Fast Target Recognition Based on Improved ORB Feature

Feature extraction algorithm of an irregular small celestial body in a weak light environment

This study focuses on creating a crater-matching algorithm to improve the matching rate and address the phenomenon of insufficient feature extraction and mismatching of irregular celestial objects and crater edge information on the dim surface of celestial bodies images. These images were captured by the detector's navigation camera. In order to improve the brightness and clarity of the images, the target images were filtered, denoised, and image-enhanced using the bilateral filtering method and improved histogram equalization algorithm, successively. Then, the enhanced image was extracted and matched using the ORB feature point detection algorithm based on scale invariance, and the feature point mismatch was processed by the Hamming distance screening method. The simulation results revealed that the optimization algorithm effectively improved the imaging quality of the target image in dark and weak light environments, increased the number of feature points extracted, reduced the mismatch of effective feature point pairs, and improved the matching rate.

High precision visual localization method of UAV based on feature matching

In this paper, the precision hovering problem of UAV operation is studied. Aiming at the diversity and complexity of the UAV operating environment, a high-precision visual positioning and orientation method based on image feature matching was proposed. The image feature matching based on the improved AKAZE algorithm is realized, and the optimal matching point pair screening method based on the fusion of Hamming distance and matching line angle is innovatively proposed, which greatly improves the robustness of the algorithm without affecting the performance of the algorithm. The real-time image is matched with the benchmark image for image feature matching. By reducing the deviation of image feature, the pose state correction of UAV hovering is achieved, and the precision hovering of the UAV is realized. Both simulation and real UAV tests verify the effectiveness of the proposed UAV high-precision visual positioning and orientation method.

Home Environment Augmented Reality System Based on 3D Reconstruction of a Single Furniture Picture

With the popularization of the concept of "metaverse", Augmented Reality (AR) technology is slowly being applied to people's daily life as its underlying technology support. In recent years, rapid 3D reconstruction of interior furniture to meet AR shopping needs has become a new method. In this paper, a virtual home environment system is designed and the related core technologies in the system are studied. Background removal and instance segmentation are performed for furniture images containing complex backgrounds, and a Bayesian Classifier and GrabCut (BCGC) algorithm is proposed to improve on the traditional foreground background separation technique. The reconstruction part takes the classical occupancy network reconstruction algorithm as the network basis and proposes a precise occupancy network (PONet) algorithm, which can reconstruct the structural details of furniture images, and the model accuracy is improved. Because the traditional 3D registration model is prone to the problems of model position shift and inaccurate matching with the scene, the AKAZE-based tracking registration algorithm is improved, and a Multiple Filtering-AKAZE (MF-AKAZE) based on AKAZE is proposed to remove the matching points. The matching accuracy is increased by improving the RANSAC filtering mis-matching algorithm based on further screening of the matching results. Finally, the system is verified to realize the function of the AR visualization furniture model, which can better complete the reconstruction as well as registration effect.