Evaluation of Local Features for NearUniform Scene Images Image stitching requires accurate matching of visual features to achieve good alignment. However, featurebased matching often has poor result particularly when image content is fairly nearuniform and thus it remains a challenging problem to be addressed. When the current stateoftheart feature detectors unable to detect sufficient reliable corresponding keypoints, the output stitched images often suffer from misalignment, projective distortion and visible artefact. This paper presents a new experimental evaluation using especially nearuniform images for the performance of some wellknown feature detectors, such as Harris, SIFT, SURF, BRISK and KAZE. In addition, we have also introduced RCS o  score to compare spatial distribution of the correct matched keypoints in overlapping region between images. The results show that the best performed local feature detector is KAZE. However, none of the tested feature detectors can reach more than 50% spread of the overlapping region.

Modified Baseline for Light Field Stitching In traditional 2D image stitching, the baseline method usually means global homography via Direct Linear Transformation (DLT) on inliers. In this paper, a modified baseline method for light field (LF) stitching is proposed to stitch two LFs. The depth map and the center subaperture image (SAI) are used to filter the feature points of the entire LF. The global 4D homography is then calculated by DLT to align all SAIs corresponding to the same angular domain coordinates of two LFs. Finally, the improved Markov Random Field (MRF) energy considering the global LF is used to find the seam of 2D SAIs instead of computational 4D graph cut. Experimental results show that the proposed method can effectively stitch the 4D LFs, and preserve the consistency of the angular and spatial domains of the stitched LF compared with implementing 2D image stitching to the corresponding SAIs. Moreover, the method proposed in this paper can easily extend all advanced 2D image stitching methods to 4D LF, so that the acquired LF can have larger field of view and wider applications.