A review of silhouette extraction algorithms for use within visual hull pipelines

ASCENSO, Guido, YAP, Moi Hoon, ALLEN, Thomas, CHOPPIN, Simon S. and PAYTON, Carl (2020). A review of silhouette extraction algorithms for use within visual hull pipelines. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1-22. [Article]

Documents
26853:553889
[thumbnail of Choppin_ReviewSillhouetteExtraction(AM).pdf]
Preview
PDF
Choppin_ReviewSillhouetteExtraction(AM).pdf - Accepted Version
Available under License All rights reserved.

Download (1MB) | Preview
Abstract
Markerless motion capture would permit the study of human biomechanics in environments where marker-based systems are impractical, e.g. outdoors or underwater. The visual hull tool may enable such data to be recorded, but it requires the accurate detection of the silhouette of the object in multiple camera views. This paper reviews the top-performing algorithms available to date for silhouette extraction, with the visual hull in mind as the downstream application; the rationale is that higher-quality silhouettes would lead to higher-quality visual hulls, and consequently better measurement of movement. This paper is the first attempt in the literature to compare silhouette extraction algorithms that belong to different fields of Computer Vision, namely background subtraction, semantic segmentation, and multi-view segmentation. It was found that several algorithms exist that would be substantial improvements over the silhouette extraction algorithms traditionally used in visual hull pipelines. In particular, FgSegNet v2 (a background subtraction algorithm), DeepLabv3+ JFT (a semantic segmentation algorithm), and Djelouah 2013 (a multi-view segmentation algorithm) are the most accurate and promising methods for the extraction of silhouettes from 2D images to date, and could seamlessly be integrated within a visual hull pipeline for studies of human movement or biomechanics.
More Information
Statistics

Downloads

Downloads per month over past year

View more statistics

Metrics

Altmetric Badge

Dimensions Badge

Share
Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Actions (login required)

View Item View Item