Robust optical flow estimation based on brightness correction fields

Optical flow estimation is still an important task in computer vision with many interesting applications.However,the results obtained by most of the optical flow techniques are affected by motion discontinuities or illumination changes.In this paper,we introduce a brightness correction field combined with a gradient constancy constraint to reduce the sensibility to brightness changes between images to be estimated.The advantage of this brightness correction field is its simplicity in terms of computational complexity and implementation.By analyzing the deficiencies of the traditional total variation regularization term in weakly textured areas,we also adopt a structure-adaptive regularization based on the robust Huber norm to preserve motion discontinuities.Finally,the proposed energy functional isminimized by solving its corresponding Euler-Lagrange equation in a more effective multi-resolution scheme,which integrates the twice downsampling strategy with a support-weight median filter.Numerous experiments show that our method is more effective and produces more accurate results for optical flow estimation.

Exponential stability for impulsive Cohen-Grossberg neural networks with time-varying delays and distributed delays

Purpose–The purpose of this paper is to develop a methodology for the existence and global exponential stability of the unique equilibrium point of a class of impulsive Cohen-Grossberg neural networks.Design/methodology/approach–The authors perform M-matrix theory and homeomorphism mapping principle to investigate a class of impulsive Cohen-Grossberg networks with time-varying delays and distributed delays.The approach builds on new sufficient criterion without strict conditions imposed on self-regulation functions.Findings–The authors’approach results in new sufficient criteria easy to verify but without the usual assumption that the activation functions are bounded and the time-varying delays are differentiable.An example shows the effectiveness and superiority of the obtained results over some previously known results.Originality/value–The novelty of the proposed approach lies in removing the usual assumption that the activation functions are bounded and the time-varying delays are differentiable,and the use of M-matrix theory and homeomorphism mapping principle for the existence and global exponential stability of the unique equilibrium point of a class of impulsive Cohen-Grossberg neural networks.