Cost Function Selection for a Graph-Based Segmentation in OCT Retinal Images

2013 ◽  
pp. 125-132 ◽  
Author(s):  
A. González ◽  
M. G. Penedo ◽  
S. G. Vázquez ◽  
J. Novo ◽  
P. Charlón
Keyword(s):  
Cost Function ◽  
Retinal Images ◽  
Selection For ◽  
Function Selection

Cost function selection for wavelet-based VBR video traffic smoothing

2004 ◽  
Vol 40 (23) ◽  
pp. 1509
Author(s):  
D. Ye ◽  
X. Li ◽  
X. Jiang
Keyword(s):  
Cost Function ◽  
Video Traffic ◽  
Traffic Smoothing ◽  
Vbr Video ◽  
Selection For ◽  
Function Selection

Compatibility Analysis of Product Design for Recyclability and Reuse

1994 ◽  
Author(s):  
Patrick Di Marco ◽  
Charles F. Eubanks ◽  
Kos Ishii
Keyword(s):  
Life Cycle ◽  
Cost Function ◽  
Product Design ◽  
Material Selection ◽  
Computer Tool ◽  
Compatibility Analysis ◽  
Life Cycle Design ◽  
Environmentally Responsible ◽  
Qualitative Knowledge ◽  
Selection For

Abstract This paper describes a method for evaluating the compatibility of a product design with respect to end-of-life product retirement issues, particularly recyclability. Designers can affect the ease of recycling in two major areas: 1) ease of disassembly, and 2) material selection for compatibility with recycling methods. The proposed method, called “clumping,” involves specification of the level of disassembly and the compatibility analysis of each remaining clump with the design’s post-life intent; i.e., reuse, remanufacturing, recycling, or disposal. The method uses qualitative knowledge to assign a normalized measure of compatibility to each clump. An empirical cost function maps the measure to an estimated cost to reprocess the product. The method is an integral part of our life-cycle design computer tool that effectively guides engineers to an environmentally responsible product design. A refrigerator in-door ice dispenser serves as an illustrative example.

Adaptive service function selection for Network Function Virtualization networking

2019 ◽  
Vol 91 ◽  
pp. 108-123 ◽  
Author(s):  
Yi-Wei Ma ◽  
Jiann-Liang Chen ◽  
Jia-Yi Jhou
Keyword(s):  
Network Function Virtualization ◽  
Service Function ◽  
Network Function ◽  
Selection For ◽  
Function Selection

scholarly journals An Automatic Subject Specific Intrinsic Mode Function Selection for Enhancing Two-Class EEG-Based Motor Imagery-Brain Computer Interface

2019 ◽  
Vol 19 (16) ◽  
pp. 6938-6947 ◽  
Author(s):  
Pramod Gaur ◽  
Ram Bilas Pachori ◽  
Hui Wang ◽  
Girijesh Prasad
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Intrinsic Mode Function ◽  
Computer Interface ◽  
Mode Function ◽  
Subject Specific ◽  
Selection For ◽  
Function Selection

scholarly journals Model Selection for Univariable Fractional Polynomials

2017 ◽  
Vol 17 (3) ◽  
pp. 619-629 ◽  
Author(s):  
Patrick Royston
Keyword(s):  
Test Procedure ◽  
Selection Procedure ◽  
Real Data ◽  
Parametric Modeling ◽  
Royal Statistical Society ◽  
Fractional Polynomials ◽  
Fractional Polynomial ◽  
Polynomial Models ◽  
Selection For ◽  
Function Selection

Since Royston and Altman's 1994 publication ( Journal of the Royal Statistical Society, Series C 43: 429–467), fractional polynomials have steadily gained popularity as a tool for flexible parametric modeling of regression relationships. In this article, I present fp_select, a postestimation tool for fp that allows the user to select a parsimonious fractional polynomial model according to a closed test procedure called the fractional polynomial selection procedure or function selection procedure. I also give a brief introduction to fractional polynomial models and provide examples of using fp and fp_select to select such models with real data.

scholarly journals SEGMENTING THE EYE FUNDUS IMAGES FOR IDENTIFICATION OF BLOOD VESSELS

2012 ◽  
Vol 17 (1) ◽  
pp. 21-30 ◽  
Author(s):  
Gediminas Balkys ◽  
Gintautas Dzemyda
Keyword(s):  
Blood Vessels ◽  
Binary Classification ◽  
Threshold Value ◽  
Retinal Images ◽  
Fundus Images ◽  
Fundus Image ◽  
Image Characteristics ◽  
Selection For ◽  
Binarization Method ◽  
Local Image

Retinal (eye fundus) images are widely used for diagnostic purposes by ophthalmologists. The normal features of eye fundus images include the optic nerve disc, fovea and blood vessels. Algorithms for identifying blood vessels in the eye fundus image generally fall into two classes: extraction of vessel information and segmentation of vessel pixels. Algorithms of the first group start on known vessel point and trace the vasculature structure in the image. Algorithms of the second group perform a binary classification (vessel or non-vessel, i.e. background) in accordance of some threshold. We focus here on the binarization [4] methods that adapt the threshold value on each pixel to the global/local image characteristics. Global binarization methods [5] try to find a single threshold value for the whole image. Local binarization methods [3] compute thresholds individually for each pixel using information from the local neighborhood of the pixel. In this paper, we modify and improve the Sauvola local binarization method [3] by extending its abilities to be applied for eye fundus pictures analysis. This method has been adopted for automatic detection of blood vessels in retinal images. We suggest automatic parameter selection for Sauvola method. Our modification allows determine/extract the blood vessels almost independently of the brightness of the picture.

Sign in / Sign up

Export Citation Format

Share Document