Images captured from different viewpoints or devices have often exhibited significant
geometric and photometric differences due to factors such as environmental variations …

In this paper, we propose an iterative framework, which consists of two phases: a generation
phase and a training phase, to generate realistic training data and yield a supervised …

Supervised homography estimation methods face a challenge due to the lack of adequate
labeled training data. To address this issue, we propose DMHomo, a diffusion model-based …

The accelerated growth of computer vision techniques (CVT) has allowed their application in
various disciplines, including horticulture, facilitating the work of producers, reducing costs …

Homography estimation is erroneous in the case of large-baseline due to the low image
overlay and limited receptive field. To address it, we propose a progressive estimation …

Existing homography and optical flow methods are erroneous in challenging scenes, such
as fog, rain, night, and snow because the basic assumptions such as brightness and …

The homography matrix plays a vital role in robotics and computer vision applications, but
mainstream estimators are usually customized for specific problems and are sensitive to …

Image stitching with parallax is still a challenging task. Existing methods often struggle to
maintain both the local and global structures of the image while reducing alignment artifacts …

Combining images from multiple cameras using computational imaging techniques is a
common way in remote sensing to achieve a larger field-of-view (FOV) and wider scene …