Demo Data from VLDB 2026 / reviewers in the wild / expert
Gabriel Molina
dblp:138/3440
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Computational photography and imaging · 50% Image and video processing · 50% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational photography and imaging › multi-perspective imaging › multi-camera systems
camera array |
0.2 | 1 | 2013 | PiCam: an ultra-thin high performance monolithic camera array · ACM Trans. Graph. 2013 |
Image and video processing
image restoration |
0.2 | 1 | 2013 | PiCam: an ultra-thin high performance monolithic camera array · ACM Trans. Graph. 2013 |
Computational photography and imaging › light field imaging
light field capture |
0.2 | 1 | 2013 | PiCam: an ultra-thin high performance monolithic camera array · ACM Trans. Graph. 2013 |
Image and video processing
super-resolution |
0.2 | 1 | 2013 | PiCam: an ultra-thin high performance monolithic camera array · ACM Trans. Graph. 2013 |
Methods — techniques the papers use, named apart from their topics
regularized image restoration · 0.2maximum a posteriori estimation · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | PiCam: an ultra-thin high performance monolithic camera arrayabstractWe presentPiCam(Pelican Imaging Camera-Array), an ultra-thin high performance monolithic camera array, that captures light fields and synthesizes high resolution images along with a range image (scene depth) through integrated parallax detection and superresolution. The camera is passive, supporting both stills and video, low light capable, and small enough to be included in the next generation of mobile devices including smartphones. Prior works [Rander et al. 1997; Yang et al. 2002; Zhang and Chen 2004; Tanida et al. 2001; Tanida et al. 2003; Duparré et al. 2004] in camera arrays have explored multiple facets of light field capture - from viewpoint synthesis, synthetic refocus, computing range images, high speed video, and micro-optical aspects of system miniaturization. However, none of these have addressed the modifications needed to achieve the strict form factor and image quality required to make array cameras practical for mobile devices. In our approach, we customize many aspects of the camera array including lenses, pixels, sensors, and software algorithms to achieve imaging performance and form factor comparable to existing mobile phone cameras. Our contributions to the post-processing of images from camera arrays include a cost function for parallax detection that integrates across multiple color channels, and a regularized image restoration (superresolution) process that takes into account all the system degradations and adapts to a range of practical imaging conditions. The registration uncertainty from the parallax detection process is integrated into a Maximum-a-Posteriori formulation that synthesizes an estimate of the high resolution image and scene depth. We conclude with some examples of our array capabilities such as postcapture (still) refocus, video refocus, view synthesis to demonstrate motion parallax, 3D range images, and briefly address future work. Kartik Venkataraman, Dan Lelescu, Jacques Duparré, Andrew McMahon, Gabriel Molina, Priyam Chatterjee, Robert Mullis, Shree K. Nayar |
ACM Trans. Graph. | 5 |