3 research outputs found
From two rolling shutters to one global shutter
Most consumer cameras are equipped with electronic rolling shutter, leading
to image distortions when the camera moves during image capture. We explore a
surprisingly simple camera configuration that makes it possible to undo the
rolling shutter distortion: two cameras mounted to have different rolling
shutter directions. Such a setup is easy and cheap to build and it possesses
the geometric constraints needed to correct rolling shutter distortion using
only a sparse set of point correspondences between the two images. We derive
equations that describe the underlying geometry for general and special motions
and present an efficient method for finding their solutions. Our synthetic and
real experiments demonstrate that our approach is able to remove large rolling
shutter distortions of all types without relying on any specific scene
structure.Comment: CVPR 202
Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effect
Physical adversarial examples for camera-based computer vision have so far
been achieved through visible artifacts -- a sticker on a Stop sign, colorful
borders around eyeglasses or a 3D printed object with a colorful texture. An
implicit assumption here is that the perturbations must be visible so that a
camera can sense them. By contrast, we contribute a procedure to generate, for
the first time, physical adversarial examples that are invisible to human eyes.
Rather than modifying the victim object with visible artifacts, we modify light
that illuminates the object. We demonstrate how an attacker can craft a
modulated light signal that adversarially illuminates a scene and causes
targeted misclassifications on a state-of-the-art ImageNet deep learning model.
Concretely, we exploit the radiometric rolling shutter effect in commodity
cameras to create precise striping patterns that appear on images. To human
eyes, it appears like the object is illuminated, but the camera creates an
image with stripes that will cause ML models to output the attacker-desired
classification. We conduct a range of simulation and physical experiments with
LEDs, demonstrating targeted attack rates up to 84%
Image Stitching and Rectification for Hand-Held Cameras
In this paper, we derive a new differential homography that can account for
the scanline-varying camera poses in Rolling Shutter (RS) cameras, and
demonstrate its application to carry out RS-aware image stitching and
rectification at one stroke. Despite the high complexity of RS geometry, we
focus in this paper on a special yet common input -- two consecutive frames
from a video stream, wherein the inter-frame motion is restricted from being
arbitrarily large. This allows us to adopt simpler differential motion model,
leading to a straightforward and practical minimal solver. To deal with
non-planar scene and camera parallax in stitching, we further propose an
RS-aware spatially-varying homography field in the principle of
As-Projective-As-Possible (APAP). We show superior performance over
state-of-the-art methods both in RS image stitching and rectification,
especially for images captured by hand-held shaking cameras.Comment: ECCV 2020. Project web: https://www.nec-labs.com/~mas/RS-APA