Plane measurement is an important task in image processing and computer vision. In this paper, we present a kind of plane measurement method for monocular vision, which calculates the image magnification by controlling the camera movement, the height of object and camera focal length. We can finally get the measurement results. Experimental results show that the proposed method can measure the distance more accurately. What’s more, it has the advantages of high universality, simple steps and small error.
In this paper we analyse the polarization imaging theory and the commonly process of the polarization imaging detection. Based on this, we summarize our many years’ research work especially in the mechanism, technology and system of the polarization imaging detection technology. Combined with the up-to-date development at home and abroad, this paper discusses many theory and technological problems of polarization imaging detection in detail from the view of the object polarization characteristics, key problem and key technology of polarization imaging detection, polarization imaging detection system and application, etc. The theory and technological problems include object all direction polarization characteristic retrieving, the optical electronic machinery integration designing of the polarization imaging detection system, the high precision polarization information analysis and the polarization image fast processing. Moreover, we point out the possible application direction of the polarization imaging detection technology both in martial and civilian fields. We also summarize the possible future development trend of the polarization imaging detection technology in the field of high spectrum polarization imaging. This paper can provide evident reference and guidance to promote the research and development of the polarization imaging detection technology.
In this paper we propose a polarization image fast fusion approach based on online dictionary learning for sparse non-negative matrix factorization, aiming at improving the efficiency of fusion methods for polarization image based on non-negative matrix factorization. Firstly, all of the polarization parameter images are taken as source data sets for sparse non-negative matrix factorization using online dictionary learning algorithm, so as to extract three feature basis images. Then, after histogram matching, the three feature basis images are mapped into three color channels of IHS color space. Finally, the fused image is achieved via the transform from IHS to RGB color model. Experimental results show that, the proposed method not only has better capacity of color representation capability and effectively pop out detailed information of objects but enhances the running efficiency evidently as well.