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Diego Gutierrez

Researcher at University of Zaragoza

Publications -  226
Citations -  6161

Diego Gutierrez is an academic researcher from University of Zaragoza. The author has contributed to research in topics: Rendering (computer graphics) & Virtual reality. The author has an hindex of 40, co-authored 216 publications receiving 4972 citations. Previous affiliations of Diego Gutierrez include Instituto Politécnico Nacional.

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A comparative study of image retargeting

TL;DR: This work creates a benchmark of images and conducts a large scale user study to compare a representative number of state-of-the-art retargeting methods, and presents analysis of the users' responses, where it is found that humans in general agree on the evaluation of the results and show that some retargeted methods are consistently more favorable than others.
Journal ArticleDOI

Saliency in VR: How Do People Explore Virtual Environments?

TL;DR: In this article, gaze and head orientation data of 169 users exploring stereoscopic, static omni-directional panoramas, for a total of 1980 head and gaze trajectories for three different viewing conditions were analyzed.
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Saliency in VR: How do people explore virtual environments?

TL;DR: This work captures and analyzes gaze and head orientation data of 169 users exploring stereoscopic, static omni-directional panoramas, for a total of 1980 head and gaze trajectories for three different viewing conditions, which leads to several important insights, such as the existence of a particular fixation bias.
Journal ArticleDOI

Non-line-of-sight imaging using phasor-field virtual wave optics

TL;DR: This work shows that the problem of non-line-of-sight imaging can be formulated as one of diffractive wave propagation, by introducing a virtual wave field that is term the phasor field, and yields a new class of imaging algorithms that mimic the capabilities of line- of-sight cameras.
Journal ArticleDOI

High-quality hyperspectral reconstruction using a spectral prior

TL;DR: A novel hyperspectral image reconstruction algorithm, which overcomes the long-standing tradeoff between spectral accuracy and spatial resolution in existing compressive imaging approaches and introduces a novel optimization method, which jointly regularizes the fidelity of the learned nonlinear spectral representations and the sparsity of gradients in the spatial domain.