Sigmoidal weighted vector directional filter

Rastislav Lukac; Bogdan Smolka; Konstantinos N. Plataniotis; Anastasios N. Venetsanopoulos

doi:10.1007/3-540-36592-3_40

Sigmoidal weighted vector directional filter

Rastislav Lukac
, Bogdan Smolka
, Konstantinos N. Plataniotis
, Anastasios N. Venetsanopoulos

Department of Data Science and Engineering (RAU/RAU4)

Slovak Image Processing Center
University of Toronto

Research output: Contribution to journal › Conference article › peer-review

Abstract

In this paper, we provide and analyze a sigmoidal optimization of a recently developed class of weighted vector directional filters (WVDFs) outputting the input multichannel sample associated with the minimum sum of weighted angular distances to other input samples. Because the WVDFs can perform a number of smoothing operations in dependence on the weight coefficients, the aim of this paper is to adapt the WVDF behavior to statistical properties of noise and original color image. The filtering results and the complete analysis of the sigmoidal function based WVDF optimization are also provided.

Original language	English
Pages (from-to)	418-427
Number of pages	10
Journal	Lecture Notes in Computer Science
Volume	2626
DOIs	https://doi.org/10.1007/3-540-36592-3_40
Publication status	Published - 2003
Event	3rd International Conference on Computer Vision Systems, ICVS 2003 - Graz, Austria Duration: 1 Apr 2003 → 3 Apr 2003

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/3-540-36592-3_40

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title = "Sigmoidal weighted vector directional filter",

abstract = "In this paper, we provide and analyze a sigmoidal optimization of a recently developed class of weighted vector directional filters (WVDFs) outputting the input multichannel sample associated with the minimum sum of weighted angular distances to other input samples. Because the WVDFs can perform a number of smoothing operations in dependence on the weight coefficients, the aim of this paper is to adapt the WVDF behavior to statistical properties of noise and original color image. The filtering results and the complete analysis of the sigmoidal function based WVDF optimization are also provided.",

author = "Rastislav Lukac and Bogdan Smolka and Plataniotis, \{Konstantinos N.\} and Venetsanopoulos, \{Anastasios N.\}",

year = "2003",

doi = "10.1007/3-540-36592-3\_40",

language = "English",

volume = "2626",

pages = "418--427",

journal = "Lecture Notes in Computer Science",

issn = "0302-9743",

publisher = "Springer Science and Business Media Deutschland GmbH",

note = "3rd International Conference on Computer Vision Systems, ICVS 2003 ; Conference date: 01-04-2003 Through 03-04-2003",

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TY - JOUR

T1 - Sigmoidal weighted vector directional filter

AU - Lukac, Rastislav

AU - Smolka, Bogdan

AU - Plataniotis, Konstantinos N.

AU - Venetsanopoulos, Anastasios N.

PY - 2003

Y1 - 2003

N2 - In this paper, we provide and analyze a sigmoidal optimization of a recently developed class of weighted vector directional filters (WVDFs) outputting the input multichannel sample associated with the minimum sum of weighted angular distances to other input samples. Because the WVDFs can perform a number of smoothing operations in dependence on the weight coefficients, the aim of this paper is to adapt the WVDF behavior to statistical properties of noise and original color image. The filtering results and the complete analysis of the sigmoidal function based WVDF optimization are also provided.

AB - In this paper, we provide and analyze a sigmoidal optimization of a recently developed class of weighted vector directional filters (WVDFs) outputting the input multichannel sample associated with the minimum sum of weighted angular distances to other input samples. Because the WVDFs can perform a number of smoothing operations in dependence on the weight coefficients, the aim of this paper is to adapt the WVDF behavior to statistical properties of noise and original color image. The filtering results and the complete analysis of the sigmoidal function based WVDF optimization are also provided.

UR - https://www.scopus.com/pages/publications/84886618037

U2 - 10.1007/3-540-36592-3_40

DO - 10.1007/3-540-36592-3_40

M3 - Conference article

AN - SCOPUS:84886618037

SN - 0302-9743

VL - 2626

SP - 418

EP - 427

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

T2 - 3rd International Conference on Computer Vision Systems, ICVS 2003

Y2 - 1 April 2003 through 3 April 2003

ER -

Sigmoidal weighted vector directional filter

Abstract

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