By Makoto Nagao
It really is well suited that the 1st quantity to seem within the sequence "Advanced functions in trend popularity" may be this monograph by way of Nagao and Matsuyama. The paintings defined here's a deep unification and synthesis of the 2 basic techniques to pat tern popularity: numerical (also often called "statistical") and struc tural ("linguistic," "syntactic"). the facility and harmony of the meth odology stream from the it appears easy and traditional use of the knowledge-base framework illuminated by means of the simplest result of man made intelligence examine. an essential component of the paintings is the algorithmic resolution of many hitherto incompletely or clumsily handled difficulties. It was once at the get together of a laboratory stopover at in reference to the 4th IJCPR (of ~hich Professor Nagao was once the very capable application Chairman) that I observed in operation the process defined the following. instantaneous I expressed the need to determine the paintings defined for the inter nationwide technical viewers during this sequence and the authors have been variety adequate to conform to give a contribution to a brand new and unknown sequence. With the e-book of this monograph at the eve of the fifth ICPR my want is fu1fi11~d. i need to thank the following the authors and Plenum Publishing company for making this quantity and the sequence a truth.
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Extra info for A Structural Analysis of Complex Aerial Photographs (Advanced Applications in Pattern Recognition)
1. Specification of the Aerial Photographs Under Analysis Before the development of remote sensing technology, only black and white (color) aerial photographs were available for surveying situations on the ground, and the analysis of the photographs was made by human specialists via visual inspection. This process, however, not only requires very specialized skills but also tends to be subjective. That is, the result of the analysis relies heavily on subjective judgements by an analyst and hence lacks objectivity.
The original picture has three different regions with the average gray levels 48, 108, and 168, respectively. Gaussian noise has been added to this pattern with the standard deviations of (a) 10, (b) 20, (c) 30, (d) 40, and (e) 50, respectively. The noisy pictures on the top row of Fig. 5 hav~ been made by this operation. The pictures on the middle row show the results of the edge-preserving smoothing after ten iterations. The pictures on the bottom row show the edges of the smoothed pictures as the result of an ordinary differentiation and thresholding.
Let a point (X, Y) belong to Rl. If (X, Y) is located at the central part of Rl, its gray-level approaches to the average graylevel of Rl (in this case 0) after several iterations of smoothing. On the other hand, if (X, Y) is near the boundary, there exist two kinds of neighborhoods, one of which is completely included in Rl 36 3. SOME BASIC TECHNIQUES IN PICTURE PROCESSING while the other includes parts of both Rl and R2. ab out a 2 • t h e f ormer 1S 1 culated as follows. The variance of The variance a 2 of the latter can be cal- Let N1 and N2 denote the numbers of points be- longing to Rl and R2, respectively, which are contained in this neighborhood.