  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%).
