Camouflage target detection is a difficult problem in the field of target detection. The practice of camouflage target detection based on depth learning shows that the scale proportion of the targets in the image is one of the important factors that affect the detection performance. Based on the region segmentation and texture analysis, two schemes of camouflage target filtering are proposed in this paper. Experiments show that the proposed schemes improve the integrity of the target filtering significantly, it lays the technical foundation for improving the accuracy of deep recognition in the later stage.
Although change detection of image objects has good future in military and civil area, effective application in fact
is still a difficult problem. This paper denotes the course of change detection by a united structure in different areas
firstly. At the same time, the author analyzes similarities and differences as well as bottleneck of several key steps under
united structure detailedly on the basis of different backgrounds and applications, and puts forward resolved method and
improved algorithms, which enhances understanding and application for kinds of change detection algorithms. Finally,
the author prospects several advice of future research and application on change detection of image objects.
With extensive application of image change detection, the author researches kinds of algorithms of image change detection in detail. Based on analysis of several typical change detection algorithms, an improved algorithm of change detection based on independence component analysis (ICA) is proposed; simultaneously, an integrated change detection scheme is presented. Taking remote sensing image with complex background for example, simulation results show that the improved algorithm and proposed scheme have advantage over current algorithm.
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