This paper presents a comprehensive survey of facial feature point detection with the
assistance of abundant manually labeled images. Facial feature point detection favors many …

This paper studies the problem of simultaneously aligning a batch of linearly correlated
images despite gross corruption (such as occlusion). Our method seeks an optimal set of …

Unsupervised joint alignment of images has been demonstrated to improve performance on
recognition tasks such as face verification. Such alignment reduces undesired variability due …

We present an unsupervised learning framework for decomposing images into layers of
automatically discovered object models. Contrary to recent approaches that model image …

Recent advances in image clustering typically focus on learning better deep
representations. In contrast, we present an orthogonal approach that does not rely on …

Determining dense semantic correspondences across objects and scenes is a difficult
problem that underpins many higher-level computer vision algorithms. Unlike canonical …

Time-series analysis is confounded by nonlinear time warping of the data. Traditional
methods for joint alignment do not generalize: after aligning a given signal ensemble, they …

We address the alignment of a group of images with simultaneous registration. Therefore,
we provide further insights into a recently introduced framework for multivariate similarity …

Robust high-dimensional data processing has witnessed an exciting development in recent
years. Theoretical results have shown that it is possible using convex programming to …

Aligning a given set of images is usually conducted in batch mode manner, which not only
requires large amount of memory but also adjusts all the previous transformations to register …