Material-Based Object Segmentation Using Near-Infrared Information

We present a framework to incorporate near-infrared (NIR) information into algorithms to better segment objects by isolating material boundaries from color and shadow edges. Most segmentation algorithms assign individual regions to parts of the object that are colorized differently. Similarly, the presence of shadows and thus large changes in image intensities across objects can also result in mis-segmentation. We first form an intrinsic image from the R, G, B, and NIR channels based on a 4-sensor camera calibration model that is invariant to shadows. The regions obtained by the segmentation algorithms are thus only due to color and material changes and are independent of the illumination. Additionally, we also segment the NIR channel only. Near-infrared (NIR) image intensities are largely dependent on the chemistry of the material and have no general correlation with visible color information. Consequently, the NIR segmentation only highlights material and lighting changes. The union of both segmentations obtained from the intrinsic and NIR images results in image partitions that are only based on material changes and not on color or shadows. Experiments show that the proposed method provides good object-based segmentation results on diverse images.