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ActiveU and passive thermographies 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-nonnegative matrix factorization (NMF) methods for detecting subsurface thermal patterns. These methods inherit the advantages of principal component thermography (PCT) and sparse PCT and tackle negative bases in sparse PCT with nonnegative 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 and preserving thermal heterogeneity for distinguishing breast abnormality in breast cancer screening data set (accuracy of 74.1&#x0025;, 75.9&#x0025;, and 77.8&#x0025;).

Measuring Heterogeneous Thermal Patterns in Infrared-Based Diagnostic Systems Using Sparse Low-Rank Matrix Approximation: Comparative Study