A parallel thinning algorithm with two-subiteration that generates one-pixel-wide skeletons

Zhang, Y.Y.; Wang, P.S.P.

doi:10.1109/icpr.1996.547608

Cited by 36 publications

(19 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…16 that the image produced by our noise removal method gives clean skeleton image after performing thinning operation [14] compared to other methods. It is worth noting that in our previous work [15] we have proven that cleaning the images using the proposed noise removal method (called Algorithm B in the work) before performing thinning-based raster to vector conversion using commercial vectorization application gives better quality of vector data compared to other methods.…”

Section: Discussionmentioning

confidence: 90%

Enhancing Salt-and-Pepper Noise Removal in Binary Images of Engineering Drawing

Al-Khaffaf

Talib

Salam

2009

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYNoise removal in engineering drawing is an important operation performed before other image analysis tasks. Many algorithms have been developed to remove salt-and-pepper noise from document images. Cleaning algorithms should remove noise while keeping the real part of the image unchanged. Some algorithms have disadvantages in cleaning operation that leads to removing of weak features such as short thin lines. Others leave the image with hairy noise attached to image objects. In this article a noise removal procedure called TrackAndMayDel (TAMD) is developed to enhance the noise removal of salt-and-pepper noise in binary images of engineering drawings. The procedure could be integrated with third party algorithms' logic to enhance their ability to remove noise by investigating the structure of pixels that are part of weak features. It can be integrated with other algorithms as a post-processing step to remove noise remaining in the image such as hairy noise attached with graphical elements. An algorithm is proposed by incorporating TAMD in a third party algorithm. Real scanned images from GREC'03 contest are used in the experiment. The images are corrupted by salt-and-pepper noise at 10%, 15%, and 20% levels. An objective performance measure that correlates with human vision as well as MSE and PSNR are used in this experiment. Performance evaluation of the introduced algorithm shows better-quality images compared to other algorithms. key words: salt/pepper noise removal, impulsive noise, engineering drawing, binary image, document analysis and recognition

show abstract

Section: Discussionmentioning

confidence: 90%

Enhancing Salt-and-Pepper Noise Removal in Binary Images of Engineering Drawing

Al-Khaffaf

Talib

Salam

2009

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

show abstract

“…Figure 4 shows how to compute the force at a pixel location and the vector sum of forces. Zhang and Wang (1996) This algorithm actually represents a modification to Guo and Hall's approach. In an iterative process the deletion decision is based on eleven 3×3 thinning templates.…”

Section: General Ideas and Examplesmentioning

confidence: 99%

“…He compared it (Fig. 6) with Zhang's algorithm given in (Zhang and Wang, 1996). ART2 showed higher data-reduction efficiency and much simpler skeletons with less noise spurs and reduced time complexity.…”

Section: General Ideas and Examplesmentioning

confidence: 99%

“…The overview of the algorithm is presented in Fig. 17 Guo and Hall (1989) iterative, 2-subcycles does not give a one-pixel-wide skeleton 1989 Chen and Hsu (1989a;1989b) 1-subcycle, parallel bias-reduced skeletons 1994 Parker et al (1994) force-based acquires a skeleton by using force vectors 1996 Zhang and Wang (1996) 2-subcycles, parallel preserves connectivity, produces one-pixel-wide skeletons 1998 Altuwaijri and Bayoumi (1998) …”

Section: Algorithm Descriptionmentioning

confidence: 99%

“…K3M has the following feasible advantages: (Guo and Hall, 1992) Zhang's (Zhang and Wang, 1996) A-W (Ahmed and Ward, 2002) KMM K3M 1. The algorithm preserves right angles at the lines interconnections, which result in better correspondence between the original and the modified image.…”

Section: Advantages and Drawbacks Of K3mmentioning

confidence: 99%

See 2 more Smart Citations

K3M: A universal algorithm for image skeletonization and a review of thinning techniques

Saeed¹,

Tabędzki²,

Rybnik³

et al. 2010

International Journal of Applied Mathematics and Computer Science

169

View full text Add to dashboard Cite

This paper aims at three aspects closely related to each other: first, it presents the state of the art in the area of thinning methodologies, by giving descriptions of general ideas of the most significant algorithms with a comparison between them. Secondly, it proposes a new thinning algorithm that presents interesting properties in terms of processing quality and algorithm clarity, enriched with examples. Thirdly, the work considers parallelization issues for intrinsically sequential algorithms of thinning. The main advantage of the suggested algorithm is its universality, which makes it useful and versatile for a variety of applications.

show abstract

Topology preserving parallel thinning algorithms

Németh

Palágyi

2011

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Thinning is an iterative object reduction technique for extracting medial curves from binary objects. During a thinning process, some border points that satisfy certain topological and geometric constraints are deleted in iteration steps. Parallel thinning algorithms are composed of parallel reduction operators that delete a set of object points simultaneously. This article presents 21 parallel thinning algorithms for (8,4) binary pictures that are derived from the sufficient conditions for topology preservation accommodated to the three parallel thinning approaches.

show abstract

A parallel thinning algorithm with two-subiteration that generates one-pixel-wide skeletons

Cited by 36 publications

References 7 publications

Enhancing Salt-and-Pepper Noise Removal in Binary Images of Engineering Drawing

Enhancing Salt-and-Pepper Noise Removal in Binary Images of Engineering Drawing

K3M: A universal algorithm for image skeletonization and a review of thinning techniques

Topology preserving parallel thinning algorithms

Contact Info

Product

Resources

About