By Topic

Fast image transforms using diophantine methods

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Chandran, S. ; Dept. of Comput. Sci. & Eng., Indian Inst. of Technol., Mumbai, India ; Potty, A.K. ; Sohoni, M.

Many image transformations in computer vision and graphics involve a pipeline when an initial integer image is processed with floating point computations for purposes of symbolic information. Traditionally, in the interests of time, the floating point computation is approximated by integer computations where the integerization process requires a guess of an integer. Examples of this phenomenon include the discretization interval of ρ and θ in the accumulator array in classical Hough transform, and in geometric manipulation of images (e.g., rotation, where a new grid is overlaid on the image). The result of incorrect discretization is a poor quality visual image, or worse, hampers measurements of critical parameters such as density or length in high fidelity machine vision. Correction techniques include, at best, anti-aliasing methods, or more commonly, a "kludge" to cleanup. In this paper, we present a method that uses the theory of basis reduction in Diophantine approximations; the method outperforms prior integer based computation without sacrificing accuracy (subject to machine epsilon).

Published in:

Image Processing, IEEE Transactions on  (Volume:12 ,  Issue: 6 )