# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
###### Abstract  
Image stitching is a technique that is often employed in image processing and computer vision applications. The feature points in an image provide a significant amount of key information. Image stitching requires accurate extraction of these features since it may decrease misalignment flaws in the final stitched image. In recent years, a variety of feature detectors and descriptors that may be utilized for image stitching have been presented. However, the computational cost and correctness of feature matching restrict the utilization of these techniques. To date, no work compared feature detectors and descriptors for image stitching applications, i.e., no one has considered the effect of detectors and descriptors on the generated final stitched image. This paper presents a detailed comparative analysis of commonly used feature detectors and descriptors proposed previously. This study gives various contributions to the development of a general comparison of feature detectors and descriptors for image stitching applications. These detectors and descriptors are compared in terms of number of matched points, time taken and quality of stitched image. After analyzing the obtained results, it was observed that the combination of AKAZE with AKAZE can be preferable almost in all possible situations. image stitching; feature detector; feature descriptor; image mosaic; image alignment; image quality assessment +
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feature points. A feature descriptor is a vector of values that represents the image patch surrounding a feature point. It might be as basic as the raw pixel values or as complex as a histogram of gradient orientations. Various feature detectors and descriptors have evolved over time. However, due to their poor speed and high number of false matches, the majority of them are not useful for image stitching applications. Feature detectors and descriptors are crucial in image alignment; if a good pairing of feature detector and descriptor is not chosen, then the images to be stitched will not be aligned properly, which will add distortions in the resulting stitched image. Thus, a comparative study is required to determine the best combination of feature detector and descriptor that can provide high-quality stitched images with lower computational cost. In the field of computer vision and image processing, the research on feature detectors and descriptors has grown rapidly in last couple of decades. A brief literature review is presented in the following to show the continuous development in feature detection and description. The first corner detector was proposed by [PERSON] [7]. The authors of [8] proposed a fast operator for the detection and precise location of distinct points, corners and centers of circular image features. [PERSON] and [PERSON] [9] proposed a new corner detector named the Harris corner detector, which overcomes the limitations of the Moravec detector. [PERSON] and [PERSON] [10] developed Kanade-Lucas-Tomasi (KLT) tracker based on the Harris detector for the detection and tracking of features across images. A comparison of the Harris corner detector with other detectors was carried out by [11; 12]. [PERSON] and [PERSON] [13] presented an improved version of the Harris corner detector named Good Features to Track (GFTT). [PERSON] [14] presented a junction detector with auto-scale selection. It is a two-stage process that begins with detection at coarse scales and progresses to localization at finer scales. [PERSON] and [PERSON] [15] presented a fast corner detection approach based on cornerness measurement of total curvature. The authors of [16] proposed a new approach for feature detection, named SUSAN; however, it is not rotation- or scale-invariant. [PERSON] et al. [17] presented a definition of saliency for scale change and evaluated the Harris corner detector, the method presented in [18] and the Harris-Laplacian corner detector [19].