A class of biometrics based upon ear features is introduced for use in the development of passive identification systems. The viability of the proposed biometric is shown both theoretically in terms of the uniqueness and measurability over time of the ear, and in practice through the implementation of a computer vision based system. Each subject's ear is modeled as an adjacency graph built from the Voronoi diagram of its curve segments. We introduce a novel graph matching based algorithm for authentication which takes into account the erroneous curve segments which can occur due to changes (e.g., lighting, shadowing, and occlusion) in the ear image. This class of biometrics is ideal for passive identification because the features are robust and can be reliably extracted from a distance.

We address the problem of describing, recognizing, and learning generic, free-form objects in real-world scenes. For this purpose, we have developed a hybrid appearance-based approach where objects are encoded as loose collections of parts and relations between neighboring parts. The key features of this approach are: part decomposition based on local structure segmentation derived from multi-scale wavelet filters, flexible and efficient recognition by combining weak structural constraints, and learning and generalization of generic object categories (with possibly large intra-class variability) from real examples.

We address the problem of describing, recognizing, and learning generic, free-form objects in real-world scenes. An appearance-based system using weak-structure and evidence accumulation where object models are implicitly encoded in a learned decision tree and objects are represented in graph form as in the method developed by Bischof and Caelli (1994). The decision tree is used to classify sequences of image components, or part paths, extracted from the object to be recognized. The part paths are in turn used to accumulate evidence for the classification of the entire object. We introduce an improved method for generating part paths based upon the part compatibility graph, a replacement for Bischof and Caelli's implicit use of the part adjacency graph. A new formalism for extending the representation and recognition scheme to utilize multiple (polymorphic) types of primitive parts is presented and the approach is demonstrated on a selection of imagery.

We investigate the problem of estimating the camera motion parameters from a 2D image sequence that has been obtained under combined 3D camera translation and rotation. Geometrical constraints in 2D are used to iteratively narrow down regions of possible values in the space of 3D motion parameters. The approach is based on a new concept called "FOE-feasibility" of an image region, for which an efficient algorithm has been implemented. Results are shown on real images.>

The computation of sensor motion from sets of displacement vectors obtained from consecutive pairs of images is discussed. The problem is investigated with emphasis on its application to autonomous robots and land vehicles. The effects of 3D camera rotation and translation upon the observed image are discussed, particularly the concept of the focus of expansion (FOE). It is shown that locating the FOE precisely is difficult when displacement vectors are corrupted by noise and errors. A more robust performance can be achieved by computing a 2D region of possible FOE locations (termed the fuzzy FOE) instead of looking for a single-point FOE. The shape of this FOE region is an explicit indicator of the accuracy of the result. It has been shown elsewhere that given the fuzzy FOE, a number of powerful inferences about the 3D sense structure and motion become possible. Aspects of computing the fuzzy FOE are emphasized, and the performance of a particular algorithm on real motion sequences taken from a moving autonomous land vehicle is shown.>

A new approach to the dynamic scene analysis is presented which departs from previous work by emphasizing a qualitative strategy of reasoning and modeling. Instead of refining a single quantitative description of the observed environment over time, multiple qualitative interpretations are maintained simultaneous-ly. This offers superior robustness and flexibility over traditional numerical techniques which are often ill-conditioned and noise-sensitive. The main tasks of our approach are (a) to detect and to classify the motion of individual objects in the scene, (b) to estimate the robot's egomotion, and (c) to derive the 3-D struc-ture of the stationary environment. These three tasks strongly depend on each other. First, the direction of heading (i.e. trans-lation) and rotation of the robot are estimated with respect to stationary locations in the scene. The focus of expansion (FOE) is not determined as particular image location, but as a region of possible FOE-locations called the Fuzzy FOE. From this infor-mation, a rule-based system constructs and maintains a Qualitative Scene Model. Results of this approach from real and synthetic imagery are presented. 1.