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Light field

About: Light field is a research topic. Over the lifetime, 5357 publications have been published within this topic receiving 87424 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors investigate how observation of upper state atomic populations localizes atomic position distributions for three-level atoms within a classical standing wave light field and explore the dependence of localization on the parameters of the field-atom interaction.
Abstract: We investigate how observation of upper state atomic populations localize atomic position distributions for three-level atoms within a classical standing wave light field. We consider a three-level atom, with the standing wave near-resonantly coupling one transition and a probe laser field near-resonantly coupling the second transition. Two different cases of localization are identified and we explore the dependence of localization on the parameters of the field–atom interaction.

59 citations

Proceedings ArticleDOI
23 May 2004
TL;DR: It is shown that an infinitesimally small surface element of a Lambertian scene exists as a plane of constant value in a 4D light field, where the orientation of the plane is determined by the depth of the element in the scene.
Abstract: It is shown that an infinitesimally small surface element of a Lambertian scene exists as a plane of constant value in a 4D light field, where the orientation of the plane is determined by the depth of the element in the scene. By applying 2D gradient operators to appropriate subsets of the light field, the orientations of these constant-valued planes, and thus the depths of the corresponding elements of the scene, may be estimated. The redundancy associated with using three color channels, and having two depth estimates based on orthogonal 2D gradient estimates, is resolved using a weighted sum based on the confidence of each estimate.

59 citations

Proceedings Article
Jun Zhang1, Meng Wang1, Jun Gao1, Yi Wang1, Xudong Zhang1, Xindong Wu1 
25 Jul 2015
TL;DR: Extensive evaluations on the recently introduced Light Field Saliency Dataset (LFSD) show that the investigated light field properties are complementary with each other and lead to improvements on 2D/3D models, and the approach produces superior results in comparison with the state-of-the-art.
Abstract: Although the light field has been recently recognized helpful in saliency detection, it is not comprehensively explored yet. In this work, we propose a new saliency detection model with light field data. The idea behind the proposed model originates from the following observations. (1) People can distinguish regions at different depth levels via adjusting the focus of eyes. Similarly, a light field image can generate a set of focal slices focusing at different depth levels, which suggests that a background can be weighted by selecting the corresponding slice. We show that background priors encoded by light field focusness have advantages in eliminating background distraction and enhancing the saliency by weighting the light field contrast. (2) Regions at closer depth ranges tend to be salient, while far in the distance mostly belong to the backgrounds. We show that foreground objects can be easily separated from similar or cluttered backgrounds by exploiting their light field depth. Extensive evaluations on the recently introduced Light Field Saliency Dataset (LFSD) [Li et al., 2014], including studies of different light field cues and comparisons with Li et al.'s method (the only reported light field saliency detection approach to our knowledge) and the 2D/3D state-of-the-art approaches extended with light field depth/focusness information, show that the investigated light field properties are complementary with each other and lead to improvements on 2D/3D models, and our approach produces superior results in comparison with the state-of-the-art.

59 citations

Proceedings ArticleDOI
07 Dec 2015
TL;DR: A novel approach to relative pose estimation which is tailored to 4D light field cameras is presented and compares favourably to direct linear pose estimation based on aligning the 3D point clouds obtained by reconstructing depth for each individual light field.
Abstract: We present a novel approach to relative pose estimation which is tailored to 4D light field cameras. From the relationships between scene geometry and light field structure and an analysis of the light field projection in terms of Pluecker ray coordinates, we deduce a set of linear constraints on ray space correspondences between a light field camera pair. These can be applied to infer relative pose of the light field cameras and thus obtain a point cloud reconstruction of the scene. While the proposed method has interesting relationships to pose estimation for generalized cameras based on ray-to-ray correspondence, our experiments demonstrate that our approach is both more accurate and computationally more efficient. It also compares favourably to direct linear pose estimation based on aligning the 3D point clouds obtained by reconstructing depth for each individual light field. To further validate the method, we employ the pose estimates to merge light fields captured with hand-held consumer light field cameras into refocusable panoramas.

59 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023135
2022375
2021274
2020493
2019555
2018503