Searching for a particular pattern in an image is a well-known problem in the art of machine vision, with many known solutions, such as feature-based search methods. Typically, the pattern is assumed to have undergone one or more of a few basic known transformations, such as having been scaled larger or smaller than the original pattern, or having been rotated. However, these known solutions often fail if the pattern has been deformed by being warped, pulled, bent, wrinkled, damaged, or otherwise fundamentally changed from the original pristine shape, or known transformations thereof, that the search method is adapted to find.
However, if the deformed pattern is broken down into smaller sub-patterns, those individual sub-patterns are themselves fairly similar to the corresponding parts of the original pattern bearing only minor deformation. For example, if the pattern has been bent into a “V” or boomerang shape, then the two legs of the boomerang both have good, easily found pieces of the pattern. Therefore, searching for a deformed pattern in an image may be facilitated by dividing the pattern into smaller sub-patterns, because for many typical types of deformation encountered, most of those sub-patterns can probably be found by known feature-based search methods because they are not themselves substantially distorted or deformed. Then, a subsequent algorithm can combine these partial results into a full match of the pattern.
The question is then how to divide the pattern into smaller sub-patterns. A human being can likely examine a large pattern, determine useful parts that will probably survive whatever deformation the image is expected to encounter, and divide it accordingly. However, automating the process is more difficult.
The obvious and standard way to automatically divide a pattern image into sub-patterns is to use a rectilinear grid, such as a tic-tac-toe grid or a checker board grid. Super-imposing such a grid over the main pattern gives, for example, nine smaller sub-patterns. However, this method has significant drawbacks. Pattern matching is based on matching information in the source pattern with information in the target image. But useful information is not usually present uniformly throughout a pattern. Some of the sub-patterns selected with a grid may be blank, and consequently have no useful information contained therein. Some grid-based sub-patterns may have a small amount of information in one corner of the grid square and no information in the rest of it. And some may have a lot of dense information that would be more usefully split into smaller pieces. Further, grids divide the information of a pattern arbitrarily and indiscriminately. If, for example, part of the pattern is a small star, the grid lines might break that star up into two or even four parts, where a human operator would likely choose to keep the star together as one single, dense, easily found sub-pattern.