SIAM Journal on Scientific and Statistical Computing
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Error Estimate of the Fast Gauss Transform
SIAM Journal on Scientific Computing
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Detecting Moving Shadows: Algorithms and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Fast Gauss Transform and Efficient Kernel Density Estimation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering Intrinsic Images from a Single Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Removal of Shadows from Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of human faces using a measurement-based skin reflectance model
ACM SIGGRAPH 2006 Papers
Shadow-resistant tracking in video
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Cast Shadow Removal with GMM for Surface Reflectance Component
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Shadow resistant tracking using inertia constraints
Pattern Recognition
Learning and Removing Cast Shadows through a Multidistribution Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Light mixture estimation for spatially varying white balance
ACM SIGGRAPH 2008 papers
A tool to create illuminant and reflectance spectra for light-driven graphics and visualization
ACM Transactions on Graphics (TOG)
Moving cast shadow detection and removal for visual traffic surveillance
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Detection of moving cast shadows for object segmentation
IEEE Transactions on Multimedia
Physical models for moving shadow and object detection in video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Error-tolerant image compositing
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Detecting ground shadows in outdoor consumer photographs
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
CrackTree: Automatic crack detection from pavement images
Pattern Recognition Letters
Efficient skin detection under severe illumination changes and shadows
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part II
Removing shadows for color projection using sun position estimation
VAST'10 Proceedings of the 11th International conference on Virtual Reality, Archaeology and Cultural Heritage
Reflectance and natural illumination from a single image
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Color constancy, intrinsic images, and shape estimation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
The narrow-band assumption in log-chromaticity space
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
Error-Tolerant Image Compositing
International Journal of Computer Vision
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Recently, a method for removing shadows from colour images was developed (Finlayson et al. in IEEE Trans. Pattern Anal. Mach. Intell. 28:59---68, 2006) that relies upon finding a special direction in a 2D chromaticity feature space. This "invariant direction" is that for which particular colour features, when projected into 1D, produce a greyscale image which is approximately invariant to intensity and colour of scene illumination. Thus shadows, which are in essence a particular type of lighting, are greatly attenuated. The main approach to finding this special angle is a camera calibration: a colour target is imaged under many different lights, and the direction that best makes colour patch images equal across illuminants is the invariant direction. Here, we take a different approach. In this work, instead of a camera calibration we aim at finding the invariant direction from evidence in the colour image itself. Specifically, we recognize that producing a 1D projection in the correct invariant direction will result in a 1D distribution of pixel values that have smaller entropy than projecting in the wrong direction. The reason is that the correct projection results in a probability distribution spike, for pixels all the same except differing by the lighting that produced their observed RGB values and therefore lying along a line with orientation equal to the invariant direction. Hence we seek that projection which produces a type of intrinsic, independent of lighting reflectance-information only image by minimizing entropy, and from there go on to remove shadows as previously. To be able to develop an effective description of the entropy-minimization task, we go over to the quadratic entropy, rather than Shannon's definition. Replacing the observed pixels with a kernel density probability distribution, the quadratic entropy can be written as a very simple formulation, and can be evaluated using the efficient Fast Gauss Transform. The entropy, written in this embodiment, has the advantage that it is more insensitive to quantization than is the usual definition. The resulting algorithm is quite reliable, and the shadow removal step produces good shadow-free colour image results whenever strong shadow edges are present in the image. In most cases studied, entropy has a strong minimum for the invariant direction, revealing a new property of image formation.