neuroscience considers how the function of the early visual system relates to the statistics of its natural input. Previous studies have shown how many basic properties of the primary visual cortex, such as the receptive fields of simple and complex cells and the spatial organization (topography) of the cells, can be understood as efficient coding of natural images. Here we extend the framework by considering how the responses of complex cells could be sparsely represented by a higher-order neural layer. This leads to contour coding and end-stopped receptive fields. In addition, contour integration could be interpreted as top-down inference in the presented model.

This article describes a developmental system based on information theory implemented on a real robot that learns a model of its own sensory and actuator apparatus. There is no innate knowledge regarding the modalities or representation of the sensory input and the actuators, and the system relies on generic properties of the robot’s world such as piecewise smooth effects of movement on sensory changes. The robot develops the model of its sensorimotor system by first performing random movements to create an informational map of the sensors. Using this map the robot then learns what effects the different possible actions have on the sensors. After this developmental process the robot can perform basic visually guided movement.

In digital imaging applications, data are typically obtained via a spatial subsampling procedure implemented as a color filter array—a physical construction whereby only a single color representative is measured at each pixel location. Owing to the growing ubiquity of color imaging and display devices, much recent work has focused on the interplay between color filter array design and subsequent digital processing, including in particular the canonical spatio-chromatic reconstruction task known as demosaicking. Here we consider the problem of improved color filter array design, leading to enhanced image fidelity. We first analyze the limitations of the well-known Bayer pattern, currently most popular in industry. We then propose a framework for designing rectangular color filter arrays amenable to efficient and completely linear reconstruction, and provide examples of new patterns that enable improvements in reconstruction quality. Index Terms — Image sensors, color measurement, image sampling, image reconstruction, image color analysis 1.

We describe a formal framework for analyzing how statistical properties of natural environments and the process of natural selection interact to determine the design of perceptual and cognitive systems. The framework consists of two parts: a Bayesian ideal observer with a utility function appropriate for natural selection, and a Bayesian formulation of Darwin’s theory of natural selection. Simulations of Bayesian natural selection were found to yield new insights, for example, into the co-evolution of camouflage, color vision, and decision criteria. The Bayesian framework captures and generalizes, in a formal way, many of the important ideas of other approaches to perception and cognition.

For several decades experiments have been performed where animals have been reared in environments with orientationally restricted contours. The aim has been to nd out what e ects the visual eld has on the development of the visual system in the brain. In this paper we describe similar experiments performed with a robot acting in an environment with only vertical contours and compare the results with the same robot in an ordinary ofce environment. Using metric projections of the informational distances between sensors it is shown that all visual sensors in the same vertical column are clustered together in the environment with only vertical contours. We also show how the informational structure of the sensors unfold when the robot moves from the environment with oriented contours to a normal environment. 1.

We investigate the perception of 3D plane orientation---focusing on the perception of tilt---from optic flow generated by the observer's movement around a simulated stationary object, and compare the performance to that of an immobile observer receiving a replay of the same optic flow. We find that perception of plane orientation is more precise in the active than in the immobile case. In particular, in the case of the immobile observer the presence of shear in optic flow drastically diminishes the precision of tilt perception, whereas in the active observer this decrease in performance is greatly reduced. Furthermore, perceived slant is better correlated with simulated slant in the active observer. We conclude with a discussion of possible systematic biases in tilt perception from optic flow, as well as of various theoretical explanations for our results.

Abstract. One of the first steps of any visual system is that of locating suitable interest points, ‘salient regions’, in the scene, to detect events, and eventually to direct gaze toward these locations. In the last few years, object-based visual attention models have received an increasing interest in computational neuroscience and in computer vision, the problem, in this case, being that of creating a model of ‘objecthood ’ that eventually guides a saliency mechanism. We present here an model of visual attention based on the definition of ‘proto-objects ’ and show its instantiation on a humanoid robot. Moreover we propose a biological plausible way to learn certain Gestalt rules that can lead to proto-objects. 1 Visual Attention Spatial attention is often assimilated to a sort of ‘filter ’ of the incoming information, a ‘spotlight’, an internal eye or a ‘zoom lens’. In particular it is believed to be deployed as a spatial gradient, centered on a particular location. Even if supported by numerous findings (see [1] for a review), this view does not stress

The Gestalt principle of collinearity (and curvilinearity) is widely regarded as being mediated by the long-range connection structure in primary visual cortex. We review the neurophysiological and psychophysical literature to argue that these connections are developed from visual experience after birth, relying on coherent object motion. We then present a neural network model that learns these connections in an unsupervised Hebbian fashion with input from real camera sequences.

Several lines of evidence suggest that the image statistics of the environment shape visual abilities. To date, the image statistics of natural scenes and faces have been well characterized using Fourier analysis. We employed Fourier analysis to characterize images of signs in American Sign Language (ASL). These images are highly relevant to signers who rely on ASL for communication, and thus the image statistics of ASL might influence signers ’ visual abilities. Fourier analysis was conducted on 105 static images of signs, and these images were compared with analyses of 100 natural scene images and 100 face images. We obtained two metrics from our Fourier analysis: mean amplitude and entropy of the amplitude across the image set (which is a measure from information theory) as a function of spatial frequency and orientation. The results of our analyses revealed interesting differences in image statistics across the three different image sets, setting up the possibility that ASL experience may alter visual perception in predictable ways. In addition, for all image sets, the mean amplitude results were markedly different from the entropy results, which raises the interesting question of which aspect of an image set (mean amplitude or entropy of the amplitude) is better able to account for known visual abilities. © 2006 Optical Society of America