By Makoto Nagao
It is splendid that the 1st quantity to seem within the sequence "Advanced purposes 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 methods to pat tern popularity: numerical (also referred to as "statistical") and struc tural ("linguistic," "syntactic"). the facility and team spirit of the meth odology move from the it seems that easy and typical use of the knowledge-base framework illuminated by way of the simplest result of man made intelligence study. a vital part of the paintings is the algorithmic answer of many hitherto incompletely or clumsily taken care of difficulties. It was once at the social gathering 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 method defined the following. immediate I expressed the need to work out the paintings defined for the inter nationwide technical viewers during this sequence and the authors have been type 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 would like to thank right here the authors and Plenum Publishing company for making this quantity and the sequence a reality.
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Extra info for A Structural Analysis of Complex Aerial Photographs
Suppose that a picture has two regions Rl and R2 whose graylevel means and variances are (0, cri) and (m, cr~) respectively. 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.
Global survey of the whole scene. Several kinds of regions with characteristic properties, which we call "characteristic regions", are extracted from the segmented picture. These characteristic regions are used to estimate approximate domains of objects. The extraction of the characteristic regions is performed in parallel by a set of "characteristic region extractors" (Fig. 2). In this process no knowledge about specific objects is used. The only knowledge involved is the general knowledge about the aerial photograph under analysis, such as photographic conditions and physical characteristics of the pictorial data: the direction of the sun, the size of a pixel on the ground, spectral bands of the aerial photograph, and so on.
In a production system, all the information is stored in the blackboard and each rule can read it at any time, so that we can easily implement a rule which performs a goal-oriented analysis. That is, once some object-detection subsystem locates an object, the properties of the object are stored in the blackboard. This enables other subsystems to utilize that information to detect "ambiguous" objects which were not recognized because of lack of reliability. A premature recognition of such ambiguous objects would have created a lot of "false objects".
A Structural Analysis of Complex Aerial Photographs by Makoto Nagao