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The traditional method to extract target contour from aerial target image is changing the aerial image into a gray level image with multiple thresholds or binary image with single threshold. From the edge of target, contour can be extracted according to the changed value. The traditional method is useful only when contrast between target and background is in the proper degree. Snakes are curves defined within an image domain that can move under the influence of internal force coming from within the curve itself and external forces are defined so that the snake will conform to an object boundary or other desired features within an image. Snakes have been proved an effective method and widely used in image processing and computer vision. Snakes synthesize parametric curves within an image domain and allow them to move toward desired edges. Particular advantages of the GVF(Gradient Vector Flow) snakes over a traditional snakes are its insensitivity to initialization and its ability to move into boundary concavities. Its initializations can be inside, outside, or across the object’s boundary. The GVF snake does not need prior knowledge about whether to shrink or expand toward the boundary. This increased capture range is achieved through a diffusion process that does not blur the edges of themselves.
Affected by the light from different incident angle, the brightness of aerial target surface changed greatly in a complicate mode. So the GVF snakes is not fast, accurate and effective all the time for this kind of images. A new contour extracting method, GVF Snakes Combined with wavelet multi-resolution Analysis is proposed in this paper. In this algorithm, bubble wavelet is used iteratively to do the multi resolution analysis in the order of degressive scale before GVF Snakes is used every time to extract accurate contour of target. After accurate contour is extracted, polygon approximation is used to extract characteristics to realize the recognition of aerial target. The process is in the following: Step 1: use bubble wavelet filter to cut big part of the noises, weakening false edges. Step 2: initialize active contour and control the contour’s move according to GVF to get a new contour. Step 3: decrease the scale of filter, and use the new contour as the initial contour and control the contour’s move to get new contour again. Step 4: repeat step 3 till the set scale is reached. The last new contour is the final contour. Step 5: find the center determine an axis by calculate distance between every point on the final contour to the center. Step 6: adjust the distance threshold and combine the points until the contour is changed into a polygon with fixed angle number which is best fit the target recognition demand. Step 7: use the polygon to match the target plate to recognize target. Applied the new algorithm to aerial target images of a helicopter and a F22 battleplan, the contour extraction and polygon approximation results show that targets can be matched and recognized successfully. This paper mainly focuses on contour extraction and polygon approximation in the recognition area.
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