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MULTISCALE OBJECT FEATURES FROM CLUSTERED COMPLEX WAVELET COEFFICIENTS
| Content Provider | CiteSeerX |
|---|---|
| Abstract | This paper introduces a method by which intuitive feature entities can be created from ILP (InterLevel Product) coefficients. The ILP transform is a pyramid of decimated complex-valued coefficients at multiple scales, derived from dual-tree complex wavelets, whose phases indicate the presence of different feature types (edges and ridges). We use an Expectation-Maximization algorithm to cluster large ILP coefficients that are spatially adjacent and similar in phase. We then demonstrate the relationship that these clusters possess with respect to observable image content, and conclude with a look at potential applications of these clusters, such as rotation- and scale-invariant object recognition. 1. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Dual-tree Complex Wavelet Ilp Transform Intuitive Feature Entity Multiple Scale Large Ilp Coefficient Decimated Complex-valued Coefficient Interlevel Product Scale-invariant Object Recognition Different Feature Type Potential Application Expectation-maximization Algorithm Observable Image Content |
| Content Type | Text |
| Resource Type | Article |
