A clustering method based on the estimation of the probability density function and on the skeleton by influence zones. Application to image processing

Herbin, Michel; Bonnet, N.; Vautrot, Philippe

doi:10.1016/0167-8655(96)00085-2

Cited by 48 publications

(24 citation statements)

References 12 publications

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“…The Maximum Likelihood method is at least as good as ISODATA on its own for the yellow, green, fuchsia, and orange classes in Table 5. One artifact of the ISODATA algorithm is the straight line delineation between classes due to the Euclidean distance measure used by the algorithm to partition the data [119,152]. These boundaries cut across contours and are not a natural division within the data.…”

Section: Resultsmentioning

confidence: 99%

An Alternative Approach to Mapping Thermophysical Units from Martian Thermal Inertia and Albedo Data Using a Combination of Unsupervised Classification Techniques

et al. 2014

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Thermal inertia and albedo provide information on the distribution of surface materials on Mars. These parameters have been mapped globally on Mars by the Thermal Emission Spectrometer (TES) onboard the Mars Global Surveyor. Two-dimensional clusters of thermal inertia and albedo reflect the thermophysical attributes of the dominant materials on the surface. In this paper three automated, non-deterministic, algorithmic classification methods are employed for defining thermophysical units: Expectation Maximisation of a Gaussian Mixture Model; Iterative Self-Organizing Data Analysis Technique (ISODATA); and Maximum Likelihood. We analyse the behaviour of the OPEN ACCESS Remote Sens. 2014, 6 5185 thermophysical classes resulting from the three classifiers, operating on the 2007 TES thermal inertia and albedo datasets. Producing a rigorous mapping of thermophysical classes at ~3 km/pixel resolution remains important for constraining the geologic processes that have shaped the Martian surface on a regional scale, and for choosing appropriate landing sites. The results from applying these algorithms are compared to geologic maps, surface data from lander missions, features derived from imaging, and previous classifications of thermophysical units which utilized manual (and potentially more time consuming) classification methods. These comparisons comprise data suitable for validation of our classifications. Our work shows that a combination of the algorithms-ISODATA and Maximum Likelihood-optimises the sensitivity to the underlying dataspace, and that new information on Martian surface materials can be obtained by using these methods. We demonstrate that the algorithms used here can be applied to define a finer partitioning of albedo and thermal inertia for a more detailed mapping of surface materials, grain sizes and thermal behaviour of the Martian surface and shallow subsurface, at the ~3 km scale.

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Section: Resultsmentioning

confidence: 99%

An Alternative Approach to Mapping Thermophysical Units from Martian Thermal Inertia and Albedo Data Using a Combination of Unsupervised Classification Techniques

et al. 2014

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“…Sobel edge detection algorithm in extracting the edge of the algorithm is effect is good method, in this article chooses the algorithm for the background image edges and simulation rendering [8][9][10][11].…”

Section: The Simulation Results and Analysismentioning

confidence: 99%

Design of Camouflage Pattern Based on Mathematical Morphology

Jia¹,

Yang²,

Xu³

et al. 2017

Proceedings of the 2017 International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2017)

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Abstract-Camouflage as one of the effective way that against modern detection and precision strike and improving the survival ability of military targets is important content 0f combat. The digital camouflage can break the boundary of a camouflaged target between different colors, and make it blurred and broken, So as to attain a better camouflage effect. This paper proposes a new algorithm of camouflage design. It would first extract the main color after the transformation of color space, then use the Mathematical Morphology to delete the small spots in the picture, finally create digital camouflage with the color clustering way to replace the similar color with the selected main color. The experimental result shows that, the camouflage is easier to blend with the natural background than the traditional camouflage, and achieves a good effect of the optical camouflage.

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“…[4,9] as well as many interesting papers on a subject similar to the methodology used here, e.g. [8,17,23,24].…”

Section: Introductionmentioning

confidence: 99%

The Complete Gradient Clustering Algorithm: properties in practical applications

Kulczycki

Charytanowicz

Kowalski

et al. 2012

Journal of Applied Statistics

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The aim of this paper is to present a Complete Gradient Clustering Algorithm, its applicational aspects and properties, as well as to illustrate them with specific practical problems from the subject of bioinformatics (the categorization of grains for seed production), management (the design of a marketing support strategy for a mobile phone operator) and engineering (the synthesis of a fuzzy controller). The main property of the Complete Gradient Clustering Algorithm is that it does not require strict assumptions regarding the desired number of clusters, which allows to better suit its obtained number to a real data structure. In the basic version it is possible to provide a complete set of procedures for defining the values of all functions and parameters relying on the optimization criterions. It is also possible to point out parameters, the potential change which implies influence on the size of the number of clusters (while still not giving an exact number) and the proportion between their numbers in dense and sparse areas of data elements. Moreover, the Complete Gradient Clustering Algorithm can be used to identify and possibly eliminate atypical elements (outliers). These properties proved to be very useful in the presented applications and may also be functional in many other practical problems.

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A clustering method based on the estimation of the probability density function and on the skeleton by influence zones. Application to image processing

Cited by 48 publications

References 12 publications

An Alternative Approach to Mapping Thermophysical Units from Martian Thermal Inertia and Albedo Data Using a Combination of Unsupervised Classification Techniques

An Alternative Approach to Mapping Thermophysical Units from Martian Thermal Inertia and Albedo Data Using a Combination of Unsupervised Classification Techniques

Design of Camouflage Pattern Based on Mathematical Morphology

The Complete Gradient Clustering Algorithm: properties in practical applications

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