Mobile wearable computers are intended to provide users with real-time access to information in a natural and unobtrusive manner. Computing and sensing in these devices must be reliable, easy to interact with, transparent, and configured to support different needs and complexities. This paper presents a vision-based robust finger tracking algorithm combined with audio-based control commands that is integrated into a multimodal unobtrusive user interface, wherein the interface may be used to segment out objects of interest in the environment by encircling them with the user's pointing fingertip. In order to quickly extract the objects encircled by the user from a complex scene, this unobtrusive interface uses a single head-mounted camera to capture color images, which are then processed using algorithms to perform: color segmentation, fingertip shape analysis, perturbation model learning, and robust fingertip tracking. This interface is designed to be robust to changes in the environment and user's movements by incorporating a state-space estimation with uncertain models algorithm, which attempts to control the influence of uncertain environment conditions on the system's fingertip tracking performance by adapting the tracking model to compensate for the uncertainties inherent in the data collected with a wearable computer.

Establishing correspondence between features in two images of the same scene taken from different viewing angles is a challenging problem in image processing and computer vision. However, its solution is an important step in many applications like wide baseline stereo, three-dimensional (3-D) model alignment, creation of panoramic views, etc. In this paper, we propose a technique for registration of two images of a face obtained from different viewing angles. We show that prior information about the general characteristics of a face obtained from video sequences of different faces can be used to design a robust correspondence algorithm. The method works by matching two-dimensional (2-D) shapes of the different features of the face (e.g., eyes, nose etc.). A doubly stochastic matrix, representing the probability of match between the features, is derived using the Sinkhorn normalization procedure. The final correspondence is obtained by minimizing the probability of error of a match between the entire constellation of features in the two sets, thus taking into account the global spatial configuration of the features. The method is applied for creating holistic 3-D models of a face from partial representations. Although this paper focuses primarily on faces, the algorithm can also be used for other objects with small modifications.

The introduction of satellite imagery characterized by high spectral and spatial resolutions has made possible the development of new viable approaches for the accurate, and cost-effective extraction of linear features with minimal human intervention. This paper presents a semi-automated method for the extraction of roads from (1-meter) pan-sharpened multispectral IKONOS imagery. An operator provides an initial seed point on the road of interest, then the region is evolved using a level set method. Further analysis through iterative smoothing refines the extracted region to accurately estimate the road centerline despite the presence of cars on the road, changes in the pavement or surface properties of the road, or obstruction resulting from foliage or shadows cast on the road by neighboring trees. Initial results have demonstrated the utility of the algorithm in efficiently extracting roads from high resolution satellite imagery with minimal human interaction. Over 97% delineation accuracy was achieved on manually ground truthed IKONOS image samples overlooking both urban and rural locations.

This paper presents a system for the automatic extraction of urban regions from multispectral satellite images using a pseudo-supervised method to classify unlabeled images. This is accomplished through a preclassification process which automatically extracts representative training data from images without the need for ground truth data or user intervention. In the preclassification stage, a technique is developed to find a set of prototypes from regions exhibiting urban-like characteristics, then a subspace is constructed by applying principle component analysis over the set of urban prototypes. Image points are then preclassified based upon their distance to the urban subspace. A set of representative training data is extracted from the preclassified urban and non-urban regions, which is used to train a Gaussian mixture model based Bayesian classifier. The final extraction of urban-like regions is determined by the trained Bayesian classifier using a maximum a posteriori (MAP) criterion. Our experimental results show that the proposed technique is effective in automatically extracting urban regions from SPOT multispectral satellite images.