According to the FLIR image of complex ground fixed target have disordered gray difference between target and
background, and no base image available, we propose a target detection algorithm in FLIR imagery using scale space
theory. First the reference image been created depending on the satellite dates, 2D Forward-looking image sequences of
target regions created by using VRMap software according to the flying route. After performing LoG filtering on these
reference images, we obtain a series of characteristic scale, where each scale is related to the size of target. Using these
scales, several blob-like candidate regions can be found with the same size with reference image. Then the result is
obtained between candidate regions and the reference image using Hausdorff measure and centroid distance. A
simulation experiment is developed for the proposed algorithm and results show that our method has high accuracy and
quickly performance and it has strong practicability.
In this paper, we present a high performance, coarse-to-fine scheme for image matching of infrared image and optical
image. In order to eliminate the gray level difference and tolerance to distortion and noise existing in practice, this
scheme uses the edge feature and combine a new similarity measures with modified Hausdorff distance to achieve the
coarse-to-fine matching scheme. Our proposed method firstly extracts the feature points based on the method of Monte
Carlo that reduce the computation load for the next matching, and then a new similarity measure is defined for coarse
matching. Based on existing method of Monte Carlo evaluation the Huasdorff distance (MCHD), we define Monte Carlo
modified Hausdorff distance (MCM-HD) to achieves the fine matching. Experiment are performed on a large test set and
the result show that the fast search method diminishes the number of positions for calculate of Hausdorff distance, thus
the computational load reduces and it is helpful to use Hausdorff distance in real time image matching. Compare with
MCHD algorithm, our proposed method effectively improves the precision and reduces the execution time.
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