46,750 research outputs found
ORGB: Offset Correction in RGB Color Space for Illumination-Robust Image Processing
Single materials have colors which form straight lines in RGB space. However,
in severe shadow cases, those lines do not intersect the origin, which is
inconsistent with the description of most literature. This paper is concerned
with the detection and correction of the offset between the intersection and
origin. First, we analyze the reason for forming that offset via an optical
imaging model. Second, we present a simple and effective way to detect and
remove the offset. The resulting images, named ORGB, have almost the same
appearance as the original RGB images while are more illumination-robust for
color space conversion. Besides, image processing using ORGB instead of RGB is
free from the interference of shadows. Finally, the proposed offset correction
method is applied to road detection task, improving the performance both in
quantitative and qualitative evaluations.Comment: Project website: https://baidut.github.io/ORGB
A rejection-free Monte Carlo method for the hard-disk system
We construct a rejection-free Monte Carlo method for the hard-disk system.
Rejection-free Monte Carlo methods preserve the time-evolution behavior of the
standard Monte Carlo method, and this relationship is confirmed for our method
by observing nonequilibrium relaxation of a bond-orientational order parameter.
The rejection-free method gives a greater computational efficiency than the
standard method at high densities. The rejection free method is implemented in
a shrewd manner using optimization methods to calculate a rejection probability
and to update the system. This method should allow an efficient study of the
dynamics of two-dimensional solids at high density.Comment: 8 pages, 9 figures. This paper has been combined into the
cond-mat/0508652, and published in Phys. Rev.
Traffic monitoring using image processing : a thesis presented in partial fulfillment of the requirements for the degree of Master of Engineering in Information and Telecommunications Engineering at Massey University, Palmerston North, New Zealand
Traffic monitoring involves the collection of data describing the characteristics of vehicles and their movements. Such data may be used for automatic tolls, congestion and incident detection, law enforcement, and road capacity planning etc. With the recent advances in Computer Vision technology, videos can be analysed automatically and relevant information can be extracted for particular applications. Automatic surveillance using video cameras with image processing technique is becoming a powerful and useful technology for traffic monitoring. In this research project, a video image processing system that has the potential to be developed for real-time application is developed for traffic monitoring including vehicle tracking, counting, and classification. A heuristic approach is applied in developing this system. The system is divided into several parts, and several different functional components have been built and tested using some traffic video sequences. Evaluations are carried out to show that this system is robust and can be developed towards real-time applications
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