Image registration is a fundamental medical image analysis task, and a wide variety of
approaches have been proposed. However, only a few studies have comprehensively …

As computer vision algorithms increase in capability, their applications in clinical systems
will become more pervasive. These applications include: diagnostics, such as colonoscopy …

We propose a novel video frame interpolation algorithm based on asymmetric bilateral
motion estimation (ABME), which synthesizes an intermediate frame between two input …

In recent years, deep-learning-based hyperspectral image (HSI) classification methods have
achieved significant development. The superior capability of feature extraction from these …

Full-motion cost volumes play a central role in current state-of-the-art optical flow methods.
However, constructed using simple feature correlations, they lack the ability to encapsulate …

Animals have evolved highly functional visual systems to understand motion, assisting
perception even under complex environments. In this paper, we work towards develo** a …

Optical flow is a fundamental method used for quantitative motion estimation on the image
plane. In the deep learning era, most works treat it as a task of'matching of features', learning …

Unsupervised learning has been popular in various computer vision tasks, including visual
object tracking. However, prior unsupervised tracking approaches rely heavily on spatial …

Optical flow, or the estimation of motion fields from image sequences, is one of the
fundamental problems in computer vision. Unlike most pixel-wise tasks that aim at achieving …

In this paper, we describe a graph-based algorithm that uses the features obtained by a self-
supervised transformer to detect and segment salient objects in images and videos. With this …