Incorporating Near-Infrared Information into Semantic Image Segmentation
This work addresses the problem of enhancing segmentation accuracy for material-based classes in computer vision, though it is incremental as it builds on existing segmentation frameworks.
The paper tackled improving semantic image segmentation by incorporating near-infrared (NIR) information alongside RGB images, showing that adding NIR leads to improved performance for material-specific classes in both indoor and outdoor scenes.
Recent progress in computational photography has shown that we can acquire near-infrared (NIR) information in addition to the normal visible (RGB) band, with only slight modifications to standard digital cameras. Due to the proximity of the NIR band to visible radiation, NIR images share many properties with visible images. However, as a result of the material dependent reflection in the NIR part of the spectrum, such images reveal different characteristics of the scene. We investigate how to effectively exploit these differences to improve performance on the semantic image segmentation task. Based on a state-of-the-art segmentation framework and a novel manually segmented image database (both indoor and outdoor scenes) that contain 4-channel images (RGB+NIR), we study how to best incorporate the specific characteristics of the NIR response. We show that adding NIR leads to improved performance for classes that correspond to a specific type of material in both outdoor and indoor scenes. We also discuss the results with respect to the physical properties of the NIR response.