Distance transform

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

Indexing and segmenting colour images using neighbourhood sequences

Abstract: In this paper we present some methods for indexing and segmenting colour images. The proposed procedures are based on well-known algorithms, but now we use digital distance functions generated by neighbourhood sequences to measure distance between colours. The application of such distance functions is quite natural and descriptive, since the colour coordinates of the pixels are nonnegative integers. An additional interesting property of neighbourhood sequences is that they do not generate metric in general, so we can obtain many distance functions in this way. We describe our methods for RGB images in detail, but other image representations also could be considered. Moreover, the proposed methods can be applied in arbitrary dimension without any difficulties.

Representation of 2D and 3D binary images using medial circles and spheres

Abstract: Representation schemes play an important role in the fields of Computer Vision, Graphics, Image Processing, CAD/CAM etc. Various representation schemes have been discussed in the literature for both 2D and 3D. In this paper, we are presenting a scheme of representation using the concept of octagonal distances. They are called Medial Circle Representation (MCR) and Medial Sphere Representation (MSR) in 2D and 3D, respectively. Storage requirement, computational complexity, merits and demerits of the representation schemes are discussed.

Three-dimensional object recognizing system

Abstract: A three-dimensional object recognizing system comprises a distance image generating portion for generating a distance image by using image pairs picked up by a stereoscopic camera, a grouping processing portion for grouping the distance data indicating the same three-dimensional object on the distance image, an input value setting portion for setting an area containing distance data group of grouped three-dimensional object on the distance image and also setting input values having typical distance data as elements every small area that is obtained by dividing the area by a set number of partition, a computing portion for computing output values having a pattern that responds to a previously set three-dimensional object by using a neural network that has at least the input values Xin as inputs to an input layer, and a discriminating portion for discriminating the type of the three-dimensional object based on the pattern of the output values.

Method for selectively regenerating an adaptively sampled distance field

Abstract: A method for modeling a graphics object generates a model of the graphics object. An adaptively sampled distance field is generated from the model according to an error measure. The adaptively sampled distance field includes interior, surface, and exterior cells. Each cell stores distance values, and the distance values of the surface cells always satisfy the error measure. A subset of cells are selected from the adaptively sampled distance field. The subset of cells only include interior and exterior cells. The selected cells are subdivided and the distance values for the subdivided cells are regenerated, until the distance values of the subdivided cells satisfy the error measure.

Convolution Filtering of Continuous Signed Distance Fields for Polygonal Meshes

Abstract: Signed distance fields obtained from polygonal meshes are commonly used in various applications. However, they can have C1 discontinuities causing creases to appear when applying operations such as blending or metamorphosis. The focus of this work is to efficiently evaluate the signed distance function and to apply a smoothing filter to it while preserving the shape of the initial mesh. The resulting function is smooth almost everywhere, while preserving the exact shape of the polygonal mesh. Due to its low complexity, the proposed filtering technique remains fast compared to its main alternatives providing C1-continuous distance field approximation. Several applications are presented such as blending, metamorphosis and heterogeneous modelling with polygonal meshes.