Edge detection by spherical separation

We describe an optimization-based method for tackling the classic image processing problem known as edge detection and we formulate it in the form of a classification one. The novelty of the approach is in the use of spherical separation as a classification tool in the image processing framework. Sp...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Computational management science Ročník 11; číslo 4; s. 517 - 530
Hlavní autoři: Astorino, A., Gaudioso, M., Khalaf, W.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2014
Springer Nature B.V
Témata:
Algorithms
Analysis
Business and Management
Classification
Data mining
Edge detection
Euclidean space
Image detection
Image processing
Image processing systems
Mathematical analysis
Mathematical models
Operations Research/Decision Theory
Optimization
Original Paper
Separation
Studies
Support vector machines
Topological manifolds
Image processing
Spherical separation
Edge detection
Binary classification
ISSN:1619-697X, 1619-6988
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:We describe an optimization-based method for tackling the classic image processing problem known as edge detection and we formulate it in the form of a classification one. The novelty of the approach is in the use of spherical separation as a classification tool in the image processing framework. Spherical separation consists in separating by means of a sphere two given discrete point-sets in a finite dimensional Euclidean space; in our context the two sets are the edge points and the non-edge points, respectively, in the digital representation of a given image. Assuming that the center of the sphere is fixed, the problem reduces to the minimization of a convex and nonsmooth function of just one variable, which can be effectively solved by means of an “ad hoc” bisection method. The results of our experiments on some edge detection benchmark images are provided.
Bibliografie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
ISSN:1619-697X
1619-6988
DOI:10.1007/s10287-013-0193-3