Fingerprints theorems for zero crossings

We prove that the scale map of the zero crossings of almost all signals filtered by a Gaussian filter of variable size determines the signal uniquely, up to a constant scaling. The proof assumes that the filtered signal can be represented as a polynomial of finite, albeit possibly high, order. The result applies to zero and level crossings of linear differential operators of Gaussian filters. In this case the signal is determined uniquely, modulus the null space of the linear operator. The theorem can be extended to two-dimensional functions. These results are reminiscent of Logan’s theorem [ Bell Syst. Tech. J.56, 487 ( 1977)]. They imply that extrema of derivatives at different scales are a complete representation of a signal. They are especially relevant for computational vision in the case of the Laplacian operator acting on image intensities, and they suggest rigorous foundations for the primal sketch.

[1]  C. Enroth-Cugell,et al.  The contrast sensitivity of retinal ganglion cells of the cat , 1966, The Journal of physiology.

[2]  J. Robson,et al.  Application of fourier analysis to the visibility of gratings , 1968, The Journal of physiology.

[3]  D. Widder The heat equation , 1975 .

[4]  D Marr,et al.  Early processing of visual information. , 1976, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[5]  B. Logan Information in the zero crossings of bandpass signals , 1977, The Bell System Technical Journal.

[6]  D Marr,et al.  A computational theory of human stereo vision. , 1979, Proceedings of the Royal Society of London. Series B, Biological sciences.

[7]  D Marr,et al.  Bandpass channels, zero-crossings, and early visual information processing. , 1979, Journal of the Optical Society of America.

[8]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[9]  D. Marr,et al.  An Information Processing Approach to Understanding the Visual Cortex , 1980 .

[10]  James L Stansfield,et al.  Conclusions from the Commodity Expert Project , 1980 .

[11]  D. Marr,et al.  Smallest channel in early human vision. , 1980, Journal of the Optical Society of America.

[12]  W. Eric L. Grimson,et al.  From images to surfaces , 1981 .

[13]  J. Crowley A representation for visual information , 1981 .

[14]  H. Keith Nishihara,et al.  Intensity, Visible-Surface, and Volumetric Representations , 1981, Artif. Intell..

[15]  T. Poggio,et al.  Zero-Crossings and Spatiotemporal Interpolation in Vision: Aliasing and Electrical Coupling between Sensors. , 1982 .

[16]  David Marr,et al.  Vision: A computational investigation into the human representation , 1983 .

[17]  W. Eric L. Grimson,et al.  Surface consistency constraints in vision , 1982, Comput. Graph. Image Process..

[18]  A. Rosenfeld,et al.  Quadtrees and Pyramids: Hierarchical Representation of Images , 1983 .

[19]  Andrew P. Witkin,et al.  Scale-Space Filtering , 1983, IJCAI.

[20]  Alan L. Yuille,et al.  Fingerprints Theorems , 1984, AAAI.

[21]  Michael Brady,et al.  The Curvature Primal Sketch , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Alan L. Yuille,et al.  Scaling Theorems for Zero Crossings , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Andrew P. Witkin,et al.  Uniqueness of the Gaussian Kernel for Scale-Space Filtering , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.