NDLI: A prototype filter design approach to pyramid generation

Content Provider	IEEE Xplore Digital Library
Author	Min Gi Kim Dinstein, I. Shaw, L.
Copyright Year	1979
Abstract	This paper presents a technique for image pyramid generation, in which the reduction (expansion) factor between layers is any rational number M/L. The image pyramid generation is modeled as an interpolation and filtering followed by a decimation. The model enables frequency domain analysis of the image pyramid, as well as convenient design of the generating kernels. L(M) generating kernels are necessary to produce an image pyramid with reduction (expansion) factor M/L(L/M). A polyphase filter network scheme is used where the L(M) generating kernels can be produced by sampling one prototype low-pass filter with cutoff frequency at omega = pi /max(M,L). Using these polyphase filters, the frequency content of pyramid image decompositions can be adjusted with great flexibility. A systematic procedure is presented here for specifying the relative positions of spatial samples in successive pyramid levels-a complication that arises when generalizing from integer reduction factors to rational factors. Two types of low-pass filters are employed in this work for the prototype filter design: a binomial filter and an FIR linear phase filter. Illustrative examples are presented.<>
Sponsorship	IEEE Computer Society
Starting Page	1233
Ending Page	1240
Page Count	8
File Size	825632
File Format	PDF
ISSN	01628828
Volume Number	15
Issue Number	12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1993-12-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Prototypes Finite impulse response filter Low pass filters Nonlinear filters Kernel Image generation Interpolation Filtering Frequency domain analysis Image sampling
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Computational Theory and Mathematics Computer Vision and Pattern Recognition Software

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