Hexadecagonal region growing

Coiras, E.; Santamaría, Javier; Miravet, Carlos

doi:10.1016/s0167-8655(98)00091-9

Cited by 12 publications

(19 citation statements)

References 13 publications

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“…Fig. (2) presents some DIs of a single pixel (in the center of the image; look closely) as well as their deviation from the ED [1,2,12,[20][21][22][23][24]. So, as is also illustrated in both Fig.…”

Section: Introductionmentioning

confidence: 83%

“…An input image consisting of some simple test objects (# A1) and their true Euclidean distance image (EDI; # C1). The four rows below this, visualize the distance transforms (DT) # 2: city block [1,2], # 3: chamfer 3-4 [21,22], # 4: hexadecagonal region growing [23], and # 5: Shih and Wu's Euclidean DT [24]. From left to right, the columns indicate: # A: resulting distance image (DI R ), # B: absolute difference with EDI (i.e., |EDI − DI R |), and # C: relative difference with the EDI (i.e., |EDI − DI R |/EDI).…”

Section: Distance Transformmentioning

confidence: 99%

“…Of course, a range of other measures can be defined. Regrettably, in most papers, the error measure is not defined; an exception to this is [23]. In this paper, the error measures are made explicit; see also Tables 1-3.…”

Section: Distance Transformmentioning

confidence: 99%

“…but do not provide an algorithm for it. Coiras et al [23] analyzed this hexagonal DT and, subsequently, provided an algorithm for the empirical hexagonal growth presented in [58].…”

Section: On More Than 30 Years Of Researchmentioning

confidence: 99%

“…As such, it approximates the EDT better than the chamfer 5-7-11 model, introduced in [21,22] that served as the 'standard' for hexadecagonal distance for more than a decade. Coiras et al's algorithm for hexadecagonal growth [23] is defined as follows:…”

Section: On More Than 30 Years Of Researchmentioning

confidence: 99%

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Distance Transforms: Academics Versus Industry

Broek

Schouten²

2011

CSENG

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Abstract:In image and video analysis, distance transformations (DT) are frequently used. They provide a distance image (DI) of background pixels to the nearest object pixel. DT touches upon the core of many applications; consequently, not only science but also industry has conducted a significant body of work in this field. However, in a vast majority of the cases this has not been published in major scientific outlets but has been filed as a patent application. This article provides a brief introduction into DT, including a specification of a few of the most prominent algorithms in the field. Next, a few interesting algorithms from the last decade are discussed. A benchmark including eight DT algorithms (i.e., city block, Danielsson's algorithm, chamfer 3-4, hexadecagonal region growing, a recent claimed true Euclidean DT, and three exact Euclidean DT) has been executed, which illustrates the intriguing complexity of DT in terms of precision and computational complexity. Subsequently, a selection of key patent applications are discussed that have emerged in this field, including their scientific merit and areas of application. Finally, this article's findings are summarized and discussed, with an emphasis on both the common ground of scientific articles and patent applications as well as the added value they can have to each other.

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Section: Introductionmentioning

confidence: 83%

Section: Distance Transformmentioning

confidence: 99%

Section: Distance Transformmentioning

confidence: 99%

“…but do not provide an algorithm for it. Coiras et al [23] analyzed this hexagonal DT and, subsequently, provided an algorithm for the empirical hexagonal growth presented in [58].…”

Section: On More Than 30 Years Of Researchmentioning

confidence: 99%

Section: On More Than 30 Years Of Researchmentioning

confidence: 99%

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Distance Transforms: Academics Versus Industry

Broek

Schouten²

2011

CSENG

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Image Analysis and Computer Vision: 1998

Rosenfeld

1999

Computer Vision and Image Understanding

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This paper presents a bibliography of over 2250 references related to computer vision and image analysis, arranged by subject matter. The topics covered include computational techniques; feature detection and segmentation; image and scene analysis; two-dimensional shape; pattern; color and texture; matching and stereo; three-dimensional recovery and analysis; three-dimensional shape; and motion. A few references are also given on related topics, including geometry and graphics, compression and processing, sensors and optics, visual perception, neural networks, artificial intelligence and pattern recognition, as well as on applications.

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Lambertian-based adversarial attacks on deep-learning-based underwater side-scan sonar image classification

Jiang

2023

Pattern Recognition

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Hexadecagonal region growing

Cited by 12 publications

References 13 publications

Distance Transforms: Academics Versus Industry

Distance Transforms: Academics Versus Industry

Image Analysis and Computer Vision: 1998

Lambertian-based adversarial attacks on deep-learning-based underwater side-scan sonar image classification

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