序列图像运动自适应V1-MT光流估计算法

针对传统算法在抗光照变化影响、大位移光流和异质点滤除等方面的不足,从人类视觉认知机理出发,提出了一种基于机器学习和生物模型的运动自适应V1-MT(motion-adaptive V1-MT,MAV1MT)序列图像光流估计算法.首先,引入基于ROF模型的结构纹理分解(structure-texture decomposition,STD)技术,有效解决了光照和色彩变化的影响.其次,利用多V1细胞加权组合及非线性正则化模拟MT细胞模型,并结合岭回归训练学习得到运动自适应的权重,解决对目标的运动速度感知问题.最后,引入由粗到精的增强方法和图像金字塔局部运动估计采样,将V1-MT运动估计模型应用于实际大位移视频序列.理论分析和实验结果表明,新方法能更加拟合人眼视觉信息处理特性,对视频序列具有普适、有效、鲁棒的运动感知性能.

Bio-inspired motion-adaptive estimation algorithm of sequence image

To overcome the insufficiencies of varying illumination, large displacement estimation, and outlier removal, a motion-adaptive V1- MT (MAV1MT) motion estimation algorithm based on machine learning and a bio-inspired model of sequence image was proposed, starting from the theory of visual cognition. First, a structure-texture decomposition technique based on the Rudin Osher Fatemi (ROF) model was introduced to manage the variation in illumination and color. Then, a pooling stage at the MT level with non-normalization, which combines the afferent V1 responses using the adaptive weights trained by ridge regression, is modeled to ob-tain the local velocities. Finally, through introducing the coarse-to-fine method and pyramid structure subsampling of the local motion, the MAV1MT model is used on realistic video. Theoretical analysis and experimental results suggest the new algorithm, which is more fitting to information processing features of the human visual system, has universal, effective and robust motion perception perform-ance.