A Width-Independent Fast Thinning Algorithm

Arcelli, Carlo; Baja, Gabriella Sanniti di

doi:10.1109/tpami.1985.4767685

Cited by 273 publications

(90 citation statements)

References 13 publications

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“…Skeletal abstraction is a difficult problem that has been greatly studied over years; and obviously it is out of the paper scope. Briefly saying, existing methods for extracting skeletons concern broad research areas comprising topological thinning algorithms (Arcelli and Baja, 1985;Lee and Kashyap, 1994;Borgefors et al, 1999;Bertrand and Couprie, 2009) where Blum grassfire transform (Blum, 1973) were used, curve evolution, variational and wavefront propagation methods (Leymarie and Levine, 1992;Geiger et al, 2003;Tek and Kimia, 2003), Voronoi diagram (Schmitt, 1989;Ogniewicz, 1993;Sheehy et al, 1996), and methods using Euclidean distance function computed for example with the Eikonal equation or Hamilton-Jacobi systems (Siddiqi et al, 2002;Torsello and Hancock, 2003;2004). For more information on that subject, interested readers can have a look on those references.…”

Section: Numerical Resultsmentioning

confidence: 99%

Multiscale Image Analysis Based on Robust and Adaptive Morphological Scale-Spaces

Diop

Angulo

2014

Image Anal Stereol

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Mathematical morphology is a powerful tool for image analysis; however, classical morphological operators suffer from lacks of robustness against noise, and also intrinsic image features are not accounted at all in the process. We propose in this work a new and different way to overcome such limits, by introducing both robustness and locally adaptability in morphological operators, which are now defined in a manner such that intrinsic image features are accounted. Dealing with partial differential equations (PDEs) for generalized Cauchy problems, we show that proposed PDEs are equivalent to impose robustness and adaptability of corresponding sup-inf operators, to structuring functions. Accurate numerical schemes are also provided to solve proposed PDEs, and experiments conducted for both synthetic and real images, show the efficiency and robustness of our approach.

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Section: Numerical Resultsmentioning

confidence: 99%

Multiscale Image Analysis Based on Robust and Adaptive Morphological Scale-Spaces

Diop

Angulo

2014

Image Anal Stereol

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“…A simple way to detect the minutiae is using the crossing number algorithm [11]. Once a binary skeleton of a fingerprint image has been obtained, an image scan allows the minutiae detection and the pixels corresponding to minutiae are characterized by a crossing number different from 2.…”

Section: Minutiae Extractionmentioning

confidence: 99%

A Fingerprint Matching Algorithm Based On Minutiae And Local Ridge Orientation

Salmento¹,

Xavier²,

Peralva³

et al. 2016

Anais Do 10. Congresso Brasileiro De Inteligência Computacional

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Resumo -Este artigo apresenta um novo método de reconhecimento de impressões digitais baseado nas informações das minúcias extraídas e do campo de orientação da imagem, em que uma rede neural artificialé usada para realizar o casamento. O método proposto foi implementado em software e seu desempenho foi avaliado. O método proposto apresentou um bom desempenho para o banco de dados composto por imagens selecionadas do banco de impressões digitais de referência FVC2000.Palavras-chave -Biometria, Impressão Digital, Extração de Minúcias, Redes Neurais.Abstract -This paper presents a new method for fingerprint recognition based on information extracted from the minutiae and the orientation field from the image that uses an artificial neural network as a matching algorithm. The proposed method was implemented by software and its performance was evaluated.The proposed method presented a good performance for the data set used in this work which is composed by selected images from the FVC2000 database.

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“…The basic idea is to detect intersection points only on the map that has been pre-processed by line thinning algorithms and noise-removal procedures. In particular, the process can be divided into the following subtasks: (1) isolate map data by a threshold, (2) decrease line width by thinning algorithms, such as [3], (3) recognize intersection points by crossing number (CN), the number of lines emanating from an intersection point [3], (4) remove misidentified intersections caused by noisy information (such as symbols and text). The details of the line intersections detection algorithm are discussed in [19].…”

Section: Figure 3: Intersection Points Automatically Detected On Imagerymentioning

confidence: 99%

“…The National Map 1 , ESRI Map Service 2 , MapQuest 3 , University of Texas Map Library 4 , Microsoft TerraService 5 , and Space Imaging 6 are good examples of map or imagery repositories. In addition, a wide variety of maps are available from various government agencies, such as property survey maps and maps of oil and natural gas fields.…”

Section: Introductionmentioning

confidence: 99%

Automatically and accurately conflating orthoimagery and street maps

Chen

Knoblock

Shahabi

et al. 2004

Proceedings of the 12th Annual ACM International Workshop on Geographic Information Systems

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Recent growth of the geospatial information on the web has made it possible to easily access various maps and orthoimagery. By integrating these maps and imagery, we can create intelligent images that combine the visual appeal and accuracy of imagery with the detailed attribution information often contained in diverse maps. However, accurately integrating maps and imagery from different data sources remains a challenging task. This is because spatial data obtained from various data sources may have different projections and different accuracy levels. Most of the existing algorithms only deal with vector to vector spatial data integration or require human intervention to accomplish imagery to map conflation. In this paper, we describe an information integration approach that utilizes common vector datasets as "glue" to automatically conflate imagery with street maps. We present efficient techniques to automatically extract road intersections from imagery and maps as control points. We also describe a specialized point pattern matching algorithm to align the two point sets and conflation techniques to align the imagery with maps. We show that these automatic conflation techniques can automatically and accurately align maps with images of the same area. In particular, using the approach described in this paper, our system automatically aligns a set of TIGER maps for an area in El Segundo, CA to the corresponding orthoimagery with an average error of 8.35 meters per pixel. This is a significant improvement considering that simply combining the TIGER maps with the corresponding imagery based on geographic coordinates provided by the sources results in error of 27 meters per pixel.

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A Width-Independent Fast Thinning Algorithm

Cited by 273 publications

References 13 publications

Multiscale Image Analysis Based on Robust and Adaptive Morphological Scale-Spaces

Multiscale Image Analysis Based on Robust and Adaptive Morphological Scale-Spaces

A Fingerprint Matching Algorithm Based On Minutiae And Local Ridge Orientation

Automatically and accurately conflating orthoimagery and street maps

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