When synthesizing panoramic images from fish-eye images, dynamically segmenting the fish-eye image's valid region is crucial. This paper addresses the challenge of segmenting the valid region in fish-eye images caused by lens glare by proposing a segmentation algorithm that combines adaptive threshold edge detection with improved circle fitting. The process begins by performing gradient and direction calculations on the original fish-eye image to extract edge contour information. Subsequently, the edge contours undergo refinement using the non-maximum suppression method. An adaptive dual threshold is generated using the maximum between-class variance method to enable accurate edge detection. The algorithm further initializes and fits the edge points using the least squares method while incorporating a loss function to enhance the circle-fitting algorithm. Finally, an iterative gradient descent method is employed to iteratively solve for the optimal solution and segment the fish-eye image's valid region. Experimental results demonstrate the algorithm's ability to accurately detect contours and effectively segment the valid region, successfully addressing the challenge of extracting the valid region caused by lens glare.
The proposed video stabilization algorithm for head-mounted mobile platforms addresses the issue of video shake. In the feature detection and extraction stage, the Super point feature extraction algorithm is used to extract feature points. This algorithm provides a more comprehensive set of feature points compared to traditional feature detection methods, enabling accurate motion estimation.In the motion smoothing stage, the camera path of a specific camera is preprocessed using Kalman filtering to remove significant shaking factors. Then, the processed path is used as a baseline to simultaneously smooth all camera paths through spatio-temporal optimization, aiming to make the camera paths as similar as possible and reduce distortion. Finally, the optimal path is selected from the refined paths to address the deviation among multiple cameras.Experimental results have demonstrated that this method achieves comparable stability to other methods while significantly reducing the required processing time.
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