Image stitching with perspective-preserving warping

Image stitching algorithms often adopt the global transformation, such as homography, and work well for planar scenes or parallax free camera motions. However, these conditions are easily violated in practice. With casual camera motions, variable taken views, large depth change, or complex structures, it is a challenging task for stitching these images. The global transformation model often provides dreadful stitching results, such as misalignments or projective distortions, especially perspective distortion. To this end, we suggest a perspective-preserving warping for image stitching, which spatially combines local projective transformations and similarity transformation. By weighted combination scheme, our approach gradually extrapolates the local projective transformations of the overlapping regions into the non-overlapping regions, and thus the final warping can smoothly change from projective to similarity. The proposed method can provide satisfactory alignment accuracy as well as reduce the projective distortions and maintain the multi-perspective view. Experiments on a variety of challenging images confirm the efficiency of the approach.Comment: ISPRS 2016 - XXIII ISPRS Congress: Prague, Czech Republic, 201

Computer Vision algorithms performance in architectural heritage multi-image based projects. General overview and operative evaluation: the North Tower of Buñol's Castle (Spain)

[EN] Multi-image based modeling has proven to be effective providing solutions for surveying and documenting cultural heritage, and in particular architectural heritage. In addition to the issues related with instruments and captation strategy, the operativity of these projects is supported by three bases: Computer Vision (C.V.) algorithms, analytical close-range photogrammetry, and theory of errors. In this work we propose an approach that examines the importance of the first, from two points of view. On one hand, we present a brief overview of its intervention in the different processing stages, both in photomodeling as in photograms stitching projects, thus reviewing the fundaments regarding the two classic branches of architectural photogrammetry. On the other, we present a review of the operational strategy with these algorithms, through a case study that evaluates the results of two software applications, advancing some methodological improvements.Cabanes Ginés, JL.; Bonafé, C. (2021). Computer Vision algorithms performance in architectural heritage multi-image based projects. General overview and operative evaluation: the North Tower of Buñol's Castle (Spain). SCIRES-IT. 11(2):125-138. https://doi.org/10.2423/i22394303v11n2p12512513811

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Department of Biomedical EngineeringImage stitching is a well-known method to make panoramic image which has a wide field-of-view and high resolution. It has been used in various fields such as digital map, gigapixel imaging, and 360-degree camera. However, commercial stitching tools often fail, require a lot of processing time, and only work on certain images. The problems of existing tools are mainly caused by trying to stitch the wrong image pair. To overcome these problems, it is important to select suitable image pair for stitching in advance. Nevertheless, there are no universal standards to judge the good image pairs. Moreover, the derived stitching algorithms cannot be compatible with each other because they conform to their own available criteria. Here, we present universal stitching parameters and their conditions for selecting good image pairs. The proposed stitching parameters can be easily calculated through analysis of corresponding features and homography, which are basic elements in feature-based image stitching algorithm. In order to specify the conditions of the stitching parameters, we devised a new method to calculate stitching accuracy for qualifying stitching results into 3 classesgood, bad, and fail. With the classed stitching results, the values of the stitching parameters could be checked how they differ in each class. Through experiments with large datasets, the most valid parameter for each class is identified as filtering level which is calculated in corresponding feature analysis. In addition, supplemental experiments were conducted with various datasets to demonstrate the validity of the filtering level. As a result of our study, universal stitching parameters can judge the success of stitching, so that it is possible to prevent stitching errors through parameter verification test in advance. This paper can greatly contribute to guide for creating high performance and high efficiency stitching software by applying the proposed stitching conditions.ope