Image capturing and stitching system based on scale-invariant feature transform features for untextured and smooth objects

Abstract. Many stitching studies employ improved scale-invariant feature transform (SIFT) or speeded up robust features (SURFs) stitching algorithms, owing to their excellent robustness against variations in scale and rotations of input images. Traditional SIFT-based stitching algorithms suffer from the limitation of having insufficient feature points that can be extracted and matched from untextured and smooth objects, resulting in stitching failure. To overcome this issue, we propose a system that uses a proposed laser pattern projection (LASPP)-pasted texture technique before feature point extraction and description. The proposed system can extract and describe feature points based on SIFT feature extraction techniques. The progressive sample consistency (PROSAC) algorithm was used to remove false matching points and enhance the accuracy in matching feature points. The experimental results clearly revealed the effect of the pattern projected using LASPP on the stitching results. Furthermore, the efficiency of the proposed system, which employed the PROSAC algorithm, in matching feature points, was compared with that of the traditional system, which used the random sample consensus algorithm. Additionally, the stitching quality of the proposed system was compared with that of other SIFT- and SURF-based stitching systems.