A PARALLEL THINNING ALGORITHM USING K×K MASKS

1993 ◽  
Vol 07 (05) ◽  
pp. 1183-1202 ◽  
Author(s):  
VIRGINIE POTY ◽  
STÉPHANE UBEDA
Keyword(s):  
Pattern Recognition ◽  
Parallel Algorithms ◽  
Basic Operation ◽  
Thinning Algorithm ◽  
Raster Scanning ◽  
Thinning Operation ◽  
Thinning Algorithms

Thinning is a standard kernel in pattern recognition. Both serial and parallel algorithms have been investigated. Two classes of thinning algorithms can be defined: thinning techniques that use a raster-scanning of the whole image and a mask applied on each pixel, and thinning techniques that use a contour generation of the whole image as a basic operation of the thinning operation. The goal of this paper is to increase the speed of mask thinning algorithms by increasing the size of the thinning window.

EFFICIENT PARALLEL ALGORITHMS FOR PATTERN RECOGNITION∗

1994 ◽  
Vol 2 (1-2) ◽  
pp. 81-98
Author(s):  
SAJAL K. DAS ◽  
PAUL S. FISHER ◽  
HUA ZHANG
Keyword(s):  
Pattern Recognition ◽  
Parallel Algorithms

Thinning Algorithm of Binary Image Based on Improved Templates

2014 ◽  
Vol 543-547 ◽  
pp. 2547-2550
Author(s):  
Yan Rui Du ◽  
Bin Xie ◽  
Li Ping Wang
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Binary Image ◽  
Original Image ◽  
Flow Process ◽  
Thinning Algorithm

Thinning is widely used in image processing and pattern recognition, it reduces redundancy of the original image and for easily extracting features. By a lot of experiments, a new improved thinning algorithm is proposed in this paper. A flow process diagram is given too. In the new algorithm, thinking about some details. For example, thing of P1 cant be deleted, thing of how to wipe off burr. The experiments show that these algorithms achieve anticipate results.

COMPUTING WITH LIGHT: TOWARD PARALLEL BOOLEAN ALGEBRA

2011 ◽  
Vol 22 (07) ◽  
pp. 1625-1637 ◽  
Author(s):  
TOM HEAD
Keyword(s):  
Boolean Algebra ◽  
Boolean Function ◽  
Parallel Algorithms ◽  
Solution Method ◽  
Basic Operation ◽  
Arbitrary Length ◽  
Color Problem ◽  
Future Technologies ◽  
Ultimate Purpose ◽  
Element Set

We design and implement highly parallel algorithms that use light as the tool of computation. Our computational laboratory consists of an ordinary xerox machine supplied with a box of transparencies. Our most basic operation is the evaluation of a Boolean function at arbitrarily many truth settings simultaneously. We find the maximum in a list of n-bit numbers of arbitrary length using at most n xerox copying steps. We count the number of elements in a list of arbitrary length of subsets of a given n-element set simultaneously in O(n2) copying steps. We decide, for any graph having n vertices and m edges, whether a 3-coloring exists in at most 2n + 4m copying steps. For large instances of problems such as the 3-color problem, this solution method may require the production of transparencies that display challengingly high densities of information. Our ultimate purpose here is to give hand tested 'ultra-parallel' algorithmic procedures that may provide useful suggestions for future technologies using light.

A NEW PARALLEL THINNING METHODOLOGY

1994 ◽  
Vol 08 (05) ◽  
pp. 999-1011 ◽  
Author(s):  
Y.Y. ZHANG ◽  
P.S.P. WANG
Keyword(s):  
Experimental Results ◽  
End Points ◽  
Thinning Algorithm ◽  
Break Points ◽  
Thinning Algorithms

A perfectly parallel thinning algorithm (PPTA) is proposed. It can generate perfect skeletons, which consist of end points, break points, and hole points only. Experimental results show that the proposed PPTA can also preserve image connectivity, produce thinner skeletons, and is faster than many existing thinning algorithms. For example, it is twice as fast as one of the fastest parallel thinning algorithm by Holt, Stewart, Clint and Perrorte.

MINIMAL NONSIMPLE SETS OF VOXELS IN BINARY IMAGES ON A FACE-CENTERED CUBIC GRID

1999 ◽  
Vol 13 (04) ◽  
pp. 485-502 ◽  
Author(s):  
C. J. GAU ◽  
T. Y. KONG
Keyword(s):  
Pattern Recognition ◽  
Cartesian Grid ◽  
Binary Image ◽  
Face Centered Cubic ◽  
Topology Preservation ◽  
Schlegel Diagram ◽  
Thinning Algorithm ◽  
Binary Images ◽  
Face Centered ◽  
Rhombic Dodecahedra

One can prove that a specified parallel thinning algorithm always preserves the topology of the input binary image by verifying that no iteration of that algorithm can ever delete a minimal non-simple ("MNS") set of 1's of an image. For binary images on a 3D face-centered cubic ("FCC") grid, we determine which sets of voxels can be MNS, and also determine which of those sets can be MNS without being a component of the 1's. These two problems are complicated by the fact that there are (at least) three reasonable ways of defining connectedness for sets of 1's and 0's in a binary image on an FCC grid, since one can: (a) use 18-connectedness for sets of 1's and 12-connectedness for sets of 0's; (b) use 12-connectedness both for sets of 1's and for sets of 0's; (c) use 12-connectedness for sets of 1's and 18-connectedness for sets of 0's. We solve the two problems in all three cases. The analogous problems for binary images on Cartesian grids were first solved by Ronse (in the 2D case) and Ma (in the 3D case). However, our treatment of simple 1's and MNS sets is rather different from theirs, in that it is based on the attachment sets of 1's in binary images. This concept was introduced in an earlier paper [T. Y. Kong, "On topology preservation in 2-D and 3-D thinning," Int. J. Pattern Recognition and Artificial Intelligence9 (1995) 813–844] and we use the same general approach to MNS sets as was used there. The voxels of an FCC grid are rhombic dodecahedra, which are rather more difficult to visualize and draw than the cubical voxels of a 3D Cartesian grid. An advantage of working with attachment sets is that such sets can be shown in a planar Schlegel diagram of a voxel, which is easy to draw.

scholarly journals Geometric Modelling of the Thinning by Cell Complexes

2019 ◽  
Vol 8 (2) ◽  
pp. 38
Author(s):  
Atefeh Hasan-Zadeh
Keyword(s):  
Pattern Recognition ◽  
Homotopy Theory ◽  
General Topology ◽  
Geometric Modelling ◽  
Active Area ◽  
Primary Role ◽  
Cell Complexes ◽  
Amount Of Information ◽  
Thinning Algorithms ◽  
Fixed Structure

Motivation: Thinning is an extremely active area of research because of its primary role in reducing the amount of information that must be processed by algorithms for pattern recognition. Most thinning algorithms are supposed to be topology-preserving, although an accurate statement of what this means is usually left unanswered.Results: The objective of this article is the presentation of a general topology via the concepts of homotopy theory to preserve the thinning. The proposed method can be applied to any decomposition of non-structural cells of the object, given that the cells have a fixed structure.  

Sign in / Sign up

Export Citation Format

Share Document