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FIGURE 8.9
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Computer plot of the contents of a frame.
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FIGURE 8.10 Result of applying edge extraction operation.
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however, to reset each edge point as it is traced. The tracing of a complete object thus removes it from the frame and ensures it will not be subsequently retraced.
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End Effector Camera Sensor Detecting Partially Visible Objects
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A new method of locating partially visible two-dimensional objects has been developed. The method is applicable to complex industrial parts that may contain several occurrences of local features, such as holes and corners. The matching process utilizes clusters of mutually consistent features to hypothesize objects and uses templates of the objects to verify these hypotheses. The technique is fast because it concentrates on key features that are automatically selected on the basis of detailed analysis of CAD-type models of the objects. The automatic analysis applies general-purpose routines for building and analyzing representations of clusters of local features that could be used in procedures to select features for other locational strategies. These routines include algorithms to compute the rotational and mirror symmetries of objects in terms of their local features. The class of tasks that involve the location of the partially visible object ranges from relatively easy tasks, such as locating a single two-dimensional object, to the extremely difficult task of locating three-dimensional
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objects jumbled together in a pallet. In two-dimensional tasks, the uncertainty is in the location of an object in a plane parallel to the image plane of the camera sensor. This restriction implies a simple one-toone correspondence between sizes and orientations in the image, on the one hand, and sizes and orientations in the plane of the object, on the other. This class of two-dimensional tasks can be partitioned into four subclasses that are defined in terms of the complexity of the scene: A portion of one of the objects Two or more objects that may touch one another Two or more objects that may overlap one another One or more objects that may be defective This list is ordered roughly by the increasing amount of effort required to recognize and locate the object. Figure 8.11 illustrates a portion of an aircraft frame member. A typical task might be to locate the pattern of holes for mounting purposes. Since only one frame member is visible at a time, each feature appears at most once, which simplifies feature identification. If several objects can be in view simultaneously and can touch one another, as in Fig. 8.12, the features may appear several times. Boundary features such as corners may not be recognizable, even though they are in the picture, because the objects are in mutual contact. If the objects can lie on one another (Fig. 8.13), even some of the internal holes may
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FIGURE 8.11
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Portion of an aircraft frame member.
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FIGURE 8.12 Objects touching each other.
FIGURE 8.13 Objects lying on top of each other.
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FIGURE 8.14
Trained image.
be unrecognizable because they are partially or completely occluded. And, finally, if the objects are defective (Fig. 8.14), the features are even less predictable and hence harder to find. Since global features are not computable from a partial view of an object, recognition systems for these more complex tasks are forced to work with either local features, such as small holes and corners, or extended features like a large segment of an object s boundary. Both types of feature, when found, provide constraints on the position and the orientations of their objects. Extended features are in general computationally more expensive to find, but they provide more information because they tend to be less ambiguous and more precisely located. Given a description of an object in terms of its features, the time required to match this description with a set of observed features appears to increase exponentially with the number of features. The multiplicity of features precludes the straightforward application of any simple matching technique. Large numbers of features have been identified by locating a few extended features instead of many local ones. Even though it costs more to locate extended features, the reduction in the combinatorial explosion is often worth it. The other approach is to start by locating just one feature and use it to restrict the search area for nearby features. Concentrating on one feature may be risky, but the reduction in the total number of features to be considered is often worth it. Another approach is to sidestep the
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