Licentiate seminar for Erik Ringaby
Title: Geometric Computer Vision for Rolling-shutter and Push-broom Sensors
Respondent: Msc Erik Ringaby
Opponent: Professor Anders Heyden, LTH
Place: Visionen, B-builing, Campus Valla
Almost all cell-phones and camcorders sold today are equipped with a CMOS (Complementary Metal Oxide Semiconductor) image sensor and there is also a general trend to incorporate CMOS sensors in other types of cameras. The sensor has many advantages over the more conventional CCD (Charge-Coupled
Device) sensor such as lower power consumption, cheaper manufacturing and the potential for on-chip processing. Almost all CMOS sensors make use of what is called a rolling shutter. Compared to a global shutter, which images all the pixels at the same time, a rolling-shutter camera exposes the image row-by-row.
This leads to geometric distortions in the image when either the camera or the objects in the scene are moving. The recorded videos and images will look wobbly (jello effect), skewed or otherwise strange and this is often not desirable. In addition, many computer vision algorithms assume that the camera used has a global shutter, and will break down if the distortions are too severe.
In airborne remote sensing it is common to use push-broom sensors. These sensors exhibit a similar kind of distortion as a rolling-shutter camera, due to the motion of the aircraft. If the acquired images are to be matched with maps or other images, then the distortions need to be suppressed.
The main contributions in this thesis are the development of the three dimensional models for rolling-shutter distortion correction. Previous attempts modelled the distortions as taking place in the image plane, and we have shown that our techniques give better results for hand-held camera motions.
The basic idea is to estimate the camera motion, not only between frames, but also the motion during frame capture. The motion can be estimated using inter- frame image correspondences and with these a non-linear optimisation problem can be formulated and solved. All rows in the rolling-shutter image are imaged at different times, and when the motion is known, each row can be transformed to the rectified position.
In addition to rolling-shutter distortions, hand-held footage often has shaky camera motion. It has been shown how to do efficient video stabilisation, in combination with the rectification, using rotation smoothing.
In the thesis it has been explored how to use similar techniques as for the rolling- shutter case in order to correct push-broom images, and also how to rectify 3D point clouds from e.g. the Kinect depth sensor.