>>2007,XU Shenghua, Straight Line Extraction Method for Stereoimage Feature Matching
Abstract
With the rapid development of economy and urbanization, technologies of acquiring digital images with high-resolution and multi-spectra are becoming more and more mature and have gotten increasing applications. 3D geospatial information is very important to various fields, such as city planning, environment monitoring, traffic controlling and updation of GIS data. Especially, the development of digital photogrammetry greatly facilitates 3D geospatial information extraction of the real world objects to a great extent. But the theory and technology of data processing and analysis based on images lag behind correspondingly and feature extraction based on images is the main barrier. How to extract straight line information for 3D reconstruction of man-made objects from images has always been concerned by the researcher from the fields of photogrammetry and computer vision.
Linear feature can provide more information than point feature and can be extracted and described conveniently compared with the planar structure. So Line extraction is the key issue of this dissertation. But due to line fragmentation, occlusion and projection of conjoint coplanar space straight lines, there are many “one-to-multiple” and even “multiple-to-multiple” mappings between two features sets in the process of stereoimage matching, to deal with such kind of problems reliably is the bottleneck of image processing. Therefore, a new straight line extraction method for stereoimage feature matching is proposed. First, the fusion and the filtering strategies are designed for the linear features extracted from the images, then feature grouping is implemented among the linear features, and each feature group contains its associated matching relationships. In this way, stereo matching becomes equivalent to extracting a set of mutually compatible feature groups from the image. The main work of this dissertation can be summarized as follows:
(1) An improved color image edge detecting algorithm and sub-pixel location method. Edge detection is the base of the image measuring, image segmentation, image compressing and pattern recognition and so on. Based on the analysis of the current means of color image edge detection, the main problems and difficulties in edge detecting are discussed. This dissertation discusses a new approach for detecting edges with sub-pixel accuracy in color images using the principal axis analysis, Zernike moments and embedded confidence. First, the principal axis of the color vectors in a three-dimensional space is obtained analytically for the image. Then all the possible edge points are quickly measured by using the edge detection with embedded confidence. At last the edge is relocated with sub-pixel accuracy by means of Zernike moment operator. Experiments show that the accuracy of the proposed method is superior to other edge detectors. The location accuracy of 0.24 pixels is obtained. Results from intensive experimental work also indicate that this method has the desired performance of anti-interference and is highly efficient.
(2) Line extraction method based on Hough Transform. Aiming at the critical time-consuming and accuracy issues of straight line extraction from large-scale remote sensing imagery, after briefly reviewing the existing straight line extraction methods, a multi-scale Hough transform method based on the pre-storage weight matrix is proposed, which saves a lot of storage space, takes care of discretization errors, and avoids the abruption and conglutination of characters that are the drawbacks of the existing straight line extraction algorithms. This dissertation introduces the principle and the implementation algorithm of multi-scale Hough transform method based on the pre-storage weight matrix in detail. To optimize speed and precision, the best choice of accumulator based on image size is suggested too. The experimental results show that this algorithm is more efficient in computation and robust to noise, and is rich in feature content and accurate, especially for large-size images.
(3) A new shadow extraction method from color aerial image based on Dempster-Shafer (D-S) evidence theory. Shadows play an important role in our understanding of imagery especially the remote sensing imagery. According to the color features of shadow in the color aerial images, a new approach is proposed to extract shadow from color aerial images based on the D-S evidence theory. Firstly three different feature descriptions of shadow in RGB and HIS spaces are introduced, then the image is segmented based on Mean Shift method, the color features of the segmented regions are computed and the basic probability assignment function (BPAF) of each segment is defined respectively. By using of the complementary characteristics of such three color features, the D-S rule is applied to compute the fusion of BPAF. Finally, the BPAF fusion is utilized to find the conclusive shadow segments.
(4) The fusion and the filtering strategies based on perceptual grouping. The Gestalt theory in cognitive psychology and the perceptual grouping theory are briefly introduced. A method of extracting regions of interest (ROI) is proposed using the K-Means classification, shadow information and Mean Shift segmentation. The information of ROI is used to filter the edges and the lines extracted by phase-grouping method. Then the lines extracted by two different algorithms are fused and filtered utilizing basic rules of perceptual grouping. The semantic interrelationships between linear features are exploited by perceptual grouping to extract structure to detect the presence of manmade objects. These features are generated by the strong boundaries typical of manmade structures: longer linear lines, “L” junctions, “U” junctions, parallel lines, and polygons.
The software prototype of straight line extraction for stereoimage feature matching proposed in this dissertation is implemented using VC++6.0. From the experimental results, this method is preferable to extract linear features from aerial stereo pairs, which will lay the solid foundation for the future research on the stereoimage matching method based on line feature.
In conclusion, the straight line extraction will be one of the difficult questions in the field of photogrammetry and computer vision in a very long period. It is very important for the practical use of digital photogrammetry system to develop more reliable line extraction techniques.
Key Words
Stereoimage feature matching, Edge detection with sub-pixel precision, Line
extraction, Shadow extraction, Perceptual grouping
PhD. Dissertation