Abstract
Material recognition applications use typically color texture-based features; however, the underlying measurements are in several application fields unavailable or too expensive (e.g., due to a limited resolution in remote sensing). Therefore, bidirectional reflectance measurements are used, i.e., dependent on both illumination and viewing directions. But even measurement of such BRDF data is very time- and resources-demanding. In this paper we use dependency-aware feature selection method to identify very sparse set of the most discriminative bidirectional reflectance samples that can reliably distinguish between three types of materials from BRDF database – fabric, wood, and leather. We conclude that ten gray-scale samples primarily at high illumination and viewing elevations are sufficient to identify type of material with accuracy over 96%. We analyze estimated placement of the bidirectional samples for discrimination between different types of materials. The stability of such directional samples is very high as was verified by an additional leave-one-out classification experiment. We consider this work a step towards automatic method of material classification based on several reflectance measurements only.
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Filip, J., Somol, P. (2015). Materials Classification Using Sparse Gray-Scale Bidirectional Reflectance Measurements. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9257. Springer, Cham. https://doi.org/10.1007/978-3-319-23117-4_25
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DOI: https://doi.org/10.1007/978-3-319-23117-4_25
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