# Improved Combined Metric for Automatic Quality Assessment of Stitched Images
## 1 Introduction  
Image stitching is typically defined as a method for creating panoramic images from a set of constituent images or, more generally, a combination of multiple images into a larger image with a wider field of view. It may be applied for various purposes including popular smartphone applications, where such panoramas may be constructed directly during the acquisition of an image series by the smartphone camera. Nevertheless, the application areas of stitched images are much wider, including in mobile robotics and robot vision [1, 2], non-destructive testing and surface inspections [3, 4], civil engineering construction [5], as well as medical X-ray imaging [6]. The main idea of image stitching is based on the application of feature extraction methods, making it possible to identify some pairs of small image fragments (features) present in two constituent images that represent the same physical objects or their fragments, e.g., corners. For this purpose, several methods may be applied from the classical Harris corner detector and Difference of Gaussians (DoG) to the use of feature detectors and descriptors, such as the Scale Invariant Feature Transform (SIFT) [7], Speeded-Up Robust Features (SURF) [8], Features from Accelerated Segment Test (FAST) [9], as well as some more advanced ones such as BRIEF (Binary Robust Independent Elementary Features) [10], ORB (Oriented FAST and Rotated BRIEF) [11], BRISK (Binary Robust Invariant Scalable Keypoints) [12], or FREAK (Fast Retina Keypoint) [13]. Having determined the interest points (keypoints), the next step is to find their matches and use affine transformations. Typically, the homography estimation is made using the Random Sampling Consensus (RANSAC) algorithm [14], which is used for the outliers' removal, and then each image is projected onto the same surface and blended. Image blending can also be conducted using several techniques, including alpha blending and gain compensation, influencing the final quality of the stitched image. Hence, by using more images partially overlapping each other, it is possible to construct a single high-resolution image representing a larger scene. Such methodology can be useful in many applications related to mobile robotics from target recognition [1,15] to robot-based inspection systems, where the 3D point clouds can also be stitched with additional texture projection helping to calculate the sensor's movement [3]. Another interesting application is related to crack detection during concrete bridge inspections by combining point and line features. During a typical inspection process, thousands of individual images are collected so the detection of cracks on them would take a lot of time. More reliable and comprehensive results may be obtained for images representing the surface as a whole. Such a system constructed for the inspection of the bottom surfaces of bridges has been presented by [PERSON] et al. [5]. Similar methods may be useful in industrial applications for video inspection of many types of large, particularly long, elements, as well as in electrical engineering for the inspection of power lines using drones. Recently, [PERSON] et al. [4] utilized image-stitching methods for the automated surface inspection of rotationally symmetric parts, e.g., ball screw drives or cylinder walls of a combustion engine, particularly for images obtained using line-scan cameras. An interesting method for stitching video sequences from a fisheye camera and a wide-angle lens camera was presented by [PERSON] et al. [16]. The typical assumed scenario was a telepresence robot, where the quality of the stitched image frames obtained after the final blending played an important role, similar to mobile robotics. In this case, the detected low-quality images might have been removed from the analysis, influencing the robot's motion control to prevent its improper steering.