Orientation (computer vision)

About: Orientation (computer vision) is a research topic. Over the lifetime, 17196 publications have been published within this topic receiving 358181 citations.

A nonlinear laplace operator as edge detector in noisy images

Abstract: An edge detection scheme is developed robust enough to perform well over a wide range of signal-to-noise ratios. It is based upon the detection of zero crossings in the output image of a nonlinear Laplace filter. Specific characterizations of the nonlinear Laplacian are its adaptive orientation to the direction of the gradient and its inherent masks which permit the development of approximately circular (isotropic) filters. We have investigated the relation between the locally optimal filter parameters, smoothing size, and filter size, and the SNR of the image to be processed. A quantitative evaluation shows that our edge detector performs at least as well—and in most cases much better—than edge detectors. At very low signal-to-noise ratios, our edge detector is superior to all others tested.

Image Analysis of Defocused Single-Molecule Images for Three-Dimensional Molecule Orientation Studies

Abstract: An efficient algorithm for pattern matching has been developed based on least-squares analysis of fitting a discrete set of master patterns against measured images. This algorithm has been applied to determine three-dimensional molecule orientations in defocused single-molecule images. The developed algorithm exploits the excellent agreement between electrodynamic calculations of single-molecule emission and experimentally measured images. The procedure is found to be reliable and simple and can be applied to any kind of pattern recognition where the patterns to be recognized are precisely known a priori. The procedure works well even for noisy and low-intensity signals as usually encountered in single-molecule experiments.

Inference of surfaces, 3D curves, and junctions from sparse, noisy, 3D data

Abstract: We address the problem of obtaining dense surface information from a sparse set of 3D data in the presence of spurious noise samples. The input can be in the form of points, or points with an associated tangent or normal, allowing both position and direction to be corrupted by noise. Most approaches treat the problem as an interpolation problem, which is solved by fitting a surface such as a membrane or thin plate to minimize some function. We argue that these physical constraints are not sufficient, and propose to impose additional perceptual constraints such as good continuity and "cosurfacity". These constraints allow us to not only infer surfaces, but also to detect surface orientation discontinuities, as well as junctions, all at the same time. The approach imposes no restriction on genus, number of discontinuities, number of objects, and is noniterative. The result is in the form of three dense saliency maps for surfaces, intersections between surfaces (i.e., 3D curves), and 3D junctions, respectively. These saliency maps are then used to guide a "marching" process to generate a description (e.g., a triangulated mesh) making information about surfaces, space curves, and 3D junctions explicit. The traditional marching process needs to be refined as the polarity of the surface orientation is not necessarily locally consistent. These three maps are currently not integrated, and this is the topic of our ongoing research. We present results on a variety of computer-generated and real data, having varying curvature, of different genus, and multiple objects.

Augmented virtual environments

Abstract: Systems and techniques to implement augmented virtual environments. In one implementation, the technique includes: generating a three dimensional (3D) model of an environment from range sensor information representing a height field for the environment, tracking orientation information of image sensors in the environment with respect to the 3D model in real-time, projecting real-time video from the image sensors onto the 3D model based on the tracked orientation information, and visualizing the 3D model with the projected real-time video. Generating the 3D model can involve parametric fitting of geometric primitives to the range sensor information. The technique can also include: identifying in real time a region in motion with respect to a background image in real-time video, the background image being a single distribution background dynamically modeled from a time average of the real-time video, and placing a surface that corresponds to the moving region in the 3D model.