Computer Vision Theory And Applications Of Three-dimensional Reconstruction

Computer stereo vision mainly studies the issues of reconstruction of the 3D world from the 2D images, and is one of the main tasks of computer vision. Computer stereo vision theories and techniques can be used in many important areas such as 3D battlefields modeling, battlefields situation monitoring, positioning and navigation. To meet the requirement of the military development for the 3D terrain reconstruction and positioning rapidly and precisely, this dissertation mainly studies the issues of image features detection and knowledge based image features spatial relation analysis, wide baseline stereo matching, real-time 3D reconstruction and error analysis about the stereo vision models, and proposes some novel and effective methods to cope with those problems.Finding out basic features in an image is always the necessary step in many algorithms in computer stereo vision. After studying the property of the straight line, a novel hierarchical algorithm about straight lines extraction is proposed in the dissertation. This straight lines extraction method extracts local line segments from local image intensity features at different spatial scale. First, the algorithm forms many short lines in the lowest level. Then it merges them into upper level according to their adjacency and straight line constraints. Finally a hierarchical straight lines structure is formed and final straight lines were extracted.In the area of region features detection, another contribution in this work is that it proposes a robust segmentation algorithm. This algorithm uses different methods dealing with different pixels in the image according to its position, namely using simple average method dealing with the pixels which are in the position of distance to edges of regions, and using the mean shift algorithm dealing with the other pixels, which are possibly near the edges of regions, both in color space and scale space. It is obvious that the proposed algorithm is effective and robust.The key problem to 3D reconstruction is image correspondence. A knowledge based image analysis system framework is proposed in the work aiming to deal with the imagematching problems in 3D reconstruction. In this framework, knowledge can be classified as knowledge about image processing itself, knowledge about specific target images and knowledge about image processing tasks. Knowledge based image analysis system employs fundamental knowledge about image analysis and experts' knowledge in specialist area to find out spatial perspective relations among interested features in the scene or other relations such as perspective vanished point position and 3D parallel lines.In this work, a novel algorithm based on local affine invariants is proposed for searching correspondences between images in the most general case, namely, under the wide baseline conditions and without any priori information about the internal or external camera parameters. This algorithm uses image point features as anchor points for image matching, and the local space-variants regions around the anchor point are used as local window region to generate geometric affine invariants and color affine invariants for image matching. The proposed stereo matching method is efficient in algorithmic time because only the local image features are used in the image matching process. Meanwhile, the image matching results are reliable and robust because its feature matching region is relatively large, and the shape of local window region is transformed according to possible projective deformation. In addition, overlapped sub-regions are used in local feature extraction to reduce partially occluding problems.On 3D reconstruction, this dissertation discusses the basic methods of 3D projective reconstruction and 3D Euclidian reconstruction, a bundle adjustment algorithm for 3D reconstruction, a real-time Kalman filtering 3D Euclidian reconstruction method, and plane based single view and multi-view constraints algorithm for 3D Euclidian reconstruction.In this work, stereo camera model error is al...