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Open Access Depth Extraction from a Single Image Based on Block-Matching and Robust Regression

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Predicting scene depth (or geometric information) from single monocular images is a challenging task. This paper addresses such challenging and essentially ill-posed problem by regression on samples for which the depth is known. In this regard, we first retrieve semantically similar RGB and depth pairs from datasets using a deep convolutional activation feature. We show that our framework provides a richer foundation for depth estimation than existing hand-craft representations. Subsequently, an initial estimation is then integrated by block-matching and robust patch regression. It assigns perceptually appropriate depth values to an input query in accordance with a data-driven depth prior. A final post processor aligns depth maps with RGB discontinuities, resulting in visually plausible results. Experiments on the Make 3D and NYU RGB-D datasets show competitive results compared to recent state-of-the-art methods.

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Document Type: Research Article

Publication date: 2016-02-14

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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