scholarly journals Selective Image Segmentation Models Using Three Distance Functions

2021 ◽  
Vol 21 (No.1) ◽  
pp. 95-116
Author(s):  
Abdul Kadir Jumaat ◽  
Siti Aminah Abdullah
Keyword(s):  
Image Segmentation ◽  
Distance Function ◽  
Execution Time ◽  
Euclidean Distance ◽  
Operator Splitting ◽  
Splitting Method ◽  
Distance Functions ◽  
Similarity Coefficients ◽  
City Block ◽  
Euclidean Distance Function

Image segmentation can be defined as partitioning an image that contains multiple segments of meaningful parts for further processing. Global segmentation is concerned with segmenting the whole object of an observed image. Meanwhile, the selective segmentation model is focused on segmenting a specific object required to be extracted. The Convex Distance Selective Segmentation (CDSS) model, which uses the Euclidean distance function as the fitting term, was proposed in 2015. However, the Euclidean distance function takes time to compute. This paper proposed the reformulation of the CDSS minimization problem by changing the fitting term with three popular distance functions, namely Chessboard, City Block, and Quasi-Euclidean. The proposed models were CDSSNEW1, CDSSNEW2, and CDSSNEW3, which applied the Chessboard, City Block, and Quasi-Euclidean distance functions, respectively. In this study, the Euler-Lagrange (EL) equations of the proposed models were derived and solved using the Additive Operator Splitting method. Then, MATLAB coding was developed to implement the proposed models. The accuracy of the segmented image was evaluated using the Jaccard and Dice Similarity Coefficients. The execution time was recorded to measure the efficiency of the models. Numerical results showed that the proposed CDSSNEW1 model based on the Chessboard distance function could segment specific objects successfully for all grayscale images with the fastest execution time as compared to other models.

scholarly journals Selective Image Segmentation Models Using Three Distance Functions

2021 ◽  
Vol 21 (No.1) ◽  
pp. 95-116
Author(s):  
Abdul Kadir Jumaat ◽  
Siti Aminah Abdullah
Keyword(s):  
Image Segmentation ◽  
Distance Function ◽  
Execution Time ◽  
Euclidean Distance ◽  
Operator Splitting ◽  
Splitting Method ◽  
Distance Functions ◽  
Similarity Coefficients ◽  
City Block ◽  
Euclidean Distance Function

Image segmentation can be defined as partitioning an image that contains multiple segments of meaningful parts for further processing. Global segmentation is concerned with segmenting the whole object of an observed image. Meanwhile, the selective segmentation model is focused on segmenting a specific object required to be extracted. The Convex Distance Selective Segmentation (CDSS) model, which uses the Euclidean distance function as the fitting term, was proposed in 2015. However, the Euclidean distance function takes time to compute. This paper proposed the reformulation of the CDSS minimization problem by changing the fitting term with three popular distance functions, namely Chessboard, City Block, and Quasi-Euclidean. The proposed models were CDSSNEW1, CDSSNEW2, and CDSSNEW3, which applied the Chessboard, City Block, and Quasi-Euclidean distance functions, respectively. In this study, the Euler-Lagrange (EL) equations of the proposed models were derived and solved using the Additive Operator Splitting method. Then, MATLAB coding was developed to implement the proposed models. The accuracy of the segmented image was evaluated using the Jaccard and Dice Similarity Coefficients. The execution time was recorded to measure the efficiency of the models. Numerical results showed that the proposed CDSSNEW1 model based on the Chessboard distance function could segment specific objects successfully for all grayscale images with the fastest execution time as compared to other models.

Improving Euclidean’s Consensus Degrees in Group Decision Making Problems Through a Uniform Extension

2021 ◽  
Author(s):  
J.M. Tapia ◽  
F. Chiclana ◽  
M.J. Del Moral ◽  
E. Herrera-Viedma
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Distance Function ◽  
Euclidean Distance ◽  
Group Decision ◽  
Distance Functions ◽  
Consensus Models ◽  
Interesting Task ◽  
Euclidean Distance Function ◽  
Uniform Extension

In a Group Decision Making problem, several people try to reach a single common decision by selecting one of the possible alternatives according to their respective preferences. So, a consensus process is performed in order to increase the level of accord amongst people, called experts, before obtaining the final solution. Improving the consensus degree as much as possible is a very interesting task in the process. In the evaluation of the consensus degree, the measurement of the distance representing disagreement among the experts’ preferences should be considered. Different distance functions have been proposed to implement in consensus models. The Euclidean distance function is one of the most commonly used. This paper analyzes how to improve the consensus degrees, obtained through the Euclidean distance function, when the preferences of the experts are slightly modified by using one of the properties of the Uniform distribution. We fulfil an experimental study that shows the betterment in the consensus degrees when the Uniform extension is applied, taking into account different number of experts and alternatives.

scholarly journals A Sweepline Algorithm for Generalized Delaunay Triangulations

1991 ◽  
Vol 20 (373) ◽  
Author(s):  
Sven Skyum
Keyword(s):  
Voronoi Diagram ◽  
Delaunay Triangulation ◽  
Distance Function ◽  
Euclidean Distance ◽  
Distance Functions ◽  
Minkowski Distance ◽  
Delaunay Triangulations ◽  
Strictly Convex ◽  
Euclidean Distance Function ◽  
Generalized Voronoi Diagram

We give a deterministic O(<em>n</em> log <em>n</em>) sweepline algorithm to construct the generalized Voronoi diagram for n points in the plane or rather its dual the generalized Delaunay triangulation. The algorithm uses no transformations and it is developed solely from the sweepline paradigm together with greediness. A generalized Delaunay triangulation can be based on an arbitrary strictly convex Minkowski distance function (including all L_p distance functions 1 &lt; p &lt; *) in contrast to ordinary Delaunay triangualations which are based on the Euclidean distance function.

scholarly journals Geometric applications of critical point theory to submanifolds of complex projective space

1974 ◽  
Vol 55 ◽  
pp. 5-31 ◽  
Author(s):  
Thomas E. Cecil
Keyword(s):  
Projective Space ◽  
Critical Point ◽  
Distance Function ◽  
Complex Projective Space ◽  
Euclidean Distance ◽  
Point Theory ◽  
Euclidean Distance Function ◽  
Complex Projective ◽  
Class Of Functions

In a recent paper, [6], Nomizu and Rodriguez found a geometric characterization of umbilical submanifolds Mn ⊂ Rn+p in terms of the critical point behavior of a certain class of functions Lp, p ⊂ Rn+p, on Mn. In that case, if p ⊂ Rn+p, x ⊂ Mn, then Lp(x) = (d(x,p))2, where d is the Euclidean distance function.

Identifying the Mastery Concepts in Linear Algebra by Using FCM-CM Algorithm

2010 ◽  
Vol 44-47 ◽  
pp. 3897-3901
Author(s):  
Hsiang Chuan Liu ◽  
Yen Kuei Yu ◽  
Jeng Ming Yih ◽  
Chin Chun Chen
Keyword(s):  
Objective Function ◽  
Linear Algebra ◽  
Fuzzy Clustering ◽  
Distance Function ◽  
Mahalanobis Distance ◽  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Euclidean Distance Function ◽  
Fuzzy C Means Algorithm

Euclidean distance function based fuzzy clustering algorithms can only be used to detect spherical structural clusters. Gustafson-Kessel (GK) clustering algorithm and Gath-Geva (GG) clustering algorithm were developed to detect non-spherical structural clusters by employing Mahalanobis distance in objective function, however, both of them need to add some constrains for Mahalanobis distance. In this paper, the authors’ improved Fuzzy C-Means algorithm based on common Mahalanobis distance (FCM-CM) is used to identify the mastery concepts in linear algebra, for comparing the performances with other four partition algorithms; FCM-M, GG, GK, and FCM. The result shows that FCM-CM has better performance than others.

scholarly journals An Accurate and Practical Explicit Hybrid Method for the Chan–Vese Image Segmentation Model

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1173
Author(s):  
Darae Jeong ◽  
Sangkwon Kim ◽  
Chaeyoung Lee ◽  
Junseok Kim
Keyword(s):  
Image Segmentation ◽  
Governing Equation ◽  
Hybrid Method ◽  
Operator Splitting ◽  
Splitting Method ◽  
Phase Field Model ◽  
Gradient Flow ◽  
Time Step ◽  
Euler’S Method ◽  
Operator Splitting Method

In this paper, we propose a computationally fast and accurate explicit hybrid method for image segmentation. By using a gradient flow, the governing equation is derived from a phase-field model to minimize the Chan–Vese functional for image segmentation. The resulting governing equation is the Allen–Cahn equation with a nonlinear fidelity term. We numerically solve the equation by employing an operator splitting method. We use two closed-form solutions and one explicit Euler’s method, which has a mild time step constraint. However, the proposed scheme has the merits of simplicity and versatility for arbitrary computational domains. We present computational experiments demonstrating the efficiency of the proposed method on real and synthetic images.

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