A modified fast parallel algorithm for thinning digital patterns

Chen, Yung‐Sheng; Hsu, Wen‐Hsing

doi:10.1016/0167-8655(88)90124-9

Cited by 100 publications

(35 citation statements)

References 7 publications

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“…A survey of 2D thinning algorithms and their applications can be found in [1]. Early thinning algorithms were designed for serial implementations, but since parallel computers are available several approaches have been developed with parallel processing in mind [2][3][4][5][6]. Some implementations on parallel computers have been reported in [7] for a shared memory architecture and in [8] for massively SIMD machines.…”

Section: Introductionmentioning

confidence: 99%

Parallel thinning algorithms on multicomputers: experimental study on load balancing

Montoya

Gil

Garcı́a

2000

Concurrency: Pract. Exper.

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SUMMARYIn this work, a practical implementation of two parallel thinning algorithms on a multicomputer system are described. The solution has been conceived for a multiprocessor using the SPMD (single program multiple data) programming model. Our main goal is intended to describe our experiences on data partition/distribution among processors for parallel thinning algorithms as a representative type of algorithm where communications take place between neighbor processors and the work load for each processor depends on the input data. It will be shown how the efficiency of the parallel implementation can be improved through the application of a preprocess. This preprocess is based on the analysis of the work load balance. An analysis of the communication cost is also made. Although the results shown here are concerned with the implementations of two parallel thinning algorithms we think that our proposal about data distribution is general and useful for a wide set of algorithms in the field of image processing. Copyright

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Section: Introductionmentioning

confidence: 99%

Parallel thinning algorithms on multicomputers: experimental study on load balancing

Montoya

Gil

Garcı́a

2000

Concurrency: Pract. Exper.

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“…In this study, we used the Zhang-Suen thinning method [13] because this method has good efficiency and is fast in obtaining the skeleton. The next process is to extract features or characteristics that are able to distinguish between letters, one of which is shape energy.…”

Section: Feature Extractionmentioning

confidence: 99%

Javanese Character Feature Extraction Based on Shape Energy

Wibowo¹,

Sigit²,

Barakbah³

2017

emitter

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Javanese characters is part of Javanese culture, which is one of Indonesia's noble culture. However, the number of Javanese people who are able to read the letters has decreased so that there needs some effort in the form of a system that is able to recognize the characters. One solution to this problem lies in Optical Character Recognition (OCR) studies, where one of its heaviest points lies in feature extraction (which is) to distinguish each character. Shape Energy is one of feature extraction methods with the basic idea of how the character can be distinguished simply through its skeleton. Based on the basic idea, then the development of feature extraction is done based on its components to produce an angular histogram with various variations of multiples angle. Furthermore, the performance test of this method and its basic method is conducted in Javanese character dataset, which has been obtained from various images, is 387 data with 19 labels by using K-Nearest Neighbors as its classification method. Performance values were obtained based on the accuracy which is generated through the Cross-Validation process of 81.90% in the angular histogram with an angle of 20 degrees, 27.32% better performance than using Shape Energy. In addition, other test results show that this method is able to recognize rotated character with the lowest performance value of 87.73% at 180-degree rotation and the highest performance value of 96.21% at 90-degree rotation. It can be concluded that this method is able to improve the performance of Shape Energy in the form of recognition of Javanese characters as well as robust to the rotation.

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“…The first cycle will be executed at odd iterations while the second cycle will be executed at even iterations. A detailed explanation of the implementation of these two subcycles is given in [54].…”

Section: ) Edge Thinningmentioning

confidence: 99%

“…The fast parallel thinning algorithm by Chen and Hsu [54] was used here, because this algorithm preserves the merits of the original such as the edge noise immunity and good effect in thinning crossing lines, and overcomes the weaknesses such as the serious shrinking and line connectivity problems. A 3 3 window is used (Fig.…”

Section: ) Edge Thinningmentioning

confidence: 99%

Information fusion for rural land-use classification with high-resolution satellite imagery

Sun

Heidt²,

Gong

et al. 2003

IEEE Trans. Geosci. Remote Sensing

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Abstract-We propose an information fusion method for the extraction of land-use information based on both the panchromatic and multispectral Indian Remote Sensing Satellite 1C (IRS-1C) satellite imagery. It integrates spectral, spatial and structural information existing in the image. A thematic map was first produced with a maximum-likelihood classification (MLC) applied to the multispectral imagery. Probabilistic relaxation (PR) was then performed on the thematic map to refine the classification with neighborhood information. Furthermore, we incorporated edges extracted from the higher resolution panchromatic imagery in the classification. An edge map was generated using operations such as edge detection, edge thresholding and edge thinning. Finally, a modified region-growing approach was used to improve image classification. The procedure proved to be more effective in land-use classification than conventional methods based only on multispectral data. The improved land-use map is characterized with sharp interregional boundaries, reduced number of mixed pixels and more homogeneous regions. The overall kappa statistics increased considerably from 0.52 before the fusion to 0.75 after.

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A modified fast parallel algorithm for thinning digital patterns

Cited by 100 publications

References 7 publications

Parallel thinning algorithms on multicomputers: experimental study on load balancing

Parallel thinning algorithms on multicomputers: experimental study on load balancing

Javanese Character Feature Extraction Based on Shape Energy

Information fusion for rural land-use classification with high-resolution satellite imagery

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