Feature detection from local energy
Pattern Recognition Letters
Computation of component image velocity from local phase information
International Journal of Computer Vision
Phase-based disparity measurement
CVGIP: Image Understanding
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns
International Journal of Computer Vision
Multifocus Image Fusion Using Local Phase Coherence Measurement
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Multi-sensor image registration based-on local phase coherence
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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Local phase coherence (LPC) is a recently discovered property that reveals the phase relationship in the vicinity of distinctive features between neighboring complex filter coefficients in the scale-space. It has demonstrated good potentials in a number of image processing and computer vision applications, including image registration, fusion and sharpness evaluation. Existing LPC computation method is restricted to be applied to three coefficients spread in three scales in dyadic scalespace. Here we propose a flexible framework that allows for LPC computation with arbitrary selections in the number of coefficients, scales, as well as the scale ratios between them. In particular, we formulate local phase prediction as an optimization problem, where the object function computes the closeness between true local phase and the predicted phase by LPC. The proposed method not only facilitates flexible and reliable computation of LPC, but also demonstrates strong robustness in the presence of noise. The groundwork laid here broadens the potentials of LPC in future applications.