I earned my Ph.D. from Washington
University in St. Louis, working under the supervision of
in the Media and Machines Lab.
Before starting my graduate work at Washington University, I received my BS in Computer
Science from Truman State University in 2009.
My research focuses on interpreting very long term time-lapse imagery
taken from tens of thousands of publicly-available outdoor webcam images,
by geographically calibrating them
with a user in the loop, and
recovering the 3D structure of the scene from a single view.
Austin Abrams, Ian Schillebeeckx, Robert Pless.
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In outdoor images, cast shadows define 3D constraints between the sun,
the points casting a shadow, and the surfaces onto which shadows are
cast. This cast shadow structure provides a powerful cue for 3D
reconstruction, but requires that shadows be tracked over time, and
this is difficult as shadows have minimal texture. Thus, we develop a
shadow tracking system that enforces geometric consistency for each
track and then combines thousands of tracking results to create a 3D
model of scene geometry. We demonstrate reconstruction results on a
variety of outdoor scenes, including some that show the 3D
structure of occluders never directly observed by the camera.
Austin Abrams, Kylia Miskell, Robert Pless.
Shadows encode a powerful geometric cue: if one pixel
casts a shadow onto another, then the two pixels are colinear with the lighting
direction. Given many images over many
lighting directions, this constraint can be leveraged to recover the depth
of a scene from a single viewpoint. For outdoor scenes with
solar illumination, we term this the episolar constraint, which
provides a convex optimization to solve for the sparse depth of a scene from
shadow correspondences, a method to reduce the search space
when finding shadow correspondences, and a method to geometrically calibrate
a camera using shadow constraints. Our method constructs a dense network
of nonlocal constraints which complements recent work on outdoor photometric
stereo and cloud based cues for 3D.
We demonstrate results across a variety of time-lapse
sequences from webcams "in the wild."
Austin Abrams, Christopher Hawley, Robert Pless.
In this work, we present a method to uncover shape from webcams "in
the wild." We present a variant of photometric stereo which uses the
sun as a distant light source, so that lighting direction can be
computed from known GPS and timestamps. We propose an iterative,
non-linear optimization process that optimizes the error in
reproducing all images from an extended time-lapse with an image
formation model that accounts for ambient lighting, shadows, changing
light color, dense surface normal maps, radiometric calibration, and
exposure. Unlike many approaches to uncalibrated outdoor image
analysis, this procedure is automatic, and we report
quantitative results by comparing extracted surface normals to Google
Earth 3D models. We evaluate this procedure on data from a varied set
of scenes and emphasize the advantages of including imagery from many
Web-Accessible Geographic Integration and Calibration of Webcams
Austin Abrams, Robert Pless.
A global network of webcams offers unique viewpoints from tens of thousands of locations. Understanding the geographic
context of this imagery is vital in using these cameras for quantitative environmental monitoring or surveillance applications.
We derive robust geo-calibration constraints that allow users to geo-register static or pan-tilt-zoom cameras by specifying a
few corresponding points, and describe our web interface suitable for novices. We discuss design decisions that support our
scalable, publicly-accessible web service that allows webcam textures to be displayed live on 3D geographic models. Finally, we
demonstrate several multimedia applications for geocalibrated cameras.
Exploratory Analysis of Time-Lapse Imagery with Fast Subset PCA
Austin Abrams, Emily Feder, Robert Pless
In surveillance and environmental monitoring applications, it is common to have millions of images of a particular scene.
While there exist tools to find particular events,
anomalies, human actions and behaviors, there has been
little investigation of tools which allow more exploratory
searches in the data. This paper proposes modifications to
PCA that enable users to quickly recompute low-rank decompositions for select spatial and temporal subsets of the
data. This process returns decompositions orders of magnitude faster than general PCA and are close to optimal in
terms of reconstruction error. We show examples of real
exploratory data analysis across several applications, including an interactive web application.
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