Image Processing for Computer Graphics and Vision

Velho, Luiz; Frery, Alejandro C.; Gomes, Jonas

doi:10.1007/978-1-84800-193-0

Cited by 42 publications

(30 citation statements)

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“…Digital halftoning is a method for creating the illusion of a continuous tone image with a device having only a limited number of tones available, see [55] for an introduction and Fig. 1 for illustration.…”

Section: Introductionmentioning

confidence: 99%

Dithering by Differences of Convex Functions

Teuber¹,

Steidl²,

Gwosdek³

et al. 2011

SIAM J. Imaging Sci.

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Motivated by a recent halftoning method which is based on electrostatic principles, we analyze a halftoning framework where one minimizes a functional consisting of the difference of two convex functions (DC). One of them describes attracting forces caused by the image gray values, the other one enforces repulsion between points. In one dimension, the minimizers of our functional can be computed analytically and have the following desired properties: the points are pairwise distinct, lie within the image frame and can be placed at grid points. In the two-dimensional setting, we prove some useful properties of our functional like its coercivity and propose to compute a minimizer by a forwardbackward splitting algorithm. We suggest to compute the special sums occurring in each iteration step by a fast summation technique based on the fast Fourier transform at non-equispaced knots which requires only O(m log m) arithmetic operations for m points. Finally, we present numerical results showing the excellent performance of our dithering method.

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Section: Introductionmentioning

confidence: 99%

Dithering by Differences of Convex Functions

Teuber¹,

Steidl²,

Gwosdek³

et al. 2011

SIAM J. Imaging Sci.

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“…A prerequisite for the establishment of the index is an attempt at the additional quantification of the variable of age based on a qualitative analysis. The result of classification shows characteristics similar to dithering based on the random algorithm as described in Velho et al (2009), which is in this case seeded largely by noise in the image data and by the error of the classification itself. Thus, in the case of assumed unambiguous distinguishability of young and old spruce stands, the classification into the two classes would lose the characteristic of thresholding and the original values above which the classification into the limit classes was made can be retroactively estimated based on the law of large numbers.…”

Section: Quantification Of Age Valuesmentioning

confidence: 83%

The use of hyperspectral remote sensing for mapping the age composition of forest stands

Skoupý¹,

Zejdová²,

Hanuš³

2012

J. For. Sci.

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ABSTRACT:The paper deals with the issue of mapping the age composition of stand groups using hyperspectral imagery acquired by the AISA Eagle VNIR sensor in the Bílý Kříž locality in the Moravian-Silesian Beskids Mts. An object-oriented approach was employed through segmentation and subsequent classification by means of Nearest Neighbour (NN) algorithm in the environment of eCognition Developer 8 and artificial neural network (ANN) classification provided by ENVI 4.7 software. Because of the dominant occurrence of Norway spruce (Picea abies [L.] Karst.) monocultures in the studied locality the work focuses primarily on the distinguishability of two selected age classes of Norway spruce (10-20 years and 70-80 years). It studies possibilities of a more detailed age estimation of stand groups aged from 10 to 80 years based on the classification into the boundary classes, which shows similarity to dithering based on random algorithm. Comparison with the outline map of the Forest Management Plan shows a correlation (r 2 = 0.83) between the spectral characteristics of Norway spruce stands and their age composition.

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“…This model is widely spread, since it is the base of many electronic devices that display color (television, computers, mobile phones, etc.). This model is the simplest to implement, since it only required an amount of each primary color added over a black surface in order to obtain a given color hue [28].…”

Section: Commonly Used Colors Modelsmentioning

confidence: 99%

Improved Unsupervised Color Segmentation Using a Modified HSV Color Model and a Bagging Procedure in K-Means++ Algorithm

Chavolla

Valdivia

Diaz

et al. 2018

Mathematical Problems in Engineering

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Accurate color image segmentation has stayed as a relevant topic between the researches/scientific community due to the wide range of application areas such as medicine and agriculture. A major issue is the presence of illumination variations that obstruct precise segmentation. On the other hand, the machine learning unsupervised techniques have become attractive principally for the easy implementations. However, there is not an easy way to verify or ensure the accuracy of the unsupervised techniques; so these techniques could lead to an unknown result. This paper proposes an algorithm and a modification to the color model in order to improve the accuracy of the results obtained from the color segmentation using the -means++ algorithm. The proposal gives better segmentation and less erroneous color detections due to illumination conditions. This is achieved shifting the hue and rearranging the equation in order to avoid undefined conditions and increase robustness in the color model.

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Image Processing for Computer Graphics and Vision

Cited by 42 publications

References 0 publications

Dithering by Differences of Convex Functions

Dithering by Differences of Convex Functions

The use of hyperspectral remote sensing for mapping the age composition of forest stands

Improved Unsupervised Color Segmentation Using a Modified HSV Color Model and a Bagging Procedure in K-Means++ Algorithm

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