Boosting Color Saliency in Image Feature Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A blob detector in color images
Proceedings of the 6th ACM international conference on Image and video retrieval
Computers in Biology and Medicine
A tensorial framework for color images
Pattern Recognition Letters
Multimedia analysis by learning
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Convexity grouping of salient contours
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Coloring local feature extraction
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
A new biologically inspired color image descriptor
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
An effective method for illumination-invariant representation of color images
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
21/2D scene reconstruction of indoor scenes from single RGB-D images
CCIW'13 Proceedings of the 4th international conference on Computational Color Imaging
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Luminance-based features are widely used as low-level input for computer vision applications, even when color data is available. The extension of feature detection to the color domain prevents information loss due to isoluminance and allows us to exploit the photometric information. To fully exploit the extra information in the color data, the vector nature of color data has to be taken into account and a sound framework is needed to combine feature and photometric invariance theory. In this paper, we focus on the structure tensor, or color tensor, which adequately handles the vector nature of color images. Further, we combine the features based on the color tensor with photometric invariant derivatives to arrive at photometric invariant features. We circumvent the drawback of unstable photometric invariants by deriving an uncertainty measure to accompany the photometric invariant derivatives. The uncertainty is incorporated in the color tensor, hereby allowing the computation of robust photometric invariant features. The combination of the photometric invariance theory and tensor-based features allows for detection of a variety of features such as photometric invariant edges, corners, optical flow, and curvature. The proposed features are tested for noise characteristics and robustness to photometric changes. Experiments show that the proposed features are robust to scene incidental events and that the proposed uncertainty measure improves the applicability of full invariants.