Frame Stitching in Human Oral Cavity Environment Using Intraoral Camera

Intra-oral cameras are essentially enabling dentists to capture images of difficult-to-reach areas in the mouth. Oral dental applications based on visual data pose various challenges such as low lighting conditions and saliva. We introduce an approach to stitch images of human teeth that are captured by an intra-oral camera. In such monocular image matching, a low rate of features on teeth surfaces causes a problem leading to a mismatch between teeth images. In this paper, we propose an approach to improve the matching in these low-texture regions. First, normals of tooth surface is extracted using a shape from shading. Due to the oral environment, the surface normals impact many of imprecise values; hence we formulate an algorithm to rectify these values and generate normal maps. The normal maps reveals the impacted geometric properties of the images inside an area, boundary, and shape. Second, the normal maps are used to detect, extract and match the corresponding features. Finally, to enhance the stitching process for these unidealized data, normal maps are used to estimate as-projective-as-possible warps. The proposed approach outperforms the state-of-the-art auto-stitching approach and shows a better performance in such cases of low-texture regions.