Pattern recognition and artificial intelligence : towards an by L. N. Kanal, E. S. Gelsema

By L. N. Kanal, E. S. Gelsema

This quantity brings jointly the result of examine into the method and functions of development reputation, with specific emphasis given to the incorporation of man-made intelligence methodologies into trend attractiveness structures. the 1st a part of this quantity covers picture research and processing software program, platforms and algorithms. development research and classifier layout are handled partly , whereas the final half offers with version dependent and specialist structures, together with uncertainty calculus equipment in trend research and item popularity. a couple of particular program components are thought of, together with such different issues as fingerprinting, astronomy, molecular biology and pathology

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Extra info for Pattern recognition and artificial intelligence : towards an integration : proceedings of an international workshop held in Amsterdam, May 18-20, 1988

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By construction they are mutually orthogonal and by proper normalization they form an orthonormal set. At the origin any pattern a(r^) can be expanded or at least approximated with such a set of basis functions. a(r, φ) = Σ n=0 hn(r) e ίηφ (3) where 2ir hn(r)= ΐηφ / β(Γ,φ) - e 0 αφ (4) Thus, we can match the radial variation hn(r) of our basis functions to a specific pattern a(r,4>). Now, assume we want to design an operator set that is matched (or at least tuned) to a certain line profile which is then our target pattern a(r,<|>) We want the operator to be local while the line ideally extends to infinity.

4. The input image b with dimension N x N is first convolved with a number of masks hk(k — 1 . K) with dimension (—m/2 . m/2, —m/2 .. m / 2 ) . m/2 m/2 i=-m/2j=—m/2 The masks should be chosen such t h a t they are sensitive to the features t h a t characterize the texture. The lower the number of masks used, the faster the classification can be done. On the resulting filtered images fk a statistical measure in moving macro windows of dimension ( M + l ) x (M + 1) is computed. Laws found out t h a t variance yielded the best texture classification results: M/2 ek(x,y)= Σ i=-M/2 M/2 Σ j=-M/2 (/*(* + *> y+ i)~ m /fc) 2 · For zero mean masks this is equivalent to an energy computation in the passband of the frequency response of the mask.

SYSTEMS WITH OPTIMAL THROUGHPUT In this section, we discuss the basic differences between such systems and the "optimal time-response" systems which we have analysed in the previous section. Systems with optimal throughput are used for example in industrial inspection systems but also in batch processing of large amounts of image data such as satellite imagery. A typical characteristic of such systems is that the analysis of image I(i+1) is started when the analysis of the previous image I(i) is not finished yet.

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