Shortest routes on varying height surfaces using gray-level distance transforms

Ikonen, Leena; Toivanen, Pekka

doi:10.1016/j.imavis.2004.06.010

Cited by 33 publications

(22 citation statements)

References 17 publications

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“…Alternative digital distance transforms approximating spatial-tonal Euclidean distance or L 1 distance and efficient algorithms are discussed in [15,16].…”

Section: The Discrete Amoeba Constructionmentioning

confidence: 99%

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Morphological Amoebas Are Self-snakes

2010

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This paper is concerned with amoeba median filtering, a structureadaptive morphological image filter. It has been introduced by Lerallut et al. in a discrete formulation. Experimental evidence shows that iterated amoeba median filtering leads to segmentation-like results that are similar to those obtained by self-snakes, an image filter based on a partial differential equation. We establish this correspondence by analysing a space-continuous formulation of iterated median filtering. We prove that in the limit of vanishing radius of the structuring elements, iterated amoeba median filtering indeed approximates the partial differential equation of self-snakes. This result holds true under very general assumptions on the metric used to construct the amoebas. We present experiments with discrete iterated amoeba median filtering that confirm qualitative and quantitative predictions of our analysis.

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“…Alternative digital distance transforms approximating spatial-tonal Euclidean distance or L 1 distance and efficient algorithms are discussed in [15,16].…”

Section: The Discrete Amoeba Constructionmentioning

confidence: 99%

“…With regard to future improvements in the algorithmic realisation of amoeba filters, we mention also digital distance transforms, in particular the work by Borgefors [3,4] and Ikonen et al [15,16].…”

Section: Introductionmentioning

confidence: 99%

Morphological Amoebas Are Self-snakes

2010

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“…The minimization is performed using the Dijkstra's algorithm [63]. In addition, the threshold T P β = 0.3 is used as proposed in [57], which prevents propagating to the regions of very low skin probability.…”

Section: 'Skinness' Propagationmentioning

confidence: 99%

Self-adaptive algorithm for segmenting skin regions

Kawulok

Nalepa

et al. 2014

EURASIP J. Adv. Signal Process.

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In this paper, we introduce a new self-adaptive algorithm for segmenting human skin regions in color images. Skin detection and segmentation is an active research topic, and many solutions have been proposed so far, especially concerning skin tone modeling in various color spaces. Such models are used for pixel-based classification, but its accuracy is limited due to high variance and low specificity of human skin color. In many works, skin model adaptation and spatial analysis were reported to improve the final segmentation outcome; however, little attention has been paid so far to the possibilities of combining these two improvement directions. Our contribution lies in learning a local skin color model on the fly, which is subsequently applied to the image to determine the seeds for the spatial analysis. Furthermore, we also take advantage of textural features for computing local propagation costs that are used in the distance transform. The results of an extensive experimental study confirmed that the new method is highly competitive, especially for extracting the hand regions in color images.

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“…The classical, binary DTs are useful, for example, for measurement and description of binary objects. Distance functions and transforms that are defined as minimal cost paths in general images include geodesic distance [9], fuzzy distances [19], and the minimax cost function; they have been used, for example, in segmentation and saliency detection [18]. The most common algorithms for computing distance transforms are: (i) raster scanning methods, where distance values are propagated by sequentially scanning the image in a pre-defined order [1,5]; (ii) wave-front propagation methods, where distance values are propagated from low distance value points (the object border) to the points with higher distance values, using data structure containing previously visited points and their corresponding distance values, until all points have been visited [15,21]; and (iii) separable algorithms, where one-dimensional subsets of the image are scanned separately, until all principal directions have been scanned [4,20].…”

Section: Introductionmentioning

confidence: 99%

The Minimum Barrier Distance: A Summary of Recent Advances

Strand

Ciesielski

Malmberg

et al. 2017

Discrete Geometry for Computer Imagery

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Abstract. In this paper we present an overview and summary of recent results of the minimum barrier distance (MBD), a distance operator that is a promising tool in several image processing applications. The theory constitutes of the continuous MBD in R n , its discrete formulation in Z n (in two different natural formulations), and of the discussion of convergence of discrete MBDs to their continuous counterpart. We describe two algorithms that compute MBD, one very fast but returning only approximate MBD, the other a bit slower, but returning the exact MBD. Finally, some image processing applications of MBD are presented and the directions of potential future research in this area are indicated.

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Shortest routes on varying height surfaces using gray-level distance transforms

Cited by 33 publications

References 17 publications

Morphological Amoebas Are Self-snakes

Morphological Amoebas Are Self-snakes

Self-adaptive algorithm for segmenting skin regions

The Minimum Barrier Distance: A Summary of Recent Advances

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