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Found 7 papers, 1 papers with code
Portmanteau Vocabularies for Multi-Cue Image Representation
We describe a novel technique for feature combination in the bag-of-words model of image classification.
The Photometry of Intrinsic Images
The proposed mathematical formulation includes information about the color of the illuminant and the effects of the camera sensors, both of which modify the observed color of the reflectance of the objects in the scene during the acquisition process.
Understanding learned CNN features through Filter Decoding with Substitution
This provides us with a new tool to directly visualize any CNN single neuron as a filter in the first layer, this is in terms of the image space.
Understanding trained CNNs by indexing neuron selectivity
The impressive performance of Convolutional Neural Networks (CNNs) when solving different vision problems is shadowed by their black-box nature and our consequent lack of understanding of the representations they build and how these representations are organized.
Deep intrinsic decomposition trained on surreal scenes yet with realistic light effects
Estimation of intrinsic images still remains a challenging task due to weaknesses of ground-truth datasets, which either are too small or present non-realistic issues.
Light Direction and Color Estimation from Single Image with Deep Regression
We present a method to estimate the direction and color of the scene light source from a single image.
Intrinsic Decomposition of Document Images In-the-Wild
However, document shadow or shading removal results still suffer because: (a) prior methods rely on uniformity of local color statistics, which limit their application on real-scenarios with complex document shapes and textures and; (b) synthetic or hybrid datasets with non-realistic, simulated lighting conditions are used to train the models.
