Many different data processing techniques have been used to identify objects in photographs or other datasets. Such techniques have been widely deployed in commercial, military, industrial and other situations. In a military setting, for example, digital imagery obtained from an aircraft or other source can be processed to identify the presence of missiles, unfriendly aircraft, or other objects of interest. Various processing techniques can allow for early detection of threats to aircraft, ships, vehicles, persons or structures that might otherwise be difficult to visually detect. Object recognition techniques are also used in other civilian and military aerospace and maritime settings (including underwater object detection), as well as in manufacturing and other industrial settings, commercial and personal photography, and in many other settings as well.
Generally speaking, it is desirable that such processing techniques be effective at identifying objects, be relatively fast, and be computationally efficient. A number of prior techniques that have been used to identify objects within images include Wiener filters, Mahalanobis filters, square-law filters and others. Each of these techniques, however, can exhibit marked disadvantages in certain settings. Wiener filters, for example, typically recognize patterns based upon previously-known information. If the patterns of certain targets are not known or if the target orientation, range or shape has varied from the a priori data provided to the Wiener filter, than the effectiveness of the filter can be significantly degraded. Square law filters, although not necessarily based upon a priori data, can have the undesired effect of amplifying background noise. Mahalanobis and Wiener filters commonly exhibit a further disadvantage in that they can be computationally intense in that they often require significant computing resources (e.g., processor cycles) to execute effectively. This can result in burdens on computing hardware, delays in obtaining results, and other undesired effects.
It is therefore desirable to create data processing systems and techniques that are effective, computationally manageable, and that are flexible enough to operate effectively without significant a priori data. These and other desirable features and characteristics will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and this background section.
USPTO Backgrounds
USPTO Backgrounds