Abstract

Material classifification is an important area of research in computer vision. Typical algorithms use color and texture information for classifification, but there are problems due to varying lighting conditions and diversity of colors in a single material class. In this work we study the use of long wave infrared (i.e. thermal) imagery for material classifification. Thermal imagery has the benefifit of relative invariance to color changes, invariance to lighting conditions, and can even work in the dark. We collect a database of 21 different material classes with both color and thermal imagery. We develop a set of features that describe water permeation and heating/cooling properties, and test several variations on these methods to obtain our fifinal classififier. The results show that the proposed method outperforms typical color and texture features, and when combined with color information, the results are improved further.