Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Higher Order Potentials for Enforcing Label Consistency
International Journal of Computer Vision
ACM SIGGRAPH 2009 papers
Color image dehazing using the near-infrared
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph cut based inference with co-occurrence statistics
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
An Efficient Approach to Semantic Segmentation
International Journal of Computer Vision
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Multi-spectral SIFT for scene category recognition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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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.