In this paper, a kind of knowledge-based automatic target recognition (ATR) technology of radome from infrared
image is studied. The circular imaging of radome is used as the characteristic distinguished from background to realize
target recognition. For the characteristic of low contrast of infrared image, brightness transformation is used to
preliminarily enhance the contrast of the original image. In the light of the fact that target background outline statistically
takes on vertical and horizontal directivity, a kind of revised Sobel operator with direction of 45°and 135°is adopted to
detect edge feature so that background noise is effectively suppressed. To reduce the error ratio of target recognition
from single frame image, the method to inspect the relativity of target recognition results of successive frames is
adopted. The performance of the algorithm is tested using actually taken infrared radome images, and the right
recognition ratio is around 90%, which turns out that this technology is feasible.
Computer vision is gaining significant importance as a cheap, passive, and information-rich sensor in research areas such
as unmanned vehicle. Using computer vision can estimate relative 3D position and attitude. This paper puts forward a
new faster relative 3D position and attitude approach based on special four feature points. This method used prior
knowledge of the four feature points of a square and parallel relation, avoided complicated iterative arithmetic in general
four feature points' methods, and reduced time of objects position and attitude estimation. The theory and experiments
with simulated data showed that the approach is efficient, robust and real-time.
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