scholarly journals Image Segmentation by Pairwise Nearest Neighbor Using Mumford-Shah Model

2021 ◽  
Author(s):  
Nilima Shah ◽  
Dhanesh Patel ◽  
Pasi Fränti
Keyword(s):  
Image Segmentation ◽  
Nearest Neighbor ◽  
Mathematical Optimization ◽  
Optimization Methods ◽  
Hierarchical Algorithm ◽  
Complex Shapes

Mumford-Shah model has been used for image segmentation by considering both homogeneity and the shape of the segments jointly. It has been previously optimized by complex mathematical optimization methods like Douglas-Rachford, and a faster but sub-optimal k-means. However, they both suffer from fragmentation caused by non-convex segments. In this paper, we present hierarchical algorithm called Pairwise nearest neighbor (PNN) to optimize the Mumford-Shah model. The merge-based strategy utilizing the connectivity of the pixels prevents isolated fragments to be formed, and in this way, reaches better quality in case of images containing complex shapes.

A More Effective Formulation of the Path Generation Mechanism Synthesis Problem

1994 ◽  
Author(s):  
Irfan Ullah ◽  
Sridhar Kota
Keyword(s):  
Objective Function ◽  
Error Function ◽  
Mathematical Optimization ◽  
Optimization Methods ◽  
Complex Nature ◽  
Mechanism Synthesis ◽  
Structural Error ◽  
Crank Angle ◽  
The Common ◽  
Selection Of

Abstract Use of mathematical optimization methods for synthesis of path-generating mechanisms has had only limited success due to the very complex nature of the commonly used Structural Error objective function. The complexity arises, in part, because the objective function represents not only the error in the shape of the coupler curve, but also the error in location, orientation and size of the curve. Furthermore, the common introduction of timing (or crank angle), done generally to facilitate selection of corresponding points on the curve for calculating structural error, has little practical value and unnecessarily limits possible solutions. This paper proposes a new objective function, based on Fourier Descriptors, which allows search for coupler curve of the desired shape without reference to location, orientation, or size. The proposed objective function compares overall shape properties of curves rather than making point-by-point comparison and therefore does not requires prescription of timing. Experimental evidence is provided to show that it is much easier to search the space of the proposed objective function compared to the structural error function.

Optimal Design of Composite Lightweight Rollers

1993 ◽  
Author(s):  
K. Bellendir ◽  
Hans A. Eschenauer
Keyword(s):  
Structural Analysis ◽  
Shell Theory ◽  
Analytical Procedure ◽  
Mathematical Optimization ◽  
Optimization Methods ◽  
Analysis Procedure ◽  
Various Boundary Conditions ◽  
Evaluation Models ◽  
Laminate Theory ◽  
Static Behaviour

Abstract A well-aimed layout of fibre-reinforced lightweight rollers does not only require an efficient structural analysis procedure but also the application of structural optimization methods. Therefore, an analytical procedure is introduced for the calculation of the static behaviour of cylindrical shells subject to axisymmetric and/or nonaxisymmetric loads. In the scope of this procedure, arbitrary, unsymmetrical laminates as well as various boundary conditions will be considered. Basis is the shell theory by Flügge enhanced by anisotropic constitutive equations (material law) in the scope of the classical laminate theory. By means of mathematical optimization procedures we then determine optimal lightweight rollers, using different design and evaluation models. For that purpose, coated and uncoated roller constructions as well as hybrid types made of CFRP/GFRP will be applied. Concluding, we will discuss possible improvements and advantages of anisotropic lightweight rollers in contrast to isotropic ones made of steel or aluminium.

The Optimum Alignment of Marine Shafting

1974 ◽  
Vol 11 (03) ◽  
pp. 260-269
Author(s):  
Ronald T. Bradshaw
Keyword(s):  
United States ◽  
Mathematical Optimization ◽  
Implementation Process ◽  
Optimization Methods ◽  
The United States ◽  
Journal Bearings ◽  
Practical Realization ◽  
Shaft System ◽  
Economic Basis ◽  
Optimum Alignment

Alignment of marine shafting generally implies a judicious misalignment of the supporting bearings in order to achieve acceptable values of bearing reactions and shaft stresses. To achieve a true optimum it is necessary to describe quantitatively the desirable features of an alignment, and a combined technical/economic basis for this description is presented. The result is a well-defined problem that is solved by mathematical optimization methods. Results of the procedure are illustrated by alignment plans derived for, and adopted by, ships recently built in the United States and abroad. The practical realization of an alignment plan is discussed and means of speeding up the implementation process are proposed. Improvements in the elastic representation of the shaft system are described and the question of hydrodynamic support in journal bearings is considered. Discussers: Technical Staff, ABSP. F. Nuclo C. L. Stahly

Application of Differential Evolution Algorithm in Transmission Expansion Planning

2013 ◽  
Vol 394 ◽  
pp. 314-320
Author(s):  
Ibrahim Al Hamrouni ◽  
Azhar Khairuddin ◽  
M. Salem
Keyword(s):  
Differential Evolution ◽  
Heuristic Method ◽  
Differential Evolution Algorithm ◽  
Mathematical Optimization ◽  
Minimum Cost ◽  
Optimization Methods ◽  
Expansion Planning ◽  
Transmission Expansion Planning ◽  
Transmission Expansion ◽  
Evolution Algorithm

Transmission expansion planning has become a complicated procedure more than it was. The rapid growth of the transmission networks and the deregulation has introduced more objectives and uncertainties to the transmission network planners. As a result of that, new approach and criteria that can replace the old ones are needed for TEP problem. The main goal of this process is to locate the additional transmission lines that must be added to meet the forecasted load in the system adequately with minimum cost. There have been several methods applied for this purpose; mathematical optimization methods, heuristic and Meta heuristic methods. This paper reviews the use of Meta heuristic method by the means of differential evolution algorithm (DEA) to solve this multi objective optimization problem. In addition, some suggestions have been made by the author that can make the DEA more efficient and applicable in the real world networks.

A Method for Efficient Flexible Process Planning by Mathematical Optimization

2012 ◽  
Author(s):  
Eiji Morinaga ◽  
Takuma Hara ◽  
Hidefumi Wakamatsu ◽  
Eiji Arai
Keyword(s):  
Process Planning ◽  
Mathematical Optimization ◽  
Point Of View ◽  
Choice Process ◽  
Computer Aided Process Planning ◽  
Optimization Framework ◽  
Flexible Process Planning ◽  
Complex Shapes ◽  
Rough Milling ◽  
Modern Manufacturing

Computer support technology for modern manufacturing should deal with variable situations to accomodate to high-mix low-volume manufacturing. Computer-aided process planning (CAPP) has been discussed from this point of view, and a method for flexible CAPP, which generates a new proper process plan easily when manufacturing situation has changed, was proposed for rough milling by a three-axis vertical machine. This method was enhanced to handle millings by a multiaxis vertical machine and by both vertical and horizontal machines. The basic idea of these methods is to generate all process plans and then choose the best one. In the choice process of the best plan, all of the generated plans are evaluated. However, this process requires a large computational power when employed in actual machining where products of complex shapes have to be produced. For this computational problem, this paper discusses application of the mathematical optimization framework to this choice process.

Graph-based image segmentation using directional nearest neighbor graph

2012 ◽  
Vol 56 (11) ◽  
pp. 1-10 ◽  
Author(s):  
Zhao Liu ◽  
DeWen Hu ◽  
Hui Shen ◽  
GuiYu Feng
Keyword(s):  
Image Segmentation ◽  
Nearest Neighbor ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph
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