Optical flow estimation is a crucial task in computer vision that provides low-level motion
information. Despite recent advances, real-world applications still present significant …

Supervised deep networks are among the best methods for finding correspondences in
stereo image pairs. Like all supervised approaches, these networks require ground truth …

Although convolutional neural network (CNN)-based optical flow approaches have exhibited
good performance in terms of computational accuracy and efficiency in recent years, the …

Computing optical flow is a fundamental problem in computer vision. However, deep
learning-based optical flow techniques do not perform well for non-rigid movements such as …

In pattern recognition, depending on how the training sample is represented, a theory
associated with the standard representation of the training sample (information) and the …

In computer vision, models and methods for facial expression recognition are continually in
development. Several models aim to describe the highly complex structure of different faces …

Large-scale non-linear least squares (NLLS) optimization problems lie at the core of many
graphics, vision, and imaging applications. The mathematical descriptions of these objective …