Name
Title | ||
|---|---|---|
Iterative voting for inference of structural saliency and characterization of subcellular events. |
Abstract | ||
|---|---|---|
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 gammaH2AX following ionizing radiation. Iterative voting has been implemented in both 2-D and 3-D for multi image analysis. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/TIP.2007.891154 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
geometric voting,circular symmetry,inferring saliency,index terms—foci detection,iterative voting method,subcellular events,segmentation,different level,iterative voting,case example,important attribute,subcellular localization.,important perceptual cue,excellent noise immunity,group nonconvex perceptual,structural saliency,topography,incomplete information,kernel,shape,iterative methods,microscopy,molecular biophysics,image analysis,ionizing radiation,algorithms,symmetry,indexing terms,kinetics,phosphorylation,radial line,noise shaping,artificial intelligence,kinetic theory,voting,image resolution,resolution,surfaces | Kernel (linear algebra),Computer vision,Pattern recognition,Radial line,Salience (neuroscience),Iterative method,Inference,Segmentation,Artificial intelligence,Kernel method,Image resolution,Mathematics | Journal |
Volume | Issue | ISSN |
16 | 3 | 1057-7149 |
Citations | PageRank | References |
57 | 3.48 | 15 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| B. Parvin | 1 | 203 | 19.16 |
| Qing Yang | 2 | 391 | 21.97 |
| Ju Han | 3 | 145 | 8.74 |
| Hang Chang | 4 | 374 | 29.11 |
| Bjorn Rydberg | 5 | 57 | 3.48 |
| Mary Helen Barcellos-Hoff | 6 | 96 | 9.15 |