Abstract
Ghosting and seams are two major challenges in creating unmanned aerial vehicle (UAV) image mosaic. In response to these problems, this paper proposes an improved method for UAV image seam-line searching. First, an image matching algorithm is used to extract and match the features of adjacent images, so that they can be transformed into the same coordinate system. Then, the gray scale difference, the gradient minimum, and the optical flow value of pixels in adjacent image overlapped area in a neighborhood are calculated, which can be applied to creating an energy function for seam-line searching. Based on that, an improved dynamic programming algorithm is proposed to search the optimal seam-lines to complete the UAV image mosaic. This algorithm adopts a more adaptive energy aggregation and traversal strategy, which can find a more ideal splicing path for adjacent UAV images and avoid the ground objects better. The experimental results show that the proposed method can effectively solve the problems of ghosting and seams in the panoramic UAV images.
Highlights
Image mosaics have a long history starting in the early days of computer vision and photogrammetry
In order to verify the effectiveness of our method (Our-flow-DP), this paper utilized the unmanned aerial vehicle (UAV) images from different regions with different flight altitudes and cameras, and compared the experimental results with the classic Duplaquet’s dynamic programming method using three search directions (Duplaquet3-DP), dynamic programming method based on Duplaquet using four search directions (Duplaquet4-DP), and the dynamic programming methods from OpenCV (OpenCV-DP)
This paper selected the essential problems of dynamic programming algorithms for image seam-line optimization, and introduced the optical flow value of the pixels in the overlapped regions for seam-line searching
Summary
Image mosaics have a long history starting in the early days of computer vision and photogrammetry. With the rise of UAV remote sensing technologies, this research has become paramount to many applications based on UAV survey including 3D reconstruction, ecological farming, disaster emergency management, and photography activity. These are due to UAV remote sensing technology’s strengths of low-cost, high–Speed, and easy accessibility [1,2,3,4,5]. In many applications mentioned above, in order to expand the image coverage area to capture more information from the target area, multiple UAV images are collected, leading to the need of mosaic multiple images to form a panoramic image. There are many challenges about the state-of-the-art image mosaic methods
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