Context Free Band Reduction Using a Convolutional Neural Network

Wei, Ran; Robles-Kelly, Antonio; Álvarez, Jose M.

doi:10.1007/978-3-319-97785-0_9

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2019

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“…Wei et al [19] employ a one-dimensional connection table to perform band reduction in imaging spectroscopy. These contrast with our method, which aims at recovering a twodimensional sparse input connection table with non-binary weights over the observation map.…”

Section: Connection Tablementioning

confidence: 99%

Learning to Minify Photometric Stereo

Robles-Kelly

You

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Photometric stereo estimates the surface normal given a set of images acquired under different illumination conditions. To deal with diverse factors involved in the image formation process, recent photometric stereo methods demand a large number of images as input. We propose a method that can dramatically decrease the demands on the number of images by learning the most informative ones under different illumination conditions. To this end, we use a deep learning framework to automatically learn the critical illumination conditions required at input. Furthermore, we present an occlusion layer that can synthesize cast shadows, which effectively improves the estimation accuracy. We assess our method on challenging real-world conditions, where we outperform techniques elsewhere in the literature with a significantly reduced number of light conditions.

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Section: Connection Tablementioning

confidence: 99%