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Mike Toelle

dblp:165/9730 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2015
—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
Image and video processing · 67% Computational photography and imaging · 33%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video processing › image sequence processing
frame selection
0.212015
Real-time hyperlapse creation via optimal frame selection · ACM Trans. Graph. 2015
Computational photography and imaging › time-lapse imaging
hyperlapse
0.212015
Real-time hyperlapse creation via optimal frame selection · ACM Trans. Graph. 2015
Image and video processing
video stabilization
0.212015
Real-time hyperlapse creation via optimal frame selection · ACM Trans. Graph. 2015

Methods — techniques the papers use, named apart from their topics

optimal frame selection · 0.2camera motion smoothing · 0.2
YearPublicationVenuePosition
2015 Real-time hyperlapse creation via optimal frame selection
abstract
Long videos can be played much faster than real-time by recording only one frame per second or by dropping all but one frame each second, i.e., by creating a timelapse. Unstable hand-held moving videos can be stabilized with a number of recently described methods. Unfortunately, creating a stabilized timelapse, or hyperlapse , cannot be achieved through a simple combination of these two methods. Two hyperlapse methods have been previously demonstrated: one with high computational complexity and one requiring special sensors. We present an algorithm for creating hyperlapse videos that can handle significant high-frequency camera motion and runs in real-time on HD video. Our approach does not require sensor data, thus can be run on videos captured on any camera. We optimally select frames from the input video that best match a desired target speed-up while also resulting in the smoothest possible camera motion. We evaluate our approach using several input videos from a range of cameras and compare these results to existing methods.
Neel Joshi, Wolf Kienzle, Mike Toelle, Matthew Uyttendaele, Michael F. Cohen
ACM Trans. Graph.3