Semantic image segmentation

Incorporating Near-Infrared Information into Semantic Image Segmentation

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 reflectionin 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.

Semantic Image Segmentation Using Visible and Near-Infrared Channels

Semantic Image Segmentation Using Visible and Near-Infrared Channels

N. Salamati; D. Larlus; G. Csurka; S. Süsstrunk

2012. 4th Workshop on Color and Photometry in Computer Vision at ECCV12 , Florence, Italy , October 7-13, 2012. p. 461-471.

DOI : 10.1007/978-3-642-33868-7_46.

Recent progress in computational photography has shown that we can acquire physical information beyond visible (RGB) image representations. In particular, we can acquire near-infrared (NIR) cues with only slight modification to any standard digital camera. In this paper, we study whether this extra channel can improve semantic image segmentation. Based on a state-of-the-art segmentation framework and a novel manually segmented image database that contains 4-channel images (RGB+NIR), we study how to best incorporate the specific characteristics of the NIR response. We show that it leads to improved performances for 7 classes out of 10 in the proposed dataset and discuss the results with respect to the physical properties of the NIR response.

Supplementary material

Here is the link to more qualitative results. Supplementary material