Manifold Constrained Low-Rank Decomposition

Low-rank decomposition (LRD) is a state-of-the-art method for visual data reconstruction and modelling. However, it is a very challenging problem when the image data contains significant occlusion, noise, illumination variation, and misalignment from rotation or viewpoint changes. We leverage the specific structure of data in order to improve the performance of LRD when the data are not ideal. To this end, we propose a new framework that embeds manifold priors into LRD. To implement the framework, we design an alternating direction method of multipliers (ADMM) method which efficiently integrates the manifold constraints during the optimization process. The proposed approach is successfully used to calculate low-rank models from face images, hand-written digits and planar surface images. The results show a consistent increase of performance when compared to the state-of-the-art over a wide range of realistic image misalignments and corruptions

Automatic facial landmark labeling with minimal supervision.

Abstract Landmark labeling of training images is essential for many learning tasks in computer vision, such as object detection, tracking, an

Unsupervised Face Alignment by Robust Nonrigid Mapping

We propose a novel approach to unsupervised facial im-age alignment. Differently from previous approaches, that are confined to affine transformations on either the entire face or separate patches, we extract a nonrigid mapping be-tween facial images. Based on a regularized face model, we frame unsupervised face alignment into the Lucas-Kanade image registration approach. We propose a robust optimiza-tion scheme to handle appearance variations. The method is fully automatic and can cope with pose variations and ex-pressions, all in an unsupervised manner. Experiments on a large set of images showed that the approach is effective. 1

Unsupervised alignment of objects in images

With the advent of computer vision, various applications become interested to apply it to interpret the 3D and 2D scenes. The main core of computer vision is visual object detection which deals with detecting and representing objects in the image. Visual object detection requires to learn a model of each class type (e.g. car, cat) to be capable to detect objects belonging to the same class. Class learning benefits from a method which automatically aligns class examples making learning more straightforward. The objective of this thesis is to further develop the sate-of-the-art feature-based alignment method which rigidly and automatically aligns object class images to a manually selected seed image. We try to compensate the weakness by providing a method to automatically select the best seed from dataset. Our method first extracts features by utilizing dense sampling method and then scale invariant feature transform (SIFT) descriptor is used to find best matches as initial local feature matches. The final alignment is based on spatial scoring procedure where the initial matches are refined to a set of spatially verified matches. The spatial score is used next to calculate similarity scores. We propose an algorithm which operates on spatial and similarity scores and finally selects the best seed. We also investigate the performance of step-wise alignment using minimum spanning tree (MST) and Dijkstra shortest path instead of direct alignment utilizing a single seed. We conduct our experiments using classes of Caltech-101 for which our unsupervised seed selection and step-wise alignment achieve state-of-the-art performance