Coliseum is a multiuser immersive remote teleconferencing system designed to provide collaborative workers the experience of face-to-face meetings from their desktops. Five cameras are attached to each PC monitor and directed at the participant. From these video streams, view synthesis methods produce arbitrary-perspective renderings of the participant and transmit them to others at interactive rates-- currently about 15 frames per second. Combining these renderings in a shared synthetic environment gives the appearance of having all participants interacting in a common space. In this way, Coliseum enables users to share a virtual world, with acquired-image renderings of their appearance replacing the synthetic representations provided by more conventional avatar-populated virtual worlds. The system supports virtual mobility-- participants may move around the shared space – and reciprocal gaze, and has been demonstrated in collaborative sessions of up to ten Coliseum workstations, and sessions spanning two continents. This paper summarizes the technology, and reports on issues related to its performance.

This paper presents a numerically stable non-iterative algorithm for fitting an ellipse to a set of data points. The approach is based on a least squares minimization and it guarantees an ellipse-specific solution even for scattered or noisy data. The optimal solution is computed directly, no iterations are required. This leads to a simple, stable and robust fitting method which can be easily implemented. The proposed algorithm has no computational ambiguity and it is able to fit more than 100,000 points in a second. Keywords: ellipses, fitting, least squares, eigenvectors INTRODUCTION One of basic tasks in pattern recognition and computer vision is a fitting of geometric primitives to a set of points (see [Duda73] for a summary). The use of primitive models allows reduction and simplification of data and, consequently, faster and simpler processing. A very important primitive is an ellipse, which, being a perspective projection of a circle, is exploited in many applications of ...

In this paper, we present a novel approach, called the “one-circle ” algorithm, for measuring the eye gaze using a monocular image that zooms in on only one eye of a person. Observing that the iris contour is a circle, we estimate the normal direction of this iris circle, considered as the eye gaze, from its elliptical image. From basic projective geometry, an ellipse can be backprojected into space onto two circles of different orientations. However, by using an anthropometric property of the eyeball, the correct solution can be disambiguated. This allows us to obtain a higher resolution image of the iris with a zoom-in camera and thereby achieving higher accuracies in the estimation. The robustness of our gaze determination approach was verified statistically by the extensive experiments on synthetic and real image data. The two key contributions in this paper are that we show the possibility of finding the unique eye gaze direction from a single image of one eye and that one can obtain better accuracy as a consequence of this. 1.

In this paper, we address the problem of the automatic metric reconstruction Surface of Revolution (SOR) from a single uncalibrated view. The apparent contour and the visible portions of the imaged SOR cross sections are extracted and classified. The harmonic homology that models the image projection of the SOR is also estimated. The special care devoted to accuracy and robustness with respect to outliers makes the approach suitable for automatic camera calibration and metric reconstruction from single uncalibrated views of a SOR. Robustness and accuracy are obtained by embedding a graph-based grouping strategy (Euclidean Minimum Spanning Tree) into an Iterative Closest Point framework for projective curve alignment at multiple scales. Classification of SOR curves is achieved through a 2-dof voting scheme based on a pencil of conics novel parametrization. The main contribution of this work is to extend the domain of automatic single view reconstruction from piecewise planar scenes to scenes including curved surfaces, thus allowing to create automatically realistic image models of man-made objects. Experimental results with real images taken from the internet are reported, and the effectiveness and limitations of the approach are discussed.

Abstract—We present a new, robust, computational procedure for tracking fluorescent markers in time-lapse microscopy. The algorithm is optimized for finding the time-trajectory of single particles in very noisy dynamic (two- or three-dimensional) image sequences. It proceeds in three steps. First, the images are aligned to compensate for the movement of the biological structure under investigation. Second, the particle’s signature is enhanced by applying a Mexican hat filter, which we show to be the optimal detector of a Gaussian-like spot in I P noise. Finally, the optimal trajectory of the particle is extracted by applying a dynamic programming optimization procedure. We have used this software, which is implemented as a Java plug-in for the public-domain ImageJ software, to track the movement of chromosomal loci within nuclei of budding yeast cells. Besides reducing trajectory analysis time by several 100-fold, we achieve high reproducibility and accuracy of tracking. The application of the method to yeast chromatin dynamics reveals different classes of constraints on mobility of telomeres, reflecting differences in nuclear envelope association. The generic nature of the software allows application to a variety of similar biological imaging tasks that require the extraction and quantitation of a moving particle’s trajectory. Index Terms—Dynamic programming (DP), fluorescence microscopy, image sequence analysis, living cell, particle tracking. I.

A calibration procedure for accurately determining the pose and internal parameters of several cameras is described. Multiple simultaneously-captured sets of images of a calibration object in different poses are used by the calibration procedure. Coded target patterns, which serve as control points, are distributed over the surface of the calibration object. The observed positions of these targets within the images can be automatically determined by means of code band patterns. The positions of the targets across the multiple images are then used to infer the camera parameters, as well as the 3D geometrical structure of the targets on the calibration object (thus avoiding the expense of a calibration object with accurately known 3D structure). Results for a three-camera system show RMS (root-mean-square) deviations of less than five microns of the inferred positions of 54 control points, distributed on the surface of a 50 mm cube, from their expected positions on a flat surface. The RMS difference between the positions of 1423 observed control points and the positions predicted by a 330 parameter model of the camera system and calibration object was 0.09 pixels.

The renormalisation technique of Kanatani is intended to iteratively minimise a cost function of a certain form while avoiding systematic bias inherent in the common method of minimisation due to Sampson. Within the computer vision community, the technique has generally proven difficult to absorb. This work presents an alternative derivation of the technique, and places it in the context of other approaches. We first show that the minimiser of the cost function must satisfy a special variational equation. A Newton-like, fundamental numerical scheme is presented with the property that its theoretical limit coincides with the minimiser. Standard statistical techniques are then employed to derive afresh several renormalisation schemes. The fundamental scheme proves pivotal in the rationalising of the renormalisation and other schemes, and enables us to show that the renormalisation schemes do not have as their theoretical limit the desired minimiser. The various minimisation schemes are finally subjected to a comparative performance analysis under controlled conditions.

Numerical simulation is replacing experimentation as a means to gain insight into complex physical phenomena. Analyzing the data produced by such simulations is extremely challenging, given the enormous sizes of the datasets involved. In order to make e#cient progress, analyzing such data must advance from current techniques that only visualize static images of the data, to novel techniques that can mine, track, and visualize the important features in the data. In this paper, we present our research on a unified framework that addresses this critical challenge in two science domains: computational fluid dynamics and molecular dynamics. We o#er a systematic approach to detect the significant features in both domains, characterize and track them, and formulate hypotheses with regard to their complex evolution. Our framework includes two paradigms for feature mining, and the choice of one over the other, for a given application, can be determined based on local or global influence of relevant features in the data.

Analysis of sketched digital ink is often aided by the division of stroke points into perceptually-salient fragments based on geometric features. Fragmentation has many applications in intelligent interfaces for digital ink capture and manipulation, as well as higher-level symbolic and structural analyses. It is our intuitive belief that the most robust fragmentations closely match a user’s natural perception of the ink, thus leading to more effective recognition and useful user feedback. We present two optimal fragmentation algorithms that fragment common geometries into a basis set of line segments and elliptical arcs. The first algorithm uses an explicit template in which the order and types of bases are specified. The other only requires the number of fragments of each basis type. For the set of symbols under test, both algorithms achieved 100 % fragmentation accuracy rate for symbols with line bases,>99 % accuracy for symbols with elliptical bases, and>90 % accuracy for symbols with mixed line and elliptical bases.

We present a system at the junction between Computer Vision and Computer Graphics, to produce a 3-D model of an object as observed in a single image, with a minimum of high-level interaction from a user. The input to our system is a single image. First, the user points, coarsely, at image features (edges) that are subsequently automatically and reproducibly extracted in real-time. The user then performs a high level labeling of the curves (e.g. limb edge, cross-section) and specifies relations between edges (e.g. symmetry, surface or part). NURBS are used as working representation of image edges. The objects described by the user specified, qualitative relationships are then reconstructed either as a set of connected parts modeled as Generalized Cylinders, or as a set of 3-D surfaces for 3-D bilateral symmetric objects. In both cases, the texture is also extracted from the image. Our system runs in real-time on a PC.