MARVEL: A System for Recognizing World Locations with Stereo Vision

To use a world model, a mobile robot must be able to determine its own position in the world. To support truly autonomous navigation, I present MARVEL, a system that builds and maintains its own models of world locations and uses these models to recognize its world position from stereo vision input. MARVEL is designed to be robust with respect to input errors and to respond to a gradually changing world by updating its world location models. I present results from real-world tests of the system that demonstrate its reliability. MARVEL fits into a world modeling system under development.

Stereo-Based Head Pose Tracking Using Iterative Closest Point and Normal Flow Constraint

In this text, we present two stereo-based head tracking techniques along with a fast 3D model acquisition system. The first tracking technique is a robust implementation of stereo-based head tracking designed for interactive environments with uncontrolled lighting. We integrate fast face detection and drift reduction algorithms with a gradient-based stereo rigid motion tracking technique. Our system can automatically segment and track a user's head under large rotation and illumination variations. Precision and usability of this approach are compared with previous tracking methods for cursor control and target selection in both desktop and interactive room environments. The second tracking technique is designed to improve the robustness of head pose tracking for fast movements. Our iterative hybrid tracker combines constraints from the ICP (Iterative Closest Point) algorithm and normal flow constraint. This new technique is more precise for small movements and noisy depth than ICP alone, and more robust for large movements than the normal flow constraint alone. We present experiments which test the accuracy of our approach on sequences of real and synthetic stereo images. The 3D model acquisition system we present quickly aligns intensity and depth images, and reconstructs a textured 3D mesh. 3D views are registered with shape alignment based on our iterative hybrid tracker. We reconstruct the 3D model using a new Cubic Ray Projection merging algorithm which takes advantage of a novel data structure: the linked voxel space. We present experiments to test the accuracy of our approach on 3D face modelling using real-time stereo images.

Optimal Unsupervised Learning in Feedforward Neural Networks

We investigate the properties of feedforward neural networks trained with Hebbian learning algorithms. A new unsupervised algorithm is proposed which produces statistically uncorrelated outputs. The algorithm causes the weights of the network to converge to the eigenvectors of the input correlation with largest eigenvalues. The algorithm is closely related to the technique of Self-supervised Backpropagation, as well as other algorithms for unsupervised learning. Applications of the algorithm to texture processing, image coding, and stereo depth edge detection are given. We show that the algorithm can lead to the development of filters qualitatively similar to those found in primate visual cortex.

Geometry and Photometry in 3D Visual Recognition

The report addresses the problem of visual recognition under two sources of variability: geometric and photometric. The geometric deals with the relation between 3D objects and their views under orthographic and perspective projection. The photometric deals with the relation between 3D matte objects and their images under changing illumination conditions. Taken together, an alignment-based method is presented for recognizing objects viewed from arbitrary viewing positions and illuminated by arbitrary settings of light sources.

How are Three-Deminsional Objects Represented in the Brain?

We discuss a variety of object recognition experiments in which human subjects were presented with realistically rendered images of computer-generated three-dimensional objects, with tight control over stimulus shape, surface properties, illumination, and viewpoint, as well as subjects' prior exposure to the stimulus objects. In all experiments recognition performance was: (1) consistently viewpoint dependent; (2) only partially aided by binocular stereo and other depth information, (3) specific to viewpoints that were familiar; (4) systematically disrupted by rotation in depth more than by deforming the two-dimensional images of the stimuli. These results are consistent with recently advanced computational theories of recognition based on view interpolation.

Role of Low-level Mechanisms in Brightness Perception

Brightness judgments are a key part of the primate brain's visual analysis of the environment. There is general consensus that the perceived brightness of an image region is based not only on its actual luminance, but also on the photometric structure of its neighborhood. However, it is unclear precisely how a region's context influences its perceived brightness. Recent research has suggested that brightness estimation may be based on a sophisticated analysis of scene layout in terms of transparency, illumination and shadows. This work has called into question the role of low-level mechanisms, such as lateral inhibition, as explanations for brightness phenomena. Here we describe experiments with displays for which low-level and high-level analyses make qualitatively different predictions, and with which we can quantitatively assess the trade-offs between low-level and high-level factors. We find that brightness percepts in these displays are governed by low-level stimulus properties, even when these percepts are inconsistent with higher-level interpretations of scene layout. These results point to the important role of low-level mechanisms in determining brightness percepts.