# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
###### Abstract
Some application-specific evaluations were presented by [45; 46; 47; 48]. [PERSON] et al. [49] introduced CenSurE, a novel feature detector that outperforms current state-of-the-art feature detectors in image registration and visual odometry. [PERSON] and [PERSON] [50] proposed a fast corner detection algorithm for use on structural images, i.e., images with man-made structures. [PERSON] et al. [51] proposed the Binary Robust Independent Elementary Features (BRIEF) descriptor, which is very helpful for extracting descriptors from interest points for image matching. The authors of [52] presented a new local image descriptor, DAISY, for wide-baseline stereo applications. [PERSON] et al. [53] proposed a combination of both detector and descriptor named Binary Robust Invariant Scalable Key-Points (BRISK), which is based on the FAST detector and in combination with the assembly of a bit-string descriptor. The use of the FAST detector reduced its computational time. [PERSON] et al. [54] presented the Oriented FAST and Rotated BRIEF (ORB) detector and descriptor as a very fast alternative to SIFT and SURF. The authors of [55] presented the Fast Retina Key-Point (FREAK) descriptor based on the retina of the human visual system (HVS). [PERSON] [56] developed a new feature detector and descriptor named KAZE in which they formed nonlinear scale spaces to detect features in order to improve localization accuracy. The faster version of KAZE was presented by the authors in the year 2013 and named Accelerated-KAZE (AKAKZE) [57]. The authors of [58] proposed the AKAKZE-based image stitching algorithm. The authors of [59; 60] presented a detailed evaluation of image stitching techniques. [PERSON] et al. [61] surveyed various image/video stitching algorithms. They first discussed the fundamental principles and weaknesses of image/video stitching algorithms and then proposed the potential solutions to overcome those weaknesses. [PERSON] et al. [62] analyzed different image stitching techniques. They reviewed various image stitching algorithms based on the three components of the image stitching process: calibration, registration and blending. Further, they discussed challenges, open issues and future directions of image stitching algorithms. Various prominent feature detectors and descriptors were analyzed by [63; 64] and [65]. However, these studies included a few detectors and descriptors, and they have not considered all combinations of detectors and descriptors. The authors of [66] introduced a new simple descriptor and compared it to BRIEF and SURF. The authors of [67] compared several feature detectors and descriptors and analyzed their performance against various transformations in general and then presented a practical analysis of detectors and descriptors for feature tracking. Several outstanding feature detectors and descriptors have been reported in the existing literature. Some studies compared detectors and descriptors in general, and some are application-specific, and a few have compared only detectors, while others have compared only descriptors. The number of detectors and descriptors used in all of these studies is limited, and they have not considered all pairs of detectors and descriptors. However, as the introduction of new detectors and descriptors is an evolving process, improved comparative studies are always needed. In this study, we aimed to update the comparative studies in connection with existing literature. The following statements show a connection of existing studies with the proposed work and the attributes that differentiate our study from previous studies:  
1. Several detectors and descriptors were included in this study, apart from those already compared by [63; 64; 67; 68; 69]. 2. A variety of prominent measures were employed in our research in accordance with [63]. We also used image quality assessment measures to compare the quality of the stitched image to the input image. 3.