Multispectral Imaging for Fine-Grained Recognition of Powders on Complex Backgrounds
Hundreds of materials, such as drugs, explosives, makeup, food additives, are in the form of powder. Recognizing such powders is important for security checks, criminal identification, drug control, and quality assessment. Powders are hard to distinguish: they are amorphous, appear matte, have little color or texture variation and blend with surfaces they are deposited on in complex ways. To address these challenges, we present the first comprehensive dataset and approach for powder recognition using multi-spectral imaging. To obtain more data, we propose a blending model to synthesize images of powders of various thickness deposited on a wide range of surfaces. We conduct fine-grained recognition of 100 powders on complex backgrounds, and achieve over 40% mean intersection-over-union (IoU) without known powder location.
Presented in Partial Fulfillment of the CSD Speaking Skills Requirement