Research On Feature Extraction And Matching Based On Organized Objects

The dominant purpose of photogrammety is to use CCD images to obtain the accurate and reliable measurements while computer vision is to make computer have visible ability which resembles that of human's eyes and to regain the geometries of three-dimensional space.Feature extracting and matching,which is an essential component of such research as three-dimensional reconstruction, motion tracking, image sequence analysis, information fusion, pattern recognition, are widely used in both computer vision and photogrammetry. There exits important practical significance in such studying.As the improving of industrial manufacturing technology and processing technic,we usually get a poor efficiency and short precision by human check in large quantities production although the production efficiency and automation can be greatly enhanced by computer vision and a high accuracy by the extensive adjustment theory. It makes a great progress in CCD devices with a high resolution and high signal to noise ratio and in image processing and pattern recognition, which enables photogrammetry-comuter vision to be a wide application way for technology of precise measurements.In order to accommodate the demands of industrial precision measurement, This paper which combines with a wealth of line features in organized objects and adopts rigorous data processing theory of photogrammetry espically the advanced technology in computer vision,focuses on the feature extraction and matching. We mainly undertook the following findings that demonstrated by our experiments with a good results: proposing a new method for points extraction by line discretization and a high precision line extraction algorithm; improving the image matching based on correlation coefficient called weighted correlation coefficient; bringing epipolar constraint into the least squares matching and creating a new way for line matching especially blocked.