Edge Detection by Adaptive Splitting II. The Three-Dimensional Case

Abstract

In Lianas and Lantaron, J. Sci. Comput. 46, 485-518 (2011) we proposed an algorithm (EDAS-<i) to approximate the jump discontinuity set of functions defined on subsets of R d . This procedure is based on adaptive splitting of the domain of the function guided by the value of an average integral. The above study was limited to the ID and 2D versions of the algorithm. In this paper we address the three-dimensional problem. We prove an integral inequality (in the case d = 3) which constitutes the basis of EDAS-3. We have performed detailed computational experiments demonstrating effective edge detection in 3D function models with different interface topologies. EDAS-1 and EDAS-2 appealing properties are extensible to the 3D case.

3D Image Edge Detection Algorithm for Depth Detection

“…Exact edge discovery from a profundity picture is fundamental for some protest recognition forms [20], which are reliant on a model of specific shape. A legitimate edge discovery process can be utilized for different Human activity investigation [21] When they are connected to issues requiring handling multioutlines, these strategies may bomb as they can't manage recently produced commotions in each casing, for example, gleaming issues. In this paper, we proposed a strategy that can identify edges from profundity pictures all the more significantly.…”

“…We can add to the immediate strategies the versatile part techniques, which depend on the versatile part of the area of the capacity guided by the estimation of a normal necessary. In [21], a calculation (EDAS-3) to inexact the bounce brokenness set of capacities characterized on subsets of situated in these strategies was…”

A Doubly Adaptive Algorithm for Edge Detection in 3D Images

“…It is proved at [21,22] that the behavior of the next average integral AI ( ) depends on the continuity or lack of continuity of on :…”

“…We can add to the direct methods the adaptive splitting methods, which are based on the adaptive splitting of the domain of the function guided by the value of an average integral. In [21], an algorithm (EDAS-3) to approximate the jump discontinuity set of functions defined on subsets of R 3 based in these methods was proposed. This algorithm overcomes most inconveniences presented by deformable models.…”

“…The process is repeated for the 1 3 and algorithm is doubly adaptive because the "straight line step" is performed in an adaptive way by EDAS-1 and the second step performs an adaptive process based on the complexity of the image. The algorithm uses less CPU time than previous algorithm [21]. We will give a C-like pseudocode description of the constituent algorithms of DA3DED.…”