论文标题
基于红外线的诊断系统的低排名凸/稀疏热矩阵近似
Low-rank Convex/Sparse Thermal Matrix Approximation for Infrared-based Diagnostic System
论文作者
论文摘要
主动和被动热量成像是两种有效的技术广泛用于测量导致诊断评估的地下缺陷的异质热模式。这项研究对在热成像中的低级矩阵近似方法进行了比较分析，并应用了半，凸，凸和稀疏的非阴性矩阵分解（NMF）方法，用于检测地下热模式。这些方法继承了主成分热力计（PCT）和稀疏PCT的优势，而PCT中的负基碱基的负面基础具有非阴性约束，并在处理数据中显示了聚类属性。这些方法的实用性和效率通过三个标本（不同的深度和尺寸缺陷）中地下缺陷检测的实验结果证明，并保留了乳腺癌筛查数据集中乳腺异常（精度为74.1％，75.8％和77.8％）的热异质性。
Active and passive thermography are two efficient techniques extensively used to measure heterogeneous thermal patterns leading to subsurface defects for diagnostic evaluations. This study conducts a comparative analysis on low-rank matrix approximation methods in thermography with applications of semi-, convex-, and sparse- non-negative matrix factorization (NMF) methods for detecting subsurface thermal patterns. These methods inherit the advantages of principal component thermography (PCT) and sparse PCT, whereas tackle negative bases in sparse PCT with non-negative constraints, and exhibit clustering property in processing data. The practicality and efficiency of these methods are demonstrated by the experimental results for subsurface defect detection in three specimens (for different depth and size defects) and preserving thermal heterogeneity for distinguishing breast abnormality in breast cancer screening dataset (accuracy of 74.1%, 75.8%, and 77.8%).
