By Ronald L. Lipsman, Denny Gulick
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Extra info for Conference on Harmonic Analysis, College Park, Maryland, 1971; [papers]
Shows the three filters measurement with which is considered in this paper. The filters shown are of scale s = 24 , and are plotted over the range x ∈ [−32, 32] . The ‘aperture’ function is a 0th order Gaussian of scale 2s , it will be relevant in section 3. The vertical dashed lines mark the outermost inflexions of the 2nd order filter Image Features and the 1-D, 2nd Order Gaussian Derivative Jet 29 2 Norm-Minimizing Metameres Measurement of local structure with a finite number of DtG filters does not fully determine the measured image even locally.
127-142. 10. , The Gaussian derivative model for spatial vision: I. Retinal mechanisms. Spatial Vision, 1987. 2: p. 273-293. 11. M. W. Meyer, The Gaussian Derivative model for spatial-temporal vision: I. Cortical model. Spatial Vision, 2001. 14(3-4): p. 261-319. 12. A. M. Lesperance, The Gaussian Derivative model for spatial-temporal vision: II. Cortical data. Spatial Vision, 2001. 14(3-4): p. 321-389. 13. J. J. van Doorn, Representation of Local Geometry in the VisualSystem. Biological Cybernetics, 1987.
However, it retains many similarities with local quadrature analysis, a technique predominantly applied to feature detection and classiﬁcation. In this section, we consider both applications of the method described in the previous sections. Indeed, it is hoped that since the new method combines aspects of both approaches, it may be possible to design symmetry inspired methods for detecting, labelling, grouping and interpreting features. To this end, we have applied the local symmetry estimation method to simultaneous feature detection and symmetry axis detection.
Conference on Harmonic Analysis, College Park, Maryland, 1971; [papers] by Ronald L. Lipsman, Denny Gulick