Reflectance Hashing for Material Recognition
This addresses material recognition in computer vision, offering a novel approach but appears incremental as it builds on reflectance-based methods.
The paper tackles material recognition by using reflectance disks captured with a unique optical camera, and introduces reflectance hashing with dictionary learning and binary hashing for efficient and accurate identification, demonstrating effectiveness on real-world materials.
We introduce a novel method for using reflectance to identify materials. Reflectance offers a unique signature of the material but is challenging to measure and use for recognizing materials due to its high-dimensionality. In this work, one-shot reflectance is captured using a unique optical camera measuring {\it reflectance disks} where the pixel coordinates correspond to surface viewing angles. The reflectance has class-specific stucture and angular gradients computed in this reflectance space reveal the material class. These reflectance disks encode discriminative information for efficient and accurate material recognition. We introduce a framework called reflectance hashing that models the reflectance disks with dictionary learning and binary hashing. We demonstrate the effectiveness of reflectance hashing for material recognition with a number of real-world materials.