1,040 research outputs found
Video Interpolation using Optical Flow and Laplacian Smoothness
Non-rigid video interpolation is a common computer vision task. In this paper
we present an optical flow approach which adopts a Laplacian Cotangent Mesh
constraint to enhance the local smoothness. Similar to Li et al., our approach
adopts a mesh to the image with a resolution up to one vertex per pixel and
uses angle constraints to ensure sensible local deformations between image
pairs. The Laplacian Mesh constraints are expressed wholly inside the optical
flow optimization, and can be applied in a straightforward manner to a wide
range of image tracking and registration problems. We evaluate our approach by
testing on several benchmark datasets, including the Middlebury and Garg et al.
datasets. In addition, we show application of our method for constructing 3D
Morphable Facial Models from dynamic 3D data
Joint optimization of manifold learning and sparse representations for face and gesture analysis
Face and gesture understanding algorithms are powerful enablers in intelligent vision systems for surveillance, security, entertainment, and smart spaces. In the future, complex networks of sensors and cameras may disperse directions to lost tourists, perform directory lookups in the office lobby, or contact the proper authorities in case of an emergency. To be effective, these systems will need to embrace human subtleties while interacting with people in their natural conditions. Computer vision and machine learning techniques have recently become adept at solving face and gesture tasks using posed datasets in controlled conditions. However, spontaneous human behavior under unconstrained conditions, or in the wild, is more complex and is subject to considerable variability from one person to the next. Uncontrolled conditions such as lighting, resolution, noise, occlusions, pose, and temporal variations complicate the matter further. This thesis advances the field of face and gesture analysis by introducing a new machine learning framework based upon dimensionality reduction and sparse representations that is shown to be robust in posed as well as natural conditions. Dimensionality reduction methods take complex objects, such as facial images, and attempt to learn lower dimensional representations embedded in the higher dimensional data. These alternate feature spaces are computationally more efficient and often more discriminative. The performance of various dimensionality reduction methods on geometric and appearance based facial attributes are studied leading to robust facial pose and expression recognition models. The parsimonious nature of sparse representations (SR) has successfully been exploited for the development of highly accurate classifiers for various applications. Despite the successes of SR techniques, large dictionaries and high dimensional data can make these classifiers computationally demanding. Further, sparse classifiers are subject to the adverse effects of a phenomenon known as coefficient contamination, where for example variations in pose may affect identity and expression recognition. This thesis analyzes the interaction between dimensionality reduction and sparse representations to present a unified sparse representation classification framework that addresses both issues of computational complexity and coefficient contamination. Semi-supervised dimensionality reduction is shown to mitigate the coefficient contamination problems associated with SR classifiers. The combination of semi-supervised dimensionality reduction with SR systems forms the cornerstone for a new face and gesture framework called Manifold based Sparse Representations (MSR). MSR is shown to deliver state-of-the-art facial understanding capabilities. To demonstrate the applicability of MSR to new domains, MSR is expanded to include temporal dynamics. The joint optimization of dimensionality reduction and SRs for classification purposes is a relatively new field. The combination of both concepts into a single objective function produce a relation that is neither convex, nor directly solvable. This thesis studies this problem to introduce a new jointly optimized framework. This framework, termed LGE-KSVD, utilizes variants of Linear extension of Graph Embedding (LGE) along with modified K-SVD dictionary learning to jointly learn the dimensionality reduction matrix, sparse representation dictionary, sparse coefficients, and sparsity-based classifier. By injecting LGE concepts directly into the K-SVD learning procedure, this research removes the support constraints K-SVD imparts on dictionary element discovery. Results are shown for facial recognition, facial expression recognition, human activity analysis, and with the addition of a concept called active difference signatures, delivers robust gesture recognition from Kinect or similar depth cameras
Low-Rank Isomap Algorithm
The Isomap is a well-known nonlinear dimensionality reduction method that
highly suffers from computational complexity. Its computational complexity
mainly arises from two stages; a) embedding a full graph on the data in the
ambient space, and b) a complete eigenvalue decomposition. Although the
reduction of the computational complexity of the graphing stage has been
investigated, yet the eigenvalue decomposition stage remains a bottleneck in
the problem. In this paper, we propose the Low-Rank Isomap algorithm by
introducing a projection operator on the embedded graph from the ambient space
to a low-rank latent space to facilitate applying the partial eigenvalue
decomposition. This approach leads to reducing the complexity of Isomap to a
linear order while preserving the structural information during the
dimensionality reduction process. The superiority of the Low-Rank Isomap
algorithm compared to some state-of-art algorithms is experimentally verified
on facial image clustering in terms of speed and accuracy
Landmarks Augmentation with Manifold-Barycentric Oversampling
The training of Generative Adversarial Networks (GANs) requires a large
amount of data, stimulating the development of new augmentation methods to
alleviate the challenge. Oftentimes, these methods either fail to produce
enough new data or expand the dataset beyond the original manifold. In this
paper, we propose a new augmentation method that guarantees to keep the new
data within the original data manifold thanks to the optimal transport theory.
The proposed algorithm finds cliques in the nearest-neighbors graph and, at
each sampling iteration, randomly draws one clique to compute the Wasserstein
barycenter with random uniform weights. These barycenters then become the new
natural-looking elements that one could add to the dataset. We apply this
approach to the problem of landmarks detection and augment the available
annotation in both unpaired and in semi-supervised scenarios. Additionally, the
idea is validated on cardiac data for the task of medical segmentation. Our
approach reduces the overfitting and improves the quality metrics beyond the
original data outcome and beyond the result obtained with popular modern
augmentation methods.Comment: 11 pages, 4 figures, 3 tables. I.B. and N.B. contributed equally.
D.V.D. is the corresponding autho
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