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

在JPEG图象压缩的量化过程中，丢弃了部分高频信息，使图象的清晰度下降。通过频谱对照分析和实际图象实验表明，JPEG图象压缩对测量和处理结果的影响与原图象特点、取样方式、滤波过程和压缩算法有关。经摄象机和图象卡获得的图象，压缩比可用到10；经扫描仪获得的图象，压缩比可用到6；数码相机图象可用到6～8；特定医学图象、晶相图象等，利用图象的特征构造最优匹配的JPEG压缩量化表，压缩比可用到10。采用合适的压缩比和改进的压缩方法，图象的细节损失可以控制在较小的范围内，基本不影响检测类图象的边界提取、分割、尺寸测量等处理结果。

在JPEG图象压缩的量化过程中，丢弃了部分高频信息，使图象的清晰度下降。通过频谱对照分析和实际图象实验表明，JPEG图象压缩对测量和处理结果的影响与原图象特点、取样方式、滤波过程和压缩算法有关。经摄象机和图象卡获得的图象，压缩比可用到10；经扫描仪获得的图象，压缩比可用到6；数码相机图象可用到6～8；特定医学图象、晶相图象等，利用图象的特征构造最优匹配的JPEG压缩量化表，压缩比可用到10。采用合适的压缩比和改进的压缩方法，图象的细节损失可以控制在较小的范围内，基本不影响检测类图象的边界提取、分割、尺寸测量等处理结果。

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.