Image matching based on the adaptive redundant keypoint elimination method in the SIFT algorithm

Multi-Angle Optical Image Automatic Registration by Combining Point and Line Features

Image registration is an important basis of image processing, which is of great significance in image mosaicking, target recognition, and change detection. Aiming at the automatic registration problem of multi-angle optical images for ground scenes, a registration method combining point features and line features to register images is proposed. Firstly, the LSD (Line Segment Detector) algorithm is used to extract line features of images. The obtained line segments whose length are less than a given threshold are eliminated by a visual significant algorithm. Then, an affine transform model obtained by estimating a Gaussian mixture model (GMM) is applied to the image to be matched. Lastly, Harris point features are utilized in fine matching to overcome shortages of methods based on line features. In experiments, the proposed algorithm is compared with popular feature-based registration algorithms. The results indicate that the proposed algorithm in this work has obvious advantages in terms of registration accuracy and reliability for optical images acquired at different angles.

Rapid Reconstruction of 3D Structural Model Based on Interactive Graph Cuts

The image-based 3D reconstruction technique has been applied in many scenarios of civil engineering, such as earthquake prevention and disaster reduction, construction monitoring, and intelligent city construction. However, the traditional technique is time-consuming, and the modeling efficiency has become a bottleneck limiting its application in emergency scenarios. In this paper, a rapid reconstruction method is proposed which combines the traditional image-based 3D reconstruction technique and an interactive graph cuts algorithm. Firstly, a sequence of images is collected around the target structure. Then, the images are preprocessed using the interactive iterative graph cuts algorithm to extract the target from each image. Finally, the resulting sequence of images is used to perform the 3D reconstruction. During the preprocessing, only a few images require manual intervention while the rest can be processed automatically. To verify the modeling accuracy of the proposed method, a column that has been destroyed is selected as a target for 3D reconstruction. The results show that compared with the traditional method, the modeling efficiency of the fast reconstruction method is doubled. In addition, the modeling accuracy is 97.65%, which is comparable to the modeling accuracy of the traditional method (97.73%); as well, by comparing the point clouds, the alignment between the two models is tremendously close, with tiny difference. The proposed rapid reconstruction method can be applied in emergency scenarios, such as rapid assessment in post-disaster situations.

Using Information Content to Select Keypoints for UAV Image Matching

Image matching is one of the most important tasks in Unmanned Arial Vehicles (UAV) photogrammetry applications. The number and distribution of extracted keypoints play an essential role in the reliability and accuracy of image matching and orientation results. Conventional detectors generally produce too many redundant keypoints. In this paper, we study the effect of applying various information content criteria to keypoint selection tasks. For this reason, the quality measures of entropy, spatial saliency and texture coefficient are used to select keypoints extracted using SIFT, SURF, MSER and BRISK operators. Experiments are conducted using several synthetic and real UAV image pairs. Results show that the keypoint selection methods perform differently based on the applied detector and scene type, but in most cases, the precision of the matching results is improved by an average of 15%. In general, it can be said that applying proper keypoint selection techniques can improve the accuracy and efficiency of UAV image matching and orientation results. In addition to the evaluation, a new hybrid keypoint selection is proposed that combines all of the information content criteria discussed in this paper. This new screening method was also compared with those of SIFT, which showed 22% to 40% improvement for the bundle adjustment of UAV images.