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where c is a constant representing the influence of
the current image in each ground image update, and c3 is a constant representing the frequency at which new ground images are produced. Both these parameters are obtained in a heuristic fashion or through the decisions of higher-level processes.
2.4. Figure image segmentation
Once a figure image has been obtained, we can consider the task of using the information that it provides to identify objects of interest. Unfortu- nately, figure images tend to contain pixels that belong to a variety of items other than just the objects of interest. For example, one may find that the figure image indicates activity caused by shadows, camera jitter, or salt-and-pepper noise. Another problem that appears with the analysis of figure images is that an image may contain multiple objects of interest. In order to distinguish better among several objects, the figure image is partitioned into segments each of which represents a single object. This figure image segmentation has a beneficial side- effect of identifying and removing many instances of the problem cases described above.
The figure image segmentation is achieved through a single pass of a sequential labeling algorithm 25
Because the algorithm creates segments from only a single pass through the figure image, all statistics that are to be calculated for the segments must be obtained dynamically. The selection of which statistics to calculate depends on the segment analysis (see Section 3. I). In some cases, it may suffice to maintain a size (pixel count) and minimum bounding rectangle.
By following the previous algorith 25 without
further modification, there can be as many as several hundred segments generated in a single image, only a few of which describe actual objects of interest. Since the computational performance of object detection relies on keeping the number of considered segments to a minimum, modifications must be made in order to make the process more efbcient. After each scanline is processed, an analysis is made of all segments that have been completely processed by previous scanlines. If any of these completed segments are found to have different dimensions than those of a typical object of interest, then they are removed from the segment collection data structure. In practice, this pruning removes a large number of the undesirable sources of figure segments. By continuously minimizing the number of segments in the data structure, this process also improves the speed at which the segmentation can occur.
(a) (b)
(d)
(a) A manipulator has positioned a camera looking down upon a wooden platform. The system has initially stored a ground image in memory.
(b) After a short period of time, a target (a remotely controlled toy car) appears under- neath the robot.
(c) A current image of the scene with a moving target.
(d) The target becomes readily apparent, as a figure image is formed by comparing the current and the ground images.
Fig. 1. Construction of a figure image.
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