Morphological operators for color image processing based on Mahalanobis distance measure

Al-Otum, Hazem Munawer

doi:10.1117/1.1594727

Cited by 28 publications

(15 citation statements)

References 16 publications

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“…13, 16, and 17. ͑Since MCD-MOA 17 gives better results than CC-MOA, the comparison is provided in respect to MCD-MOA.͒ Simulations were carried out for the main color morphological operators when applied to the test images. Some of these results are depicted in Fig.…”

Section: Results Of Implementing the Main Morphological Operatorsmentioning

confidence: 99%

“…The psychophysical procedure has been organized as follows. 17 • The used original resulting images were subjectively evaluated by 25 observers: 5 specialists in image processing, 5 engineers with some background in image processing, and 15 undergraduate students that have passed a course in image processing. Before starting the procedure, the participants were briefly told about the concept of image morphology.…”

Section: Results Of Implementing the Main Morphological Operatorsmentioning

confidence: 99%

“…These operators were extended to operate on nonbinary signals by Serra 8,9 and others. 3,4,[10][11][12][13][14][15][16][17][18][19] Mathematical morphology can be described geometrically, in terms of the actions of the operators on binary or grayscale images. The geometric descriptions depend on small synthetic images called structuring elements.…”

Section: Introductionmentioning

confidence: 99%

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Color image morphology using an adaptive saturation-based technique

Al-Otum

Uraikat

2004

Opt. Eng

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An adaptive algorithm that implements a saturation-based ordering (for color image morphology) is introduced. The adaptation is achieved using a tradeoff parameter in the form of a nonlinear function of the local saturation. To evaluate the performance of the proposed algorithm, a deigned psychophysical experiment is used to derive a metric denoted as the average value for the psychophysical evaluation in percent (APE%). Results of implementing the proposed APE show that an APEϭ73 to 96% can be achieved for basic morphological operators, i.e., dilation, erosion, opening, and closing. APE value depends on the size and shape of the structuring element as well as on the image details. The proposed algorithm has also been extended to other morphological operators, such as image smoothing (noise suppression), top hat, gradient, and Laplacian operators. In the case of a smoothing operation, an average peak signal-to-noise ratio (PSNR)ϭ31 to 37 dB is achieved at various structuring elements and applied noise variances, while good results are achieved with the proposed top-hat operators.

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Section: Results Of Implementing the Main Morphological Operatorsmentioning

confidence: 99%

Section: Results Of Implementing the Main Morphological Operatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Color image morphology using an adaptive saturation-based technique

Al-Otum

Uraikat

2004

Opt. Eng

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“…The R-ordering has been also used to build morphological operators by means of Euclidean distances by Comer and Delp [15] and by Ortiz et al [36]; or using Mahalanobis distances by Goutsias et al [17], and by Al-Otum [1]. Sartor and Weeks [55,43] proposed a combination of an R-ordering and a C-ordering; in fact, our present approach can be considered as a generalisation of this interesting study.…”

Section: Introductionmentioning

confidence: 88%

Morphological colour operators in totally ordered lattices based on distances: Application to image filtering, enhancement and analysis

Angulo

2007

Computer Vision and Image Understanding

116

111

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“…For instance, Mahalanobis distance has been employed in several works on multivariate morphology including the information from a reference set [10]. Recently, reduced supervised ordering has shown be useful in the analysis of high dimensional images [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Random Projection Depth for Multivariate Mathematical Morphology

Velasco-Forero

Angulo

2012

IEEE J. Sel. Top. Signal Process.

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Abstract-The open problem of the generalization of mathematical morphology to vector images is handled in this paper using the paradigm of depth functions. Statistical depth functions provide from the "deepest" point a "center-outward ordering" of a multidimensional data distribution and they can be therefore used to construct morphological operators. The fundamental assumption of this data-driven approach is the existence of "background/foreground" image representation. Examples in real color and hyperspectral images illustrate the results.Index Terms-Hyperspectral images, multivariate morphology, statistical depth function.

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Morphological operators for color image processing based on Mahalanobis distance measure

Cited by 28 publications

References 16 publications

Color image morphology using an adaptive saturation-based technique

Color image morphology using an adaptive saturation-based technique

Morphological colour operators in totally ordered lattices based on distances: Application to image filtering, enhancement and analysis

Random Projection Depth for Multivariate Mathematical Morphology

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