Adaptive Fractional-Order Multi-Scale Optimization TV-L1 Optical Flow Algorithm

Abstract

We propose an adaptive fractional multi-scale optimization optical flow algorithm, which for the first time improves the over-smoothing of optical flow estimation under the total variation model from the perspective of global feature and local texture balance, and solves the problem that the convergence of fractional optical flow algorithms depends on the order parameter. Specifically, a fractional-order discrete L1-regularization Total Variational Optical Flow model is constructed. On this basis, the Ant Lion algorithm is innovatively used to realize the iterative calculation of the optical flow equation, and the fractional order is dynamically adjusted to obtain an adaptive optimization algorithm with strong search accuracy and high efficiency. In this paper, the flexibility of optical flow estimation in weak gradient texture scenes is increased, and the optical flow extraction rate of target features at multiple scales is greatly improved. We show excellent recognition performance and stability under the MPI_Sintel and Middlebury benchmarks.

Crop and Weed Segmentation and Fractal Dimension Estimation Using Small Training Data in Heterogeneous Data Environment

“…Image processing has been widely adopted in various fields [3][4][5][6][7][8][9][10][11]. In addition, recently, deep learning methods have offered various solutions, and the use of computer vision has grown significantly in various applications including building monitoring, image enhancement, medical image processing, biomedical engineering, and underwater computer vision, where some research has adopted fractal-related perspectives, also in [9][10][11][12][13][14][15][16].…”