Fast Computation of the Difference of Low-Pass Transform

This paper defines the difference of low-pass (DOLP) transform and describes a fast algorithm for its computation. The DOLP is a reversible transform which converts an image into a set of bandpass images. A DOLP transform is shown to require O(N2) multiplies and produce O(N log(N)) samples from an N sample image. When Gaussian low-pass filters are used, the result is a set of images which have been convolved with difference of Gaussian (DOG) filters from an exponential set of sizes. A fast computation technique based on ``resampling'' is described and shown to reduce the DOLP transform complexity to O(N log(N)) multiplies and O(N) storage locations. A second technique, ``cascaded convolution with expansion,'' is then defined and also shown to reduce the computational cost to O(N log(N)) multiplies. Combining these two techniques yields an algorithm for a DOLP transform that requires O(N) storage cells and requires O(N) multiplies.

[1]  H. Nyquist,et al.  Certain factors affecting telegraph speed , 1924, Journal of the A.I.E.E..

[2]  N. Wiener,et al.  Fourier Transforms in the Complex Domain , 1934 .

[3]  Helly Fourier transforms in the complex domain , 1936 .

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

[5]  J. Robson,et al.  Spatial-frequency channels in human vision. , 1971, Journal of the Optical Society of America.

[6]  F. W. Campbell,et al.  The Transmission of Spatial Information Through the Visual System , 1973 .

[7]  Alfred V. Aho,et al.  The Design and Analysis of Computer Algorithms , 1974 .

[8]  E. Hall,et al.  Hierarchical search for image matching , 1976, 1976 IEEE Conference on Decision and Control including the 15th Symposium on Adaptive Processes.

[9]  A. Rosenfeld Coarse-fine template matching , 1977 .

[10]  D. Marr,et al.  Representation and recognition of the spatial organization of three-dimensional shapes , 1978, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[11]  T. Poggio,et al.  A computational theory of human stereo vision , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[12]  J. Bergen,et al.  A four mechanism model for threshold spatial vision , 1979, Vision Research.

[13]  S. Ullman,et al.  The interpretation of visual motion , 1977 .

[14]  P. Burt Fast, Hierarchical Correlations with Gaussian-Like Kernels , 1980 .

[15]  James L. Crowley,et al.  Transfer Function Analysis of Picture Processing Operators , 1980 .

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

[17]  Hans P. Moravec Obstacle avoidance and navigation in the real world by a seeing robot rover , 1980 .

[18]  P. J. Burt,et al.  Fast Filter Transforms for Image Processing , 1981 .

[19]  P. Burt Fast filter transform for image processing , 1981 .

[20]  A. Papoulis Systems and transforms with applications in optics , 1981 .

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

[22]  Thomas O. Binford,et al.  Survey of Model-Based Image Analysis Systems , 1982 .

[23]  O. Faugeras,et al.  Sequential convolution techniques for image filtering , 1982 .

[24]  Azriel Rosenfeld,et al.  Compact Region Extraction Using Weighted Pixel Linking in a Pyramid , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  James L. Crowley,et al.  A Representation for Shape Based on Peaks and Ridges in the Difference of Low-Pass Transform , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.