Robust Image Stitching with Multiple Registrations

Yuan

et al. 2020

Sensors

Image stitching based on a global alignment model is widely used in computer vision. However, the resulting stitched image may look blurry or ghosted due to parallax. To solve this problem, we propose a parallax-tolerant image stitching method based on nonrigid warping in this paper. Given a group of putative feature correspondences between overlapping images, we first use a semiparametric function fitting, which introduces a motion coherence constraint to remove outliers. Then, the input images are warped according to a nonrigid warp model based on Gaussian radial basis functions. The nonrigid warping is a kind of elastic deformation that is flexible and smooth enough to eliminate moderate parallax errors. This leads to high-precision alignment in the overlapped region. For the nonoverlapping region, we use a rigid similarity model to reduce distortion. Through effective transition, the nonrigid warping of the overlapped region and the rigid warping of the nonoverlapping region can be used jointly. Our method can obtain more accurate local alignment while maintaining the overall shape of the image. Experimental results on several challenging data sets for urban scene show that the proposed approach is better than state-of-the-art approaches in both qualitative and quantitative indicators.

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image Stitching Based on Nonrigid Warping for Urban Scene

Yuan

et al. 2020

Sensors

Lecture Notes in Computer Science

“…Our registration approach largely follows [10], except that we only use a single registration in the seam finding stage. To generate a registration, we first identify a homography that matches a large portion of the image and then run a content-preserving warp (CPW) in order to fix small misalignments [19].…”

Section: Registrationmentioning

confidence: 99%

Object-Centered Image Stitching

Herrmann

Wang

Bowen

et al. 2018

Self Cite

Image stitching is typically decomposed into three phases: registration, which aligns the source images with a common target image; seam finding, which determines for each target pixel the source image it should come from; and blending, which smooths transitions over the seams. As described in [1], the seam finding phase attempts to place seams between pixels where the transition between source images is not noticeable. Here, we observe that the most problematic failures of this approach occur when objects are cropped, omitted, or duplicated. We therefore take an object-centered approach to the problem, leveraging recent advances in object detection [2,3,4]. We penalize candidate solutions with this class of error by modifying the energy function used in the seam finding stage. This produces substantially more realistic stitching results on challenging imagery. In addition, these methods can be used to determine when there is non-recoverable occlusion in the input data, and also suggest a simple evaluation metric that can be used to evaluate the output of stitching algorithms.

“…[40] (c) AutoStitch [27] (d) Herrmann vd. [45] (e) Önerilen yöntem Şekil 10. Önerilen yöntemin metin görüntüsü birleştirme senaryosunda değerlendirilmesi (Evaluation of the proposed method in a text image stitching scenario)…”

Section: Gi̇ri̇ş (Introduction)unclassified

Automatic Image Stitching Using SURF and RANSAC Based Homography Estimation

Baş

2022

Politeknik Dergisi