.png){kind=icon}
返回
Conic sections fitting in disperse data using Differential Evolution
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2019.105769.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Robust conics detection and extraction have received an increased interest due to the potential applications in many critical tasks. In medical imaging, the conics can help to detect optic disk abnormalities in retinographic images, or cranial sections and bone structures in radiography, magnetic resonance, and computed tomography images. Moreover, the physicians can be guided in the prostate cancer diagnosis by assessing the size and shape of the prostate in ultrasound or magnetic resonance images. Some of these structures are composed of discontinuous points, imprecise directions, multiple bifurcations, and variable line-trace width, or may present undesired noise, which makes the curve fitting hard to compute. Image acquisition also plays a fundamental role in adding artifacts, as unexpected outliers, which jeopardizes the applicability and efficiency of the current methods. This article proposes a strategy for determining the general equation of oblique conics in noisy and sparse data sets. As a novelty, this methodology employs four uncorrelated points from a randomly sampled population to estimate the initial space search. Then, Differential Evolution (DE) method iterates until the solution that best fits the oblique conic section is found. Advantages of the method include a DE implementation using a weighted fitness function based on the Mean Squared Error (MSE) and reinforced with the Random Sample Consensus (RANSAC) inliers measurement idea. This objective function allows associating the fitting error to the number of points into the inliers-region. Additionally, the number of parameters to tune is reduced and almost generalized. A distinctive feature is related to the eccentricity, which could conduct to a variety of rich solutions. However, some disadvantages have arisen trying to identify a specific conic. For instance, when some parabolas are considered, the method could evolve optimally to an ellipse. The proposed methodology has been evaluated using synthetic and real images. These images were contaminated with additive white Gaussian noise to prove the method stability. Numerical results showed that the proposed method is a reliable and fast technique for computing any conic from scattered data. Results obtained from two dissimilar applications prove that the proposed method can be used in a variety of sensitive applications. Those applications include the optic disk segmentation and traffic sign classification. The assessment showed an improvement of 2% across the metrics for detecting the optic disk and reached a classification accuracy of 0.9730 for the speed limit traffic signs. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Parameter optimization
Oblique parabola detection
Traffic sign recognition
Biomedical image feature extraction
Differential Evolution
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
ElliFit: An unconstrained, non-iterative, least squares based geometric Ellipse Fitting method.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
Electromigration statistics and damage evolution for Pb-free solder joints with Cu and Ni UBM in plastic flip-chip packages塑料倒装芯片封装中具有Cu和Ni UBM的无铅焊点的电迁移统计和损伤演化
PREAI
分享收藏Leakage and pressurization risk assessment of CO 2 reservoirs: A metamodeling modeling approach
PREAI
分享收藏
查看更多内容