A novel single-pass thinning algorithm and an effective set of performance criteria

Intelligent Systems Laboratory, School of Applied Science, Nanyang Technological University, Nanyang Avenue, Singapore 2263, Singapore
Pattern Recognition Letters (Impact Factor: 1.55). 12/1995; 16(12):1267-1275. DOI: 10.1016/0167-8655(95)00078-X
Source: DBLP


A new sequential thinning algorithm, which uses both flag map and bitmap simultaneously to decide if a boundary pixel can be deleted, as well as the incorporation of smoothing templates to smooth the final skeleton, is proposed in this paper. Three performance measurements are proposed for an objective evaluation of this novel algorithm against a set of well established techniques. Extensive result comparison and analysis are presented in this paper for discussion.

Download full-text


Available from: Chai Quek
  • Source
    • "he binary images by using two pulse coupled Neural networks and claimed that in future the applications of their algorithm will be studied. They used filling technique to fill the region for the obtaining of the inner and outer image. The firing step obtains the thinned image which is decided and the final thinned image or the process is repeated.(Zhou et al. (1995) presented a novel thinning algorithm based on single pass. Bitmap and flag map are used concurrently for the decision of pixel to be deleted. Problems in existing algorithms and their solutions are presented and a smoothing template is also proposed for the smoothing of thinned image.Chiu and Tseng (1997) presents a handwritten Chinese "
    [Show abstract] [Hide abstract]
    ABSTRACT: Optical Character Recognition (OCR) is converting image based images into editable text so the text written in image form is available for editing purpose. The thinning technique can be applied in preprocessing stage or after segmentation of words and characters from a text image when features are extracted to differentiate the characters. Thinning is to decrease the thickness of strokes and finding out one pixel skeleton of the character image. In this paper we present an iterative and interactive thinning algorithm for Sindhi script step by step. Our thinning algorithm removes pixels by preserving connectivity and pattern of image intact. The process can be stopped and checked with pixel based editor for the connectivity patterns. This algorithm can be used with segmentation-based Sindhi OCR and segmentation-free OCR. The algorithm along with application is tested on Sindhi line text and individual characters and the results are presented. The algorithm and the application can also be applied with other language scripts. The presented work is a part of research done on Sindhi OCR.
    Full-text · Article · Oct 2015
  • Source
    • "While some researchers have developed sequential algorithms [6]–[9], the main focus is in parallel thinning algorithms [2], [10]–[13], which are efficient and fast. Raju and Xu [14] in their study of parallel thinning algorithms compared Zhang-Suen, Guo-Hall and One Pass Thinning Algorithm (OPTA) for character recognition. "

    Full-text · Conference Paper · Jan 2011
  • Source
    • "results in a black and white image with white pixels for bacteria and black pixels for background. The image is then thinned to single pixel width by applying two common skeletonization algorithms in sequence -firstly that of Zhou et al. [3], then that of Zhang and Suen [4]. Once this process has produced a skeleton image, algorithms are applied to identify tip points and branch points. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Streptomycetes are soil-based bacteria which exhibit fungal-like filamentous growth patterns and are an important source of medic- inal antibiotics. Despite their usefulness, there are large gaps in our understanding of the mechanisms of growth and branching that they employ. Our research has focussed on analysis and mod- elling of the early stage morphology of the model organism Strep- tomyces coelicolor. To enable this work, we have developed a software tool to provide automatic analysis of microscope images of filamentous microbes. In this paper, we describe the techniques employed in the tool and present some early results from experi- ments we have carried out to quantify the differences between the wild-type and a number of mutants developed at UEA.
    Full-text · Article · Jul 2009
Show more