Vessel extraction and vessel motion estimation from X-ray angiograms has been a challenging computer vision problem for several years. We have developed a fast and accurate method for extracting and tracking intravascular imaging data from X-ray angiograms. We accomplish this by reconstructing a moving 3D vessel, which contains more information than the static 2D snapshot image. Our approach involves identifying the vessel-of-interest in two biplane images, abstracting them into centerlines, and tracking them in ensuing images using deformable templates and graph techniques for optimization. When tested on fifteen patient datasets, the computational time was approximately 5 seconds per vessel per frame for vessels of length 80-100 mm.

The automatic recognition of text on scanned images has enabled many applications such as searching for words in large volumes of documents, automatic sorting of postal mail, and convenient editing of previously printed documents. The domain of handwriting in the Arabic script presents unique technical challenges and has been addressed more recently than other domains. Many different methods have been proposed and applied to various types of images. This paper provides a comprehensive review of these methods. It is the first survey to focus on Arabic handwriting recognition and the first Arabic character recognition survey to provide recognition rates and descriptions of test data for the approaches discussed. It includes background on the field, discussion of the methods, and future research directions.

We propose a novel algorithm for the segmentation and prerecognition of offline handwritten Arabic text. Our character segmentation method over-segments each word, and then removes extra breakpoints using knowledge of letter shapes. On a test set of 200 images, 92.3% of the segmentation points were detected correctly, with 5.1% instances of over-segmentation. The prerecognition component annotates each detected letter with shape information, to be used for recognition in future work.

Curve evolution schemes for segmentation, implemented with level set methods, have become an important approach in computer vision. Previous work has modeled evolving contours which are curves in 2D or surfaces in 3D. Our objective is to explore recent mathematical work enabling the evolution of manifolds of higher co-dimension. We consider 1D curves in 3D (codimension-two) for the application of automatically, segmenting blood vessels in volumetric magnetic resonance angiography (MRA) images. This paper describes the theoretical foundations of our system, CURVES, then provides segmentation results compared against segmentation obtained interactively by a neurosurgeon. Segmentation of bronchi in lung computed tomography (CT) scans are also presented. The new experiments, comparisons to manual segmentation, and sample comparison to the use of a codimension-one regularization force are the primary contributions of this report.

This paper presents an autonomous vision-based obstacle avoidance system. The system consists of three independent vision modules for obstacle detection, each of which is computationally simple and uses a different criterion for detection purposes. These criteria are based on brightness gradients, RGB (red, green, blue) color, and HSV (hue, saturation, value) color, respectively. Selection of which modules are used to command the robot proceeds exclusively from the outputs of the modules themselves. The system is implemented on a small monocular mobile robot and uses very lour resolution images. It has been tested for over 200 hours in diverse environments.

In this paper we address the problem of recognizing an object from a novel viewpoint, given a single "model" view of that object. As is common in model-based recognition, objects and images are represented as sets of feature points. We present an efficient algorithm for determining whether two sets of image points (in the plane) could be projections of a common object (a three-dimensional point set). The method relies on the fact that two sets of points in the plane are orthographic projections of the same three-dimensional point set exactly when they have a common projection onto a line. This is a form of the well-known epipolar constraint used in stereopsis. Our algorithm can be used to recognize an object by comparing a stored two-dimensional view of the object against an unknown view, without requiring the correspondence between points in the views to be known a priori. We provide some examples illustrating the approach.