Fish-eye image effective region segmentation algorithm based on circle fitting
Taiping Jiang, Nong Yu, Xue Li · 2023
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.