# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
###### Abstract
Harris-Laplacian is a combination of the Harris and Laplacian methods which can detect feature points in an image, each with their own characteristic and scale. The authors of [20] compared various popular detectors. Later, [PERSON] et al. [21] modified the comparison procedure and conducted several qualitative tests. These tests showed that the Harris detector outperforms all other algorithms. [PERSON] [22] presented the scale-invariant feature transform (SIFT) algorithm, which is a combination of both detector and descriptor. SIFT is invariant to scaling, orientation and illumination changes. [PERSON] and [PERSON] [23; 24] used SIFT for automatic construction of panoramas. They used the SIFT detector for feature detection and description. Multi-band blending was used to smoothly blend the images. Image stitching techniques based on SIFT are well suited for stitching high-resolution images with a number of transformations (rotation, scale, affine and so on); however, they suffer from image mismatch and are computationally costly. The authors of [25] combined the Harris corner detector with the SIFT descriptor with the aim to address scaling, rotation and variations in lighting condition issues across the images. The authors of [26] tried to accelerate the process and reduced the distortions by modifying the traditional algorithm. The center image was selected as a reference image, then the following image in the sequence was taken based on a set of statistical matching points between adjacent images. The authors of [27] compared different descriptors with SIFT, and the results proved that SIFT is one of the best detectors based on the robustness of its descriptor. [PERSON] et al. [28] presented a mathematical comparison of various popular approaches with the Harris corner detector and KLT tracker. Several researchers examined and refined the Harris corner detector and SIFT [29; 30; 31]. A comparative study of SIFT and Its variants was carried out by [32]. [PERSON] and [PERSON] [33] proposed a novel approach for detecting interest points invariant to scale and affine transformations. It uses the Harris interest point detector to build a multi-scale representation and then identifies points where a local measure (the Laplacian)is maximum across scales. [PERSON] [34] compared the performance of different affine covariant region detectors under varying imaging conditions. The authors of [35] evaluated Harris, Hessian and difference of Gaussian (DoG) filters on images of 3D objects with different viewpoints, lighting variations and changes of scale. [PERSON] et al. [36] proposed the Maximally Stable Extremal Regions (MSER) detector, which detects blobs in images. [PERSON] and [PERSON] [37] provided its faster implementation. [PERSON] et al. [38] proposed SURF, which is both a detector and descriptor. It is based on a Hessian matrix and is similar to SIFT. It reduces execution time at the cost of accuracy as compared to SIFT. The authors of [39] proposed a phase correlation and SURF-based automated image stitching method. First, phase correlation is utilized to determine the overlapping and the connection between two images. Then, in the overlapping areas, SURF is used to extract and match features. The authors of [40] used SURF features for image stitching. They attempted to speed up the image stitching process by removing non-key feature points and then utilized the RELIEF-F algorithm to reduce dimension and simplify the SURF descriptor. [PERSON] et al. [41] proposed an image stitching approach based on SURF line segments, with the goal of achieving robustness to scaling, rotation, change in light and substantial affine distortion across images in a panorama series. For real-time corner detection, the authors of [42] proposed a new detector named the Features from Accelerated Segment Test (FAST) corner detector. The authors of [43] surveyed different local invariant feature detectors. The authors of [44] provided a good comparison of local feature detectors.