dc.description.abstract | To construct 3-D objects from stereo images has been studied for decades. It shows some success for construction of simple objects with smooth surfaces, but in complex and unique object, such as a large terrain, house, or more complicate human face, that still remain an issue for 3-D reconstruction. In this study, we acquire the terrain aerial photos and facial images for our research, to construct 3-D model with automatic feature extraction from stereo photos without any models. To achieve robustness and efficient matching of features, we adopt Scale-Invariant Feature Transform (SIFT) algorithm to detect the feature with the Gaussian pyramid and find the feature descriptors which make the features more distinctive, hence, the feature matching can be efficiently accomplished based on Euclidean distance. Moreover, those matching pairs must satisfy the Stereoscopic vision between two images, therefore, the angle and distance constraints for features are added before feature matching to improve the accuracy of matching pairs and stereo vision. Then, the 3-D model can be reconstructed by solving the binocular stereo vision theorem from feature pairs in stereo photos, the error estimated of depth can be smooth by the median filter. Finally, we compare two feature extraction methods to the Digital Terrain Model. Compare to manual extraction, the automatic feature extracted method is much better, more efficient and provides more matched features, and therefore, it shows better results for the studies of the topographic images in 3-D construction. In accuracy assessment, we compare to the results of manual extraction to verify the reliability of the automatic method. Our experimental results in the facial images show that taking stereo pictures vertically is much better than horizontally, because vertical direction can avoid errors of feature matching in the edges of cheeks. | en_US |