A Self-adaptation Method for Human Skin Segmentation based on Seed Growing

2015 ◽  
Author(s):  
Anderson Carlos Sousa e Santos ◽  
Helio Pedrini
Keyword(s):  
Human Skin ◽  
Skin Segmentation ◽  
Self Adaptation ◽  
Adaptation Method ◽  
Seed Growing

scholarly journals Special Finite Elements with Adaptive Strain Field on the Example of One-Dimensional Elements

2021 ◽  
Vol 11 (2) ◽  
pp. 609
Author(s):  
Tadeusz Chyży ◽  
Monika Mackiewicz
Keyword(s):  
Finite Elements ◽  
Strain Field ◽  
Main Idea ◽  
Deformation Field ◽  
Geometrical Parameters ◽  
Single Element ◽  
One Dimensional ◽  
New Type ◽  
Self Adaptation ◽  
Adaptation Method

The conception of special finite elements called multi-area elements for the analysis of structures with different stiffness areas has been presented in the paper. A new type of finite element has been determined in order to perform analyses and calculations of heterogeneous, multi-coherent, and layered structures using fewer finite elements and it provides proper accuracy of the results. The main advantage of the presented special multi-area elements is the possibility that areas of the structure with different stiffness and geometrical parameters can be described by single element integrated in subdivisions (sub-areas). The formulation of such elements has been presented with the example of one-dimensional elements. The main idea of developed elements is the assumption that the deformation field inside the element is dependent on its geometry and stiffness distribution. The deformation field can be changed and adjusted during the calculation process that is why such elements can be treated as self-adaptive. The application of the self-adaptation method on strain field should simplify the analysis of complex non-linear problems and increase their accuracy. In order to confirm the correctness of the established assumptions, comparative analyses have been carried out and potential areas of application have been indicated.

Self-Adaptation of Natural-Coordinate System

2013 ◽  
Vol 367 ◽  
pp. 286-291
Author(s):  
Ke Wei Zhang ◽  
Yun Qing Zhang
Keyword(s):  
Coordinate System ◽  
Equal Weight ◽  
Natural Coordinates ◽  
Model Quality ◽  
Step Method ◽  
Natural Coordinate System ◽  
Self Adaptation ◽  
Natural Coordinate ◽  
Analysis Platform ◽  
Adaptation Method

A self-adaptation method for natural-coordinate systems is proposed, in order to automate the selection of natural coordinates for each rigid element of a multibody system. The four-step method includes: First, find out all empty positions, which come from the feature points or vectors of the joints attached to the element, and give equal weight to them; second, delete redundant empty positions and add their weight to the unique one; third, select at most four empty positions which have a maximum total weight and can be occupied by a natural-coordinate system at the same time; fourth, the standard natural-coordinate system on the element can adapt itself to the selected empty positions, leading to an actual natural-coordinate system, which contains twelve rational natural coordinates for the element. The implementation of the method has been achieved on a multibody dynamics and motion analysis platform, InteDyna, with the result that modeling efficiency is enhanced and model quality improved.

Convergence in Evolutionary Programs with Self-Adaptation

2001 ◽  
Vol 9 (2) ◽  
pp. 147-157 ◽  
Author(s):  
Garrison W. Greenwood ◽  
Qiji J. Zhu
Keyword(s):  
Optimization Problems ◽  
Previous Analysis ◽  
Evolution Strategies ◽  
Convergence Properties ◽  
Formal Proofs ◽  
Self Adaptation ◽  
Long Term Behavior ◽  
Adaptation Method ◽  
Strategy Parameters

Evolutionary programs are capable of finding good solutions to difficult optimization problems. Previous analysis of their convergence properties has normally assumed the strategy parameters are kept constant, although in practice these parameters are dynamically altered. In this paper, we propose a modified version of the 1/5-success rule for self-adaptation in evolution strategies (ES). Formal proofs of the long-term behavior produced by our self-adaptation method are included. Both elitist and non-elitist ES variants are analyzed. Preliminary tests indicate an ES with our modified self-adaptation method compares favorably to both a non-adapted ES and a 1/5-success rule adapted ES.

Burnt Human Skin Segmentation and Depth Classification Using Deep Convolutional Neural Network (DCNN)

2020 ◽  
Vol 10 (10) ◽  
pp. 2421-2429
Author(s):  
Fakhri Alam Khan ◽  
Ateeq Ur Rehman Butt ◽  
Muhammad Asif ◽  
Hanan Aljuaid ◽  
Awais Adnan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Human Skin ◽  
Low Income ◽  
Deep Convolutional Neural Network ◽  
Image Feature ◽  
World Health ◽  
Skin Segmentation ◽  
Average Accuracy ◽  
Research Task

World Health Organization (WHO) manage health-related statistics all around the world by taking the necessary measures. What could be better for health and what may be the leading causes of deaths, all these statistics are well organized by WHO. Burn Injuries are mostly viewed in middle and low-income countries due to lack of resources, the result may come in the form of deaths by serious injuries caused by burning. Due to the non-accessibility of specialists and burn surgeons, simple and basic health care units situated at tribble areas as well as in small cities are facing the problem to diagnose the burn depths accurately. The primary goals and objectives of this research task are to segment the burnt region of skin from the normal skin and to diagnose the burn depths as per the level of burn. The dataset contains the 600 images of burnt patients and has been taken in a real-time environment from the Allied Burn and Reconstructive Surgery Unit (ABRSU) Faisalabad, Pakistan. Burnt human skin segmentation was carried by the use of Otsu's method and the image feature vector was obtained by using statistical calculations such as mean and median. A classifier Deep Convolutional Neural Network based on deep learning was used to classify the burnt human skin as per the level of burn into different depths. Almost 60 percent of images have been taken to train the classifier and the rest of the 40 percent burnt skin images were used to estimate the average accuracy of the classifier. The average accuracy of the DCNN classifier was noted as 83.4 percent and these are the best results yet. By the obtained results of this research task, young physicians and practitioners may be able to diagnose the burn depths and start the proper medication.

Sign in / Sign up

Export Citation Format

Share Document