Abstract:
Time-of-flight (ToF) cameras are active imaging sensors able to capture the 3-D geometry of a scene. Objects can be then segmented and recognized from their 3-D geometry....Show MoreMetadata
Abstract:
Time-of-flight (ToF) cameras are active imaging sensors able to capture the 3-D geometry of a scene. Objects can be then segmented and recognized from their 3-D geometry. Only very recently it has been shown that ToF cameras can also recognize the material on a per-pixel basis, as opposed to classical computer vision approaches, which rely on texture and operate per patches or regions. Differently, ToF-based material recognition takes profit of differences between the temporal impulse response function of different materials to distinguish them. Existing approaches use either raw data or processed depth measurements at different modulation frequencies to generate distinctive feature vectors for classification. In this letter we stress the importance of the bandlimited character of the material impulse response function (MIRF) and propose direct Fourier sampling. These Fourier coefficients are then used as set of features after appropriate complex scaling. Using orthogonal sensing functions enables classification with fewer but more informative measurements. Minimizing the number of measurements speeds up both acquisition, training, and classification. Moreover, 2-D superpixel segmentation in feature space allows for reliable classification with few queries per region. We demonstrate that the proposed approach allows for real-time simultaneous depth- and material-sensing ToF imaging.
Published in: IEEE Sensors Letters ( Volume: 4, Issue: 7, July 2020)