This paper presents an aircraft recognition system, which addresses realistic concerns resulting from the imaging process and the environment surrounding the aircraft. The system employs a bottom up approach, where recognition begins by extracting low level features (e.g., lines), which are subsequently combined into more complex sets of line groupings representing parts of an aircraft, as viewed from a generic viewpoint. Knowledge about aircraft is represented in the form of whole/part shape description and the connectedness property, and is embedded in production rules, which primarily aim at finding instances of the aircraft parts in the image and checking the connectedness property between the parts. The system has demonstrated robustness against occlusion, shadows, excessive background clutter and many forms of image degradation.
Automatic aircraft recognition is very complex because of clutter, shadows, clouds, self-occlusion and degraded imaging conditions. This paper presents an aircraft recognition system, which assumes from the start that the image is possibly degraded, and implements a number of strategies to overcome edge fragmentation and distortion. The current vision system employs a bottom up approach, where recognition begins by locating image primitives (e.g., lines and corners), which are then combined in an incremental fashion into larger sets of line groupings using knowledge about aircraft, as viewed from a generic viewpoint. Knowledge about aircraft is represented in the form of whole/part shape description and the connectedness property, and is embedded in production rules, which primarily aim at finding instances of the aircraft parts in the image and checking the connectedness property between the parts. Once a match is found, a confidence score is assigned and as evidence in support of an aircraft interpretation is accumulated, the score is increased proportionally. Finally a selection of the resulting image interpretations with the highest scores, is subjected to competition tests, and only non-ambiguous interpretations are allowed to survive. Experimental results demonstrating the effectiveness of the current recognition system are given.
This work presents a geometry based vision system for aircraft recognition and pose estimation using single images. Pose estimation improves the tracking performance of guided weapons with imaging seekers, and is useful in estimating target manoeuvres and aim-point selection required in the terminal phase of missile engagements. After edge detection and straight-line extraction, a hierarchy of geometric reasoning algorithms is applied to form line clusters (or groupings) for image interpretation. Assuming a scaled orthographic projection and coplanar wings, lateral symmetry inherent in the airframe provides additional constraints to further reject spurious line clusters. Clusters that accidentally pass all previous tests are checked against the original image and are discarded. Valid line clusters are then used to deduce aircraft viewing angles. By observing that the leading edges of wings of a number of aircraft of interest are within 45 to 65 degrees from the symmetry axis, a bounded range of aircraft viewing angles can be found. This generic property offers the advantage of not requiring the storage of complete aircraft models viewed from all aspects, and can handle aircraft with flexible wings (e.g. F111). Several aircraft images associated with various spectral bands (i.e. visible and infra-red) are finally used to evaluate the system's performance.
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