Abstract
Image stitching aims at generating high-quality panoramas with the lowest computational cost. In this paper, we present an improved parallax image-stitching algorithm using feature blocks (PIFB), which achieves a more accurate alignment and faster calculation speed. First, each image is divided into feature blocks using an improved fuzzy C-Means (FCM) algorithm, and the characteristic descriptor of each feature block is extracted using scale invariant feature transform (SIFT). The feature matching block of the reference image and the target image are matched and then determined, and the image is pre-registered using the homography calculated by the feature points in the feature block. Finally, the overlapping area is optimized to avoid ghosting and shape distortion. The improved algorithm considering pre-blocking and block stitching effectively reduced the iterative process of feature point matching and homography calculation. More importantly, the problem that the calculated homography matrix was not global has been solved. Ghosting and shape warping are significantly eliminated by re-optimizing the overlap of the image. The performance of the proposed approach is demonstrated using several challenging cases.
Highlights
Stitching images is a broad intercrossing technology that is widely used in many disciplines, such as computer vision, image processing, and computer graphics, and it is widely used to obtain panorama images [1,2,3]
Based on the global pre-registration, the image is first meshed and the local homography constraint, the local similarity constraint, and the global similarity constraint are introduced into the constructed energy function for mesh optimization
An effective and efficient image stitching method based on feature blocks is proposed in this study
Summary
Stitching images is a broad intercrossing technology that is widely used in many disciplines, such as computer vision, image processing, and computer graphics, and it is widely used to obtain panorama images [1,2,3]. Fan Zhang presented a parallax-tolerant image stitching method (CPW) [12] This method combines content-preserving and seam-driven warps, and position transform and shape distortion constraints are used to approximate the target image, which can minimize the registration error and preserve the stiffness of the scene. Most existing methods use the SIFT method to calculate image feature points In this algorithm, detecting and describing feature points in a full image and matching feature points are the two most time-consuming steps. The remainder of this paper is organized as follows: Section 2 describes the image being segmented using an improved FCM algorithm to determine the overlap; Section 3 describes the global homography calculated using feature blocks.
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