2442998
Image-consistent surface triangulation
Given a set of 3D points that we know lie on the surface of an object, we can define many possible surfaces that pass through all of these points. Even when we consider only surface triangulations, there are still an exponential number of valid triangulations that all fit the data. Each triangulation will produce a different faceted surface connecting the points. Our goal is to overcome this ambiguity and find the particular surface that is closest to the true object surface. We do not know the true surface but instead we assume that we have a set of images of the object. We propose selecting a triangulation based on its consistency with this set of images of the object. We present an algorithm that starts with an initial rough triangulation and refines the triangulation until it obtains a surface that best accounts for the images of the object. Our method is thus able to overcome the surface ambiguity problem and at the same time capture sharp corners and handle concave regions and occlusions. We show results for a few real objects.
31729209
Recognizing upper face action units for facial expression analysis
We develop an automatic system to analyze subtle changes in upper face expressions based on both permanent facial features (brows, eyes, mouth) and transient facial features (deepening of facial furrows) in a nearly frontal image sequence. Our system recognizes fine-grained changes in facial expression based on Facial Action Coding System (FACS) action units (AUs). Multi-state facial component models are proposed for tracting and modeling different facial features, including eyes, brews, cheeks, and furrows. Then we convert the results of tracking to detailed parametric descriptions of the facial features. These feature parameters are fed to a neural network which recognizes 7 upper face action units. A recognition rate of 95% is obtained for the test data that include both single action units and AU combinations.
16413090
Reconstructing specimens using DIC microscope images
Differential Interference Contrast (DIC) microscopy is a powerful visualization tool used to study live biological cells. Its use, however, has been limited to qualitative observations. The inherent nonlinear relationship between the object properties and the image intensity makes quantitative analysis difficult. Towards quantitatively measuring optical properties of objects from DIC images, the authors develop a method to reconstruct the specimen's optical properties over a three-dimensional volume. The method is a nonlinear optimization which uses hierarchical representations of the specimen and data. As a necessary tool, the authors have developed and validated a computational model for the DIC image formation process. They test their algorithm by reconstructing the optical properties of known specimens.
3129922
Uncertainty Modeling for Optimal Structure from Motion
The parameters estimated by Structure from Motion (SFM) contain inherent indeterminacies which we call gauge freedoms. Under a perspective camera, shape and motion parameters are only recovered up to an unknown similarity transformation. In this paper we investigate how covariance-based uncertainty can be represented under these gauge freedoms. Past work on uncertainty modeling has implicitly imposed gauge constraints on the solution before considering covariance estimation. Here we examine the effect of selecting a particular gauge on the uncertainty of parameters. We show potentially dramatic effects of gauge choice on parameter uncertainties. However the inherent geometric uncertainty remains the same irrespective of gauge choice. We derive a Geometric Equivalence Relationship with which covariances under different parametrizations and gauges can be compared, based on their true geometric uncertainty. We show that the uncertainty of gauge invariants exactly captures the geometric uncertainty of the solution, and hence provides useful measures for evaluating the uncertainty of the solution. Finally we propose a fast method for covariance estimation and show its correctness using the Geometric Equivalence Relationship.
35787245
An Image Overlay System for Medical Data Visualization
Image Overlay is a computer display technique which superimposes computer images over the user's direct view of the real world. The images are transformed in real-time so they appear to the user to be an integral part of the surrounding environment. By using Image Overlay with three-dimensional medical images such as CT reconstructions, a surgeon can visualize the data 'in-vivo', exactly positioned within the patient's anatomy, and potentially enhance the surgeon's ability to perform a complex procedure. This paper describes prototype Image Overlay systems and initial experimental results from those systems.
10162727
The Asymmetry of Image Registration and Its Application to Face Tracking
Most image registration problems are formulated in an asymmetric fashion. Given a pair of images, one is implicitly or explicitly regarded as a template and warped onto the other to match as well as possible. In this paper, we focus on this seemingly arbitrary choice of the roles and reveal how it may lead to biased warp estimates in the presence of relative scaling. We present a principled way of selecting the template and explain why only the correct asymmetric form, with the potential inclusion of a blurring step, can yield an unbiased estimator. We validate our analysis in the domain of model-based face tracking. We show how the usual active appearance model (AAM) formulation overlooks the asymmetry issue, causing the fitting accuracy to degrade quickly when the observed objects are smaller than their model. We formulate a novel, "resolution-aware fitting" (RAF) algorithm that respects the asymmetry and incorporates an explicit model of the blur caused by the camera's sensing elements into the fitting formulation. We compare the RAF algorithm against a state-of-the-art tracker across a variety of resolutions and AAM complexity levels. Experimental results show that RAF significantly improves the estimation accuracy of both shape and appearance parameters when fitting to low-resolution data. Recognizing and accounting for the asymmetry of image registration leads to tangible accuracy improvements in analyzing low-resolution imagery
20385116
Toward autonomous driving: the CMU Navlab. Part I - Perception
The Navlab project, which seeks to build an autonomous robot that can operate in a realistic environment with bad weather, bad lighting, and bad or changing roads, is discussed. The perception techniques developed for the Navlab include road-following techniques using color classification and neural nets. These are discussed with reference to three road-following systems, SCARF, YARF, and ALVINN. Three-dimensional perception using three types of terrain representation (obstacle maps, terrain feature maps, and high-resolution maps) is examined. It is noted that perception continues to be an obstacle in developing autonomous vehicles. This work is part of the Defense Advanced Research Project Agency. Strategic Computing Initiative. >