Picture Processing by Computer

Rosenfeld, Azriel

doi:10.1145/356551.356554

Cited by 230 publications

(76 citation statements)

References 133 publications

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“…The technique these authors describe is essentially a Radon transform. Rosenfeld [14] describes this technique (for straight lines) in Section 8.4.e "Coordinate Conversion". Neither [13] nor [14] identify this technique as the Radon transform.…”

Section: The Radon Transformmentioning

confidence: 99%

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The generalized Radon transform: Sampling, accuracy and memory considerations

Hendriks

Ginkel

Verbeek

et al. 2005

Pattern Recognition

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The generalized Radon (or Hough) transform is a well-known tool for detecting parameterized shapes in an image. The Radon transform is a mapping between the image space and a parameter space. The coordinates of a point in the latter correspond to the parameters of a shape in the image. The amplitude at that point corresponds to the amount of evidence for that shape. In this paper we discuss three important aspects of the Radon transform. The first aspect is discretization. Using concepts from sampling theory we derive a set of sampling criteria for the generalized Radon transform. The second aspect is accuracy. For the specific case of the Radon transform for spheres, we examine how well the location of the maxima matches the true parameters. We derive a correction term to reduce the bias in the estimated radii. The third aspect concerns a projection-based algorithm to reduce memory requirements.

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Section: The Radon Transformmentioning

confidence: 99%

“…Rosenfeld [14] describes this technique (for straight lines) in Section 8.4.e "Coordinate Conversion". Neither [13] nor [14] identify this technique as the Radon transform. Our study of the discretization of the Radon transform in the next section also applies to the popular Hough transform which is often used for shape detection.…”

Section: The Radon Transformmentioning

confidence: 99%

The generalized Radon transform: Sampling, accuracy and memory considerations

Hendriks

Ginkel

Verbeek

et al. 2005

Pattern Recognition

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“…Contextual transformations are operations in which the outcome depends on a modified pixel and its surroundings [4,5]. Alternatively, the name "digital filters" is also used.…”

Section: Introductionmentioning

confidence: 99%

“…The general concept of the context filters is to create the output image on the basis of the source image in such a way that the value of each pixel at coordinates (x,y) in the output image is determined based on a certain neighborhood of the pixel at coordinates (x,y) in the source image [4][5][6][7][8][9]. This neighborhood is defined as a filter window.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Kuwahara filter

Bartyzel

2015

SIViP

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A new filter was created by improving the standard Kuwahara filter. It allows more efficient noise reduction without blurring the edges and image preparation for segmentation and further analyses operations. One of the biggest and most common restrictions encountered in filter algorithms is the need for a declarative definition of the filter window size or the number of iterations that an operation should be repeated. In the case of the proposed solution, we are dealing with automatic adaptation of the algorithm to the local environment of each pixel in the processed image.

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“…One technique that has played an important role in the development of pattern matching is the Hough transform (HT). This technique was originally developed to nd bubble tracks rather than shapes in images (Hough 1962) and its potential as a coordinate conversion algorithm capable of line extraction was popularized by Rosenfeld (1969). Since then, the HT has been the subject of extensive research (Illingworth & Kittler 1988;Leavers 1993) which has aimed primarily to increase its generality (the complexity of the shapes that can be extracted) while avoiding the large requirements of time and memory inherent in its implementation.…”

Section: Introductionmentioning

confidence: 99%