Abstract:
Material classification using multiplexed illumination by broadband spectral light from multiple different incident angles, coupled with multi-spectral narrow band spectral measurement of light reflected from the illuminated object of unknown material, wherein selection of spectral bands for illumination or for narrow-band capture may comprise analysis of a database of labeled training material samples within a multi-class classification framework, captured using a relatively large number of spectral bands (such as 32 spectral bands), so as to select a subset of a relatively fewer number of spectral bands (such as 5 spectral bands), wherein the selected spectral bands in the subset retain a significant aptitude for distinguishing between different classifications of materials.
Abstract:
Material classification using multiplexed illumination by broadband spectral light from multiple different incident angles, coupled with multi-spectral narrow band spectral measurement of light reflected from the illuminated object of unknown material, wherein selection of spectral bands for illumination or for narrow-band capture may comprise analysis of a database of labeled training material samples within a multi-class classification framework, captured using a relatively large number of spectral bands (such as 32 spectral bands), so as to select a subset of a relatively fewer number of spectral bands (such as 5 spectral bands), wherein the selected spectral bands in the subset retain a significant aptitude for distinguishing between different classifications of materials.
Abstract:
Material classification of an object is provided. Parameters for classification are accessed. The parameters include a selection to select a subset of angles for classification, a selection to select a subset of spectral bands for classification, a selection to capture texture features, and a selection to compute image-level features. The object is illuminated and a feature vector is computed based on the parameters. The material from which the object is fabricated is classified using the feature vector.
Abstract:
Material classification of an object is provided. Parameters for classification are accessed. The parameters include a selection to select a subset of angles for classification, a selection to select a subset of spectral bands for classification, a selection to capture texture features, and a selection to compute image-level features. The object is illuminated and a feature vector is computed based on the parameters. The material from which the object is fabricated is classified using the feature vector.
Abstract:
Devices, systems, and methods for classifying materials in a scene obtain spectral-BRDF material samples; learn feature-vector representations for the spectral-BRDF material samples based on the obtained spectral-BRDF material samples; train classifiers using the learned feature-vector representations; and generate a material classification using the trained classifiers and a new material sample.