A Dataset of Flash and Ambient Illumination Pairs from the Crowd

Abstract

Illumination is a critical element of photography and is essential for many computer vision tasks. Flash light is unique in the sense that it is a widely available tool for easily manipulating the scene illumination. We present a dataset of thousands of ambient and flash illumination pairs to enable studying flash photography and other applications that can benefit from having separate illuminations. Different than the typical use of crowdsourcing in generating computer vision datasets, we make use of the crowd to directly take the photographs that make up our dataset. As a result, our dataset covers a wide variety of scenes captured by many casual photographers. We detail the advantages and challenges of our approach to crowdsourcing as well as the computational effort to generate completely separate flash illuminations from the ambient light in an uncontrolled setup. We present a brief examination of illumination decomposition, a challenging and underconstrained problem in flash photography, to demonstrate the use of our dataset in a data-driven approach.

The Dataset

Flash and Ambient Illuminations Dataset Brief Description The Flash and Ambient Illuminations Dataset (FAID) consists of aligned flash-only and ambient-only illumination pairs captured with mobile devices by many crowd-workers participated in our collection effort. This dataset accompanies our ECCV 2018 paper. BibTeX @INPROCEEDINGS{flashambient, author={Ya\u{g}{\i}z Aksoy and Changil Kim and Petr Kellnhofer and Sylvain Paris and Mohamed Elgharib and Marc Pollefeys and Wojciech Matusik}, booktitle={Proc. ECCV}, title={A Dataset of Flash and Ambient Illumination Pairs from the Crowd}, year={2018}, }

Manuscript

BibTeX

Related Publications

Crowd-Guided Ensembles: How Can We Choreograph Crowd Workers for Video Segmentation? Alexandre Kaspar, Geneviève Patterson, Changil Kim, Yağız Aksoy, Wojciech Matusik and Mohamed Elgharib ACM CHI Conference on Human Factors in Computing Systems, 2018 Abstract In this work, we propose two ensemble methods leveraging a crowd workforce to improve video annotation, with a focus on video object segmentation. Their shared principle is that while individual candidate results may likely be insufficient, they often complement each other so that they can be combined into something better than any of the individual results - the very spirit of collaborative working. For one, we extend a standard polygon-drawing interface to allow workers to annotate negative space, and combine the work of multiple workers instead of relying on a single best one as commonly done in crowdsourced image segmentation. For the other, we present a method to combine multiple automatic propagation algorithms with the help of the crowd. Such combination requires an understanding of where the algorithms fail, which we gather using a novel coarse scribble video annotation task. We evaluate our ensemble methods, discuss our design choices for them, and make our web-based crowdsourcing tools and results publicly available. Manuscript & more BibTeX @INPROCEEDINGS{crowdensembles, author={Alexandre Kaspar and Genevi\`eve Patterson and Changil Kim and Ya\u{g}{\i}z Aksoy and Wojciech Matusik and Mohamed Elgharib}, title={Crowd-Guided Ensembles: How Can We Choreograph Crowd Workers for Video Segmentation?}, booktitle={Proc. ACM CHI}, year={2018}, }