Distance transform

About: Distance transform is a research topic. Over the lifetime, 2886 publications have been published within this topic receiving 59481 citations.

Automatic focusing of every image point in an image

Abstract: Every image point of an image of a sample formed by an optical objective, focussed by, for each image point, deriving a representation of the autocorrelation function of the brightness distribution of the image of that image point, deriving a representation of the average of the autocorrelation function of the brightness distribution of a plurality of image points in the region of that image point, deriving a representation constituting a function of the quotient formed by dividing the representation of the autocorrelation function for that image point by the representation of the average of the autocorrelation function, and varying the distance between the objective and the sample while iteratively performing the above steps of deriving for determining the distance at which the function of the quotient has a maximum value.

High Performance GPU‐based Proximity Queries using Distance Fields

Abstract: Proximity queries such as closest point computation and collision detection have many applications in computer graphics, including computer animation, physics-based modelling, augmented and virtual reality. We present efficient algorithms for proximity queries between a closed rigid object and an arbitrary, possibly deformable, polygonal mesh. Using graphics hardware to densely sample the distance field of the rigid object over the arbitrary mesh, we compute minimal proximity and collision response information on the graphics processing unit (GPU) using blending and depth buffering, as well as parallel reduction techniques, thus minimizing the readback bottleneck. Although limited to image-space resolution, our algorithm provides high and steady performance when compared with other similar algorithms. Proximity queries between arbitrary meshes with hundreds of thousands of triangles and detailed distance fields of rigid objects are computed in a few milliseconds at high-sampling resolution, even in situations with large overlap.

Distance Solutions for Medial Axis Transform

Abstract: A method towards robust and efficient medial axis transform (MAT) of arbitrary domains using distance solutions is presented. The distance field, d, is calculated by solving the hyperbolic-natured Eikonal (or Level Set) equation. The solution is obtained on Cartesian grids. Both the fast-marching method and fast-sweeping method are used to calculate d. Medial axis point clouds are then extracted based on the distance solution via a simple criteria: the Laplacian or the Hessian determinant of d. These point clouds in 2D-pixel and 3D-voxel space are further thinned to curves and surfaces through binary image thinning algorithms. This results in an overall hybrid approach. As an alternative to other methods, the current d −MAT procedure bypasses difficulties that are usually encountered by pure geometric methods (e.g. the Voronoi approach), especially in 3D, and provides better accuracy than pure thinning methods. It is also shown that the d −MAT approach provides the potential to sculpt/control the MAT form for specialized solution purposes. Various examples are given to demonstrate the current approach.

Parallel Region Property Computation by Active Quadtree Networks

Abstract: Given a binary image stored in a cellular array, a local reconfiguration process can be used to reconnect some of the cells into a quadtree network representing the image. This quadtree can also be ``roped,'' i.e., nodes representing adjacent image blocks of the same size can be joined. Using the roped quadtree network as a parallel (cellular) computer, image properties such as perimeter and genus, as well as the quadtree distance transform, can be computed in O(tree height) = O(log image diameter) time. The area and centroid of the image can be computed in O(height) time without the need for roping.