Jiang Zhiguo

A wavelet based algorithm for multi-focus micro-image fusion

In this paper, extending the depth of focus from microscopy images is investigated by using multi-focus image fusion techniques. The principle of image fusion based on depth of focus is analyzed, and comparisons of different spatial domain fusion methods are given. Furthermore, a multi-focus micro-image fusion algorithm based on area wavelet transform, which offers improved performance over the existing algorithms, is studied and presented. The performance of the spatial domain and wavelet-based algorithms is compared. Advantages and disadvantages of this algorithm are analyzed by experiments, and the main features of this algorithm are summarized and applicable areas are suggested.

Full-viewpoint 3D Space Object Recognition Based on Kernel Locality Preserving Projections

Abstract Space object recognition plays an important role in spatial exploitation and surveillance, followed by two main problems: lacking of data and drastic changes in viewpoints. In this article, firstly, we build a three-dimensional (3D) satellites dataset named BUAA Satellite Image Dataset (BUAA-SID 1.0) to supply data for 3D space object research. Then, based on the dataset, we propose to recognize full-viewpoint 3D space objects based on kernel locality preserving projections (KLPP). To obtain more accurate and separable description of the objects, firstly, we build feature vectors employing moment invariants, Fourier descriptors, region covariance and histogram of oriented gradients. Then, we map the features into kernel space followed by dimensionality reduction using KLPP to obtain the submanifold of the features. At last, k -nearest neighbor ( k NN) is used to accomplish the classification. Experimental results show that the proposed approach is more appropriate for space object recognition mainly considering changes of viewpoints. Encouraging recognition rate could be obtained based on images in BUAA-SID 1.0, and the highest recognition result could achieve 95.87%.

Automatic Extraction and Matching of Control Points for Distortion Correction in Star-Background Image

Usually a typical geometry distortion will occur when images are captured because of inaccuracy of axis distance of lens in the optical camera lens. The problem is also obvious in star-background images, which necessitate distortion correction for subsequent analysis. In this paper, a new method based on star- point matching is proposed to extract and match automatically control points in star-background images. We acquire automatically control point pairs using point matching between points in pre-corrected image and points in ideal image which relies on the catalog. This work extends applied domain of Hausdorff Distance (HD) which is one of commonly used measures for object matching. In our experiments, least Trimmed Square HD (LTS-HD) was used in point matching, and the result is effective.