Sequential edge detection using joint hierarchical Bayesian learning
This addresses edge detection in sequential imaging for applications like medical or surveillance imaging, but it is incremental as it builds on existing SBL methods.
The paper tackles the problem of recovering temporal sequences of edge maps from noisy, under-sampled Fourier data by introducing a new sparse Bayesian learning algorithm that jointly processes images, avoiding separate change detection. The result shows favorable performance compared to standard SBL approaches in numerical examples.
This paper introduces a new sparse Bayesian learning (SBL) algorithm that jointly recovers a temporal sequence of edge maps from noisy and under-sampled Fourier data. The new method is cast in a Bayesian framework and uses a prior that simultaneously incorporates intra-image information to promote sparsity in each individual edge map with inter-image information to promote similarities in any unchanged regions. By treating both the edges as well as the similarity between adjacent images as random variables, there is no need to separately form regions of change. Thus we avoid both additional computational cost as well as any information loss resulting from pre-processing the image. Our numerical examples demonstrate that our new method compares favorably with more standard SBL approaches.