Work

Ground truth dataset and baseline evaluations for intrinsic image algorithms

Year: 2009

Type: conference-paper

Abstract: The intrinsic image decomposition aims to retrieve “intrinsic” properties of an image, such as shading and reflectance. To make it possible to quantitatively compare different approaches to this problem in realistic settings, we present a ground-truth dataset of intrinsic image decompositions for a variety of real-world objects. For each object, we separate an image of it into three components: Lambertian shading, reflectance, and specularities. We use our dataset to quantitatively compare several existing algorithms; we hope that this dataset will serve as a means for evaluating future work on intrinsic images.

Source: IEEE/CVF International Conference on Computer Vision (ICCV)

DOI: 10.1109/iccv.2009.5459428

Authors: Roger Grosse, Micah K Johnson, Edward H Adelson, William T Freeman

Institution: Massachusetts Institute of Technology

Language: English

Topic: Computer Graphics and Visualization Techniques

Subfield: Computer Graphics and Computer-Aided Design

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Microsoft Research, National Geospatial-Intelligence Agency, Multidisciplinary University Research Initiative

Awards: Image Statistics in Digital Forensics, G329060674