SIGMA: A Knowledge-Based Aerial Image Understanding System by Takashi Matsuyama, Vincent Shang-Shouq Hwang

By Takashi Matsuyama, Vincent Shang-Shouq Hwang

It has lengthy been a dream to gain machines with versatile visible belief potential. examine on electronic photo processing via desktops used to be initiated approximately 30 years in the past, and because then a large choice of photo processing algorithms were devised. utilizing such photo processing algorithms and complicated applied sciences, many sensible ma­ chines with visible attractiveness power were applied and are utilized in a number of fields: optical personality readers and layout chart readers in workplaces, position-sensing and inspection structures in factories, desktop tomography and scientific X-ray and microscope exam structures in hospitals, etc. even if those machines are valuable for particular projects, their features are constrained. that's, they could research merely easy pictures that are recorded lower than very rigorously adjusted photographic stipulations: gadgets to be famous are remoted opposed to a uniform history and below well-controlled synthetic lights. within the overdue Seventies, many snapshot realizing structures have been de­ veloped to check the automated interpretation of advanced common scenes. They brought synthetic intelligence strategies to symbolize the knowl­ area approximately scenes and to gain versatile regulate constructions. the 1st writer built an automated aerial photo interpretation process in accordance with the blackboard version (Naga1980). even if those structures might learn particularly complicated scenes, their features have been nonetheless constrained; the categories of recognizable items have been restricted and diverse popularity vii viii Preface blunders happened because of noise and the imperfection of segmentation algorithms.

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Union) performs its corresponding set operation to produce new data, which then are used as input to another computation node. 5. Constraint (location) network (from Ba1l1982). The most general method of representing geometric relations would be to use geometric transformations between object-centered coordinate systems defining structures of individual objects. In ACRONYM (Bro01981), a geometric relation between parts of objects, which are described by generalized cylinders (Fig. 4), is represented by a transformation matrix which transforms one object-centered coordinate system into another.

These advantages come from the active generation of hypotheses based on the object models, a step which is not included in probabilistic reasoning or optimization methods. As is well known, however, the Hough transform requires both much computation time to generate hypotheses and large memory space to record all generated hypotheses. As will be described in Chapter 2, our spatial reasoning method used in SIGMA shares much with the Hough transform-both advantages and disadvantages. From a philosophical point of view, knowledge-based analysis can be considered as a framework within which to tackle a profound problem in visual perception: the relation between sensation and perception.

GRE first examines the consistency among all pieces of evidence in the database to form what we call situations. Each situation consists of mutually consistent pieces of evidence and represents a local environment (context). GRE selects one situation and focuses its attention on the local environment represented by the selected situation. Then, either the bottom-up analysis to establish a relation between object instances or the top-down analysis to search for a new (missing) object is activated, depending on the nature of the local environment on which it is focusing.

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