Quantitative Evaluation of Image Mosaicing in Multiple Scene Categories

Image mosaicing has been practiced in several computer vision and scientific research areas. There is a clear indication of the advancement of the state of the art of mosaicing algorithms. However, the methods of quantitative evaluation of mosaicing algorithms are still inadequate. Furthermore, a majority of the previous evaluation methodologies lack a sufficient number of performance metrics, while others suffer from computational complication. Therefore, this paper proposes an evaluation method to assess the performance of mosaicing algorithms. This method involves four metrics: percentage of mismatches, difference of pixel intensities, peak signal-to-noise ratio, and mutual information to measure the quality of the mosaicing outputs. These outputs are obtained using a mosaicing algorithm based on the Scale Invariant Feature Transform, Best Bins First, and Random Sample Consensus, reprojection and stitching algorithms. In order to evaluate mosaicing performance objectively, the proposed method compares mosaicing images with the ground-truth images that depict the same scene view. Evaluation has been performed using 36 test sequences from 3 different categories: images of 2D surfaces, images of outdoor 3D scenes, and airborne images from an Unmanned Aerial Vehicle. Exhaustive testing has shown that the proposed metrics are effective in assessing the quality of mosaicing outputs.