Accurate object location is a key problem in image measurement system and the basis of other image processing procedures. In our project, we aim to locate some chromatic round objects in a large field. In this paper, the regions where the objects lie are isolated firstly from the original image using their color information, in which the edges of objects are detected. A novel fast method for fitting a ellipse to the edge points in the region is presented, by which the geometric distortion is calibrated by the ellipse model calculated linearly according to the position of the region. And the position of object with the precision of one pixel is obtained through edge fitting. Finally the accurate data in 1/5 pixel result from subpixel subdivision.

Accurate object location is a key problem in image measurement system and the basis of other image processing procedures. In our project, we aim to locate some chromatic round objects in a large field. In this paper, the regions where the objects lie are isolated firstly from the original image using their color information, in which the edges of objects are detected. A novel fast method for fitting a ellipse to the edge points in the region is presented, by which the geometric distortion is calibrated by the ellipse model calculated linearly according to the position of the region. And the position of object with the precision of one pixel is obtained through edge fitting. Finally the accurate data in 1/5 pixel result from subpixel subdivision.

本文介绍了一种基于半像素错位的多幅图象重建超分辨率图象技术。文中分析了半像素错位的多幅图象与高分辨率图象各像素灰度值的对应关系，并从CCD数字化采样的角度进行了论证。同时，本文结合实际摄象机CCD结构，求出了高分辨率图象重建的计算公式，并通过实验进行了验证和完善。重建的本质是以原高分辨率图象的4邻域平均图象为基础，增加一定比例的边缘细节信息，去接近原高分辨率图象。CCD的动态范围越大，图像的灰度级越多，那么计算误差就越小，图象的边缘细节信息就可以利用更多，重建的图象就越接近原高分辨率图象。通过实验和分析表明，利用半像素错位的多幅低分辨率图象重建超分辨率图象的原理是正确的，方案是可行的。

本文介绍了一种基于半像素错位的多幅图象重建超分辨率图象技术。文中分析了半像素错位的多幅图象与高分辨率图象各像素灰度值的对应关系，并从CCD数字化采样的角度进行了论证。同时，本文结合实际摄象机CCD结构，求出了高分辨率图象重建的计算公式，并通过实验进行了验证和完善。重建的本质是以原高分辨率图象的4邻域平均图象为基础，增加一定比例的边缘细节信息，去接近原高分辨率图象。CCD的动态范围越大，图像的灰度级越多，那么计算误差就越小，图象的边缘细节信息就可以利用更多，重建的图象就越接近原高分辨率图象。通过实验和分析表明，利用半像素错位的多幅低分辨率图象重建超分辨率图象的原理是正确的，方案是可行的。

The movement of an object can be detected by subtracting the successive two frames of the object's video sequence. On the image after subtraction, there is a larger difference in the object area while a smaller one in the background part. So the track of a moving object can be detected by calculating the position of its gravity center on the image. But if there are several moving objects or the background is moving too, the gravity center of the image is not the real one of any objects. In this paper, we divided the image into 16