Distance transform

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

Calculating the Distance Map for Binary Sampled Data

Abstract: High quality rendering and physics-based modeling in volume graphics have been limited because intensity-based volumetric data do not represent surfaces well. High spatial frequencies due to abrupt intensity changes at object surfaces result in jagged or terraced surfaces in rendered images. Use of a distance-to-closest-surface function to encode object surfaces allows accurate reconstruction of objet surfaces for volumetric data. However, constructing the distance map for distance-based rendering requires a prior model of the object surface. Here we present a number of methods that can be used to estimate the distance map from a binary segmented volume, where no prior knowledge of object surfaces exists.

Distance measuring instrument and distance measuring method

Abstract: PROBLEM TO BE SOLVED: To provide a small-sized distance measuring instrument for measuring distance information on a physical object in a short time. SOLUTION: This distance measuring instrument includes an image sensor camera 1, an image capture board 2, a memory 3 for therein storing image data, an image processing part 4 for calculating distance information to the physical object from the image data stored in the memory 3, and a display monitor 5 for displaying the distance information calculated by the processing part 4. The processing part 4 includes a reconstruction part 4a, a luminance information calculation part 4b, and a distance information calculation part 4c. COPYRIGHT: (C)2009,JPO&INPIT

Hierarchical image matching: a chamfer matching algorithm using interesting points

Abstract: Image matching in conjunction with a distance transform has played an important role in computer vision and image analysis. This paper presents a new hierarchical chamfer matching algorithm based on the detection of interesting points. The algorithm extends the traditional method by introducing interesting points to replace edge points in the distance transform for the matching measurement. A series of images, with different numbers of interesting points featuring in the original image, is created in a pyramid structure through a dynamic thresholding scheme. The matching is performed in this pyramid in a coarse-to-fine level order, by minimizing a given matching criterion in terms of the distance between selected interesting points. This hierarchical structure aims to reduce the computational load. The algorithm is simple to implement and quite insensitive to noise and other disturbances. In addition, such a hierarchical matching scheme is implemented on a low-cost heterogeneous PVM (Parallel Virtual Machine) network to speed up the processing without any specific software and hardware requirements.

Moving object detection method and image processing system for moving object detection

Abstract: A moving object detection method and an image processing system thereof are provided. First, a pixel-wise distance of a received image to a reference image is computed to obtain a distance map. A histogram analysis is performed on the distance map to obtain a distance distribution. An entropy value of the distance distribution is computed and a peak distance value which is with a maximum occurrence probability in the distance distribution is searched out. Then, by using a mapping rule, the entropy value and the peak distance value are transformed into a decision threshold value. The decision threshold value is applied in classifying the pixels of the distance map into a group of foreground attributes and a group of background attributes and thereby moving objects in the current image are obtained.