Real Projective Plane Mapping for Detection of Orthogonal Vanishing Points
published: April 3, 2014, recorded: September 2013, views: 2649
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
This paper deals with the detection of orthogonal vanishing points. The aim is to
efficiently cope with the clutter edges in real-life images and to determine the camera
orientation in the Manhattan world reliably. We are using a modified scheme of the
Cascaded Hough Transform where only one Hough space is accumulated – the space
of the vanishing points. The parameterization of the vanishing points – the “diamond
space” – is based on the PClines line parameterization and it is defined as a mapping of
the whole real projective plane to a finite space.
Our algorithm for detection of vanishing points operates directly on edgelets detected by an edge detector, skipping the common step of grouping edges into straight lines or line segments. This decreases the number of configuration parameters and reduces the complexity of the algorithm. Evaluated on the York Urban DB, our algorithm yields 98.04% success rate at 10 angular error tolerance, which outperforms comparable existing solutions.
Our parameterization of vanishing points is in all aspects linear; it involves no goniometric or other non-linear operations and thus it is suitable for implementation in embedded chips and circuitry. The iterative search scheme allows for finding orthogonal triplets of vanishing points with high accuracy and low computational costs. At the same time, our approach can be used without the orthogonality constraint.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !