Dental computed tomography (CT) images and optical surface scan data are widely used in dental computer-aided design systems. Registration is essential if they are used in software systems. Existing automatic registration methods are either time-consuming or rough, and interactive registration methods are experience-dependent and tedious because of a great deal of purely manual interactions. For overcoming these disadvantages, a two-stage registration method is proposed. In the rough registration stage, the rough translation and rotation matrices are obtained by applying unit quaternion based method on the points interactively selected from the two types of data. In the precise registration stage, the stridden sampling is used to reduce computational complexity and the proposed registration algorithm with scale transformation is used for precise registration. The proposed method offers a good trade-off between precision and time cost. The experimental results demonstrate that the proposed method provides faster convergence and smaller registration errors than existing methods.

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Recently, unmanned aerial vehicles (UAVs) have been widely used in industries such as transportation and agriculture for capturing scenes. Aerial images taken by UAVs are one of the most widely used scenario for object detection applications. However, since UAV images are captured from high altitude, the target scale of UAV images is inconsistent compared with ordinary images as the background is more complex and the target size is smaller. To address this problem, we proposed a new network model IEC-YOLOX, which is based on YOLOX. The model adds a detection layer to YOLOX-tiny to enable the detection of small targets and utilizes iAFF to fuse features of different scales. The backbone network CSPDarknet is replaced with Extended-ELAN, which reduces the number of parameters by one-fifth. Additionally, a lightweight attention mechanism called CBAM is added to the new backbone network.This paper uses VisDrone 2019 as the experimental data. The result of our YOLOX-tiny network structure's mAP@50 is 35.3$\%$, and the number of parameters is 4.29M, which is about 4.3$\%$ higher than the baseline model (YOLOX-tiny) in terms of mAP@50, and the number of parameters has been reduced by 0.74M.

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