Iterative Voting for Inference of Structural Saliency and Characteriztion
of Subcellular Events
IEEE Transaction on Image Processing, March 2007
Saliency is an important perceptual cue that occurs at
different levels of resolution. Important attributes of saliency are
symmetry, continuity, and closure. Detection of these attributes
is often hindered by noise, variation in scale, and incomplete
information. This paper introduces the iterative voting method,
which uses oriented kernels for inferring saliency as it relates to
symmetry. A unique aspect of the technique is the kernel topography,
which is refined and reoriented iteratively. The technique
can cluster and group nonconvex perceptual circular symmetries
along the radial line of an object's shape. It has an excellent noise
immunity and is shown to be tolerant to perturbation in scale.
The application of this technique to images obtained through
various modes of microscopy is demonstrated. Furthermore, as a
case example, the method has been applied to quantify kinetics
of nuclear foci formation that are formed by phosphorylation of
histone gamaH2AX following ionizing radiation. Iterative voting has
been implemented in both 2-D and 3-D for multi image analysis.
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