Abstract
We introduce a sinusoidal image model consisting of an oriented sinusoid plus a residual component. The model parameters are derived from the circular harmonic vector, a representation of local image structure consisting of the responses to the higher-order Riesz transforms of an isotropic wavelet. The vector is split into sinusoidal and residual components. The sinusoidal component gives a phase-based description of the dominant local linear symmetry, with improved orientation estimation compared to previous sinusoidal models. The residual component describes the remaining parts of the local structure, from which a complex-valued representation of intrinsic dimension is derived. The usefulness of the model is demonstrated for corner and junction detection and parameter-driven image reconstruction.
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