Distance transform

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

Image transformation apparatus

Abstract: In an image transformation apparatus, the image formed by an input video signal is stored into a memory, and the input video signal is read based on a read-address data of a predetermined read-address generating means, to execute a predetermined image transformation with respect to an image. The transformed image is formed by using a function in which a distance from the center of the transformed image is changeable corresponding to an angle from the center of that, so as to obtain the lens effect transforming the image into images having various curvilinearly shapes.

Dynamic digital distance maps in two dimensions

Abstract: An efficient means for dealing with obstacles in motion is provided to extend the usefulness of digital distance maps. An algorithm is presented that allows one to compute what portions of a map will probably be affected by an obstacle's motion. The algorithm is based on an analysis of the distance transform as a problem in wave propagation. The regions that must be checked for possible updates when an obstacle moves are those that are in its or in the shadow of obstacles that are partially in the shadow of the moving obstacle. The technique can handle multiple fixed goals, multiple obstacles moving and interacting in an arbitrary fashion, and it is independent of the technique used for calculation of the distance map. The algorithm is demonstrated on a number of synthetic two-dimensional examples, and example timing results are reported. >

Triangulation type distance sensor for moving objects with window forming means

Abstract: A distance sensor is disclosed composed of a pair of objective lenses 1 and 2 separated by a base length L, and a pair of two-dimensional image sensors 3 and 4. A left and a right image of a moving object 5, shifted from each other by a horizontal distance corresponding to a distance R to the object 5, is formed on the sensors 3 and 4. The left or the right image formed on the sensor is displayed on the display 11. A human operator initially sets, via the window forming device 12, the dimension and position of a window within the screen of the display. Thereafter, the microcomputer 10A samples at a predetermined sampling period the pixel signals of the image sensors 3 and 4, adjusts the dimension and the position of the window such that the window just covers the image of the object, and determines the amount of shift of the left and the right images on the basis of the image within the window.

Imaging apparatus and distance information acquisition method

Abstract: PROBLEM TO BE SOLVED: To accurately obtain a distance to an object in each part of a captured image and perform blurring processing on the basis of the distance.SOLUTION: When a photometry switch S1 is turned ON (S100), an imaging apparatus performs the steps of: driving a lens, and while focusing on a plurality of positions, storing a through-image at each position in a memory (S102); focusing on a focal position in AF processing (S104); when a release switch S2 is turned ON, capturing a main image at the focal position; dividing the stored plurality of through-images into areas of a size corresponding to a camera shake respectively, obtaining distance information to an object corresponding to each area of the captured image, and creating a distance map (S114); creating a mask on the basis of the distance map (S116); and performing blurring processing according to the distance to the object (S118).

Accurate Tracking of Monotonically Advancing Fronts

Abstract: A wide range of computer vision applications such as distance field computation, shape from shading, and shape representation require an accurate solution of a particular Hamilton-Jacobi (HJ) equation, known as the Eikonal equation. Although the fast marching method (FMM) is the most stable and consistent method among existing techniques for solving such equation, it suffers from large numerical error along diagonal directions as well as its computational complexity is not optimal. In this paper, we propose an improved version of the FMMthat is both highly accurate and computationally efficient for Cartesian domains. The new method is called the multi-stencils fast marching (MSFM), which computes the solution at each grid point by solving the Eikonal equation along several stencils and then picks the solution that satisfies the fast marching causality relationship. The stencils are centered at each grid point x and cover its entire nearest neighbors. In 2D space, 2 stencils cover the 8-neighbors of x, while in 3D space, 6 stencils cover its 26-neighbors. For those stencils that are not aligned with the natural coordinate system, the Eikonal equation is derived using directional derivatives and then solved using a higher order finite difference scheme.