A Review Over Panoramic Image Stitching Techniques

Abstract

Using digital images at present are increased rapidly in many fields such as in solving big problems, with the extraordinary appearance of them in all areas of life, in medicine, agriculture, industry, the Internet, and others, where their use is extensive and is considered a source of information for technological progress. One of the important use is stitching the images, also called mosaic images. The stitching images means a grouping of images for the same sense with the overlapping areas to be a panoramic image of high resolution and wide width. With the modification and development of the algorithms used in this field in recent years, it has become one of the essential branches of image processing. There are many applications of stitching, used in maps and satellites, knowledge and positioning, etc. So this summary article will provide a set of image stitching techniques and investigate its use in terms of advantages, disadvantages, and accuracy for each one of them with comparative studies of several research papers in this field for the period of years (2017 – 2020). Therefore, this article may be useful for researchers working in this field to benefit and develop stitching algorithms in terms of discovering features and matching them to create a useful, problem-free, and high-resolution panoramic image.

Improved polarimetric analysis of human skin through stitching: advantages, limitations, and applications in dermatology

“…There are several techniques that can be used for image stitching. Feature-based stitching involves detecting distinctive features in each image and using these traits to align and stitch the images [19]. Homographybased stitching methods use a mathematical model known as homography to transform and align the images, taking into consideration any perspective shifts that may have happened [20].…”

“…Homographybased stitching methods use a mathematical model known as homography to transform and align the images, taking into consideration any perspective shifts that may have happened [20]. Direct techniques compute the best transformation directly from the images, without intermediate processes such as feature extraction or homography estimation [19]. The choice of the stitching approach will depend on the specific requirements and constraints of the application, such as the type and quality of the images, the desired level of accuracy, and the computational processing resources available [21].…”

Acquisition method of target leveling height based on machine vision

“…The histogram between images is balanced during image processing, and the darker one is corrected so that the illumination of several images is at the same level. [63][64][65][66][67][68][69][70][71][72][73][74][75] The schematic diagram of image stitching is shown in Figure 5.…”

“…In addition, because of the influence of illumination on the stitching result, lighting equipment is added to the image acquisition device to try to avoid the generation of noise. The histogram between images is balanced during image processing, and the darker one is corrected so that the illumination of several images is at the same level 63‐75 . The schematic diagram of image stitching is shown in Figure 5.…”

Analysis of the Photogrammetric Use of 360-Degree Cameras in Complex Heritage-Related Scenes: Case of the Necropolis of Qubbet el-Hawa (Aswan Egypt)

“…Regarding stitching techniques to obtain spherical images in a 360-degree camera, there are several classifications [62][63][64][65] taking into account some aspects related to the fusion and projection of images. In general, there are direct pixel-based methods and, most commonly, feature detection methods, which are based on algorithms such as the Scale-Invariant Feature Transform (SIFT) [3], and the Speeded Up Robust Features (SURF) [66], among others.…”