Distance Transform Gradient Density Estimation using the Stationary Phase Approximation

Details Distance Transform Gradient Density Estimation using the Stationary Phase Approximation Distance Transform Gradient Density Estimation using the Stationary Phase Approximation

2011-04-19

arxiv.org/abs/1104.3621v3

Statistics

Machine Learning

24 pages, 5 figures

Scientific paper

The complex wave representation (CWR) converts unsigned 2D distance transforms into their corresponding wave functions. Here, the distance transform S(X) appears as the phase of the wave function \phi(X)---specifically, \phi(X)=exp(iS(X)/\tau where \tau is a free parameter. In this work, we prove a novel result using the higher-order stationary phase approximation: we show convergence of the normalized power spectrum (squared magnitude of the Fourier transform) of the wave function to the density function of the distance transform gradients as the free parameter \tau-->0. In colloquial terms, spatial frequencies are gradient histogram bins. Since the distance transform gradients have only orientation information (as their magnitudes are identically equal to one almost everywhere), as \tau-->0, the 2D Fourier transform values mainly lie on the unit circle in the spatial frequency domain. The proof of the result involves standard integration techniques and requires proper ordering of limits. Our mathematical relation indicates that the CWR of distance transforms is an intriguing, new representation.

Affiliated with

Also associated with

No associations

Rating

Distance Transform Gradient Density Estimation using the Stationary Phase Approximation does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Distance Transform Gradient Density Estimation using the Stationary Phase Approximation, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Distance Transform Gradient Density Estimation using the Stationary Phase Approximation will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-182195