Effective Video Frame Acquisition for Image Stitching

Abstract

We present an effective video frame (including reference frame and key frames) acquisition method for image stitching. The method simultaneously analyzes different types of factors, namely, the video-level stability, image-level stability, and content scale stability, to take advantage of their complementary strengths. We model the three factors with three modules that are learned from an analysis of the shooting process. The video stabilization module (VSM) selects a stable segment, while the shooting distance module (SDM) obtains a similar content scale. They collaborate during the reference video sequence so that they can benefit from each other. Then, the image quality module (IQM) obtains a reference frame from the above sequence by choosing high-quality images. Finally, to obtain the key frame set, the SDM and IQM are again used to continuously filter the overlapping video sequences formed by the reference frame or the latest key frame. In particular, a comprehensive dataset containing a variety of challenges and scenarios is introduced. We have conducted an extensive set of experiments on this dataset. The results confirm the effectiveness of each module and their collaboration; our method outperforms current state-ofthe-art methods. INDEX TERMS Image stitching; Effective video frame; Image stitching dataset

Image Stitching Technology in 3D Film Production Based on Digital Image Processing

“…Image stitching is an important task in computer vision and image processing systems. Image stitching or photo stitching is a process that creates a high-resolution image by combining multiple images [1]. Image stitching technology is widely used for photo editing processes that contain certain camera features.…”