The process of Reference image preparation, is to extract region of interest area from a image,to get a simplified image which be used as template image for other algorithms.Because of the complex of scene,one usually need to excute a set of different algorithms to get a final image which only has the information of interest area. This paper presents a new variational model which used L0 norm for idelity item, to keep information of interested area better, while removing other redundant information. Experiments show that,this method can remove information of grediant in some special range,to handle a more general case,compared with the original L0 gradient method which can only remove low frequency information. Compared with the same variational model but using L1orL2 norm，the proposed method can well retain the original information.Those advantages is very important for making the process of reference image preparation faster and easier
Proc. SPIE. 9812, MIPPR 2015: Automatic Target Recognition and Navigation
KEYWORDS: Image processing algorithms and systems, Data mining, Detection and tracking algorithms, Image segmentation, Image processing, Image resolution, Data processing, Information technology, Image classification, Algorithms
The image classification is an important means of image segmentation and data mining, how to achieve rapid automated image classification has been the focus of research. In this paper, based on the super pixel density of cluster centers algorithm for automatic image classification and identify outlier. The use of the image pixel location coordinates and gray value computing density and distance, to achieve automatic image classification and outlier extraction. Due to the increased pixel dramatically increase the computational complexity, consider the method of ultra-pixel image preprocessing, divided into a small number of super-pixel sub-blocks after the density and distance calculations, while the design of a normalized density and distance discrimination law, to achieve automatic classification and clustering center selection, whereby the image automatically classify and identify outlier. After a lot of experiments, our method does not require human intervention, can automatically categorize images computing speed than the density clustering algorithm, the image can be effectively automated classification and outlier extraction.
Visible image, compared with SAR image and infrared image, has the advantage of high resolution, clear details, etc. So it can be selected for object extraction. Water objects play an important role in locating bridges, dams and other typical buildings. This paper presents a segmentation method for visible image based on gradient of the original image, and combined with the features of the water targets. According to the feature of water targets, gray uniform, smaller entropy, and smaller local variance, water objects can be extracted automatically and effectively by using clustering method from image segmentation result.