A sampling optimization method based on color difference analysis was proposed in this paper. Firstly, three color sets--respectively a super set used to simulate the whole CMYK color space, a test set for characterization accuracy verification and an initial characterization set were defined and created. Secondly, the colorimetric values of test set can be predicted according to the characterization results of the current characterization set. Thirdly, by analyzing the color difference of test set, 10% samples with larger color difference were selected as the larger color difference set to carry on optimization. After that, the samples in the super set which are closest to the larger color difference set were found and added to the characterization set. Finally, cyclic optimization was conducted until the characterization accuracy meets the given requirements. Experimental results showed a significant reduction in the number of samples with an improvement of characterization accuracy.
The commercialization of high resolution remote sensing image provides the image application more widely space. How
to extract the interested important objects quickly and exactly from remote sensing image is always the research focus.
After analyzed the characteristics of road in high resolution image, the paper constructed the road extraction model based
on MRF and Bayesian. And finally the validity of the method was confirmed by an example.
In the workflow of image reproduction, the halftone image is usually used as scanned original image. The moiré patterns
are a result of intervention during the process of scanning. This paper provides a descreening method of scanned halftone
image based on wavelet according to the cause and the character of moiré patterns. Experiment result shows that this
descreening method can remove the moiré patterns effectively and at the same time hold the definition and entropy of the
scanned halftone image.
Projection Pursuit is applied to explore the potential structures and characters of the multi-dimension data through projecting the high dimensional data set into a low dimensional data space while retaining the information of interest. For the application of hyperspectral images analysis, the detection of small man-made objects is very difficult. But the man-made object can be viewed as anomalies in an unknown environment due to the fact that their spectral is different from those of the large known background. This paper presents a method to detect man-made objects of hyperspectral images based on Projection Pursuit. Also evolutional algorithm was developed in order to find the optimal projection index.