Multilinear Wavelets: A Statistical Shape Space for Human Faces

We present a statistical model for $3$D human faces in varying expression, which decomposes the surface of the face using a wavelet transform, and learns many localized, decorrelated multilinear models on the resulting coefficients. Using this model we are able to reconstruct faces from noisy and occluded $3$D face scans, and facial motion sequences. Accurate reconstruction of face shape is important for applications such as tele-presence and gaming. The localized and multi-scale nature of our model allows for recovery of fine-scale detail while retaining robustness to severe noise and occlusion, and is computationally efficient and scalable. We validate these properties experimentally on challenging data in the form of static scans and motion sequences. We show that in comparison to a global multilinear model, our model better preserves fine detail and is computationally faster, while in comparison to a localized PCA model, our model better handles variation in expression, is faster, and allows us to fix identity parameters for a given subject.Comment: 10 pages, 7 figures; accepted to ECCV 201

Context-sensitive Spelling Correction Using Google Web 1T 5-Gram Information

In computing, spell checking is the process of detecting and sometimes providing spelling suggestions for incorrectly spelled words in a text. Basically, a spell checker is a computer program that uses a dictionary of words to perform spell checking. The bigger the dictionary is, the higher is the error detection rate. The fact that spell checkers are based on regular dictionaries, they suffer from data sparseness problem as they cannot capture large vocabulary of words including proper names, domain-specific terms, technical jargons, special acronyms, and terminologies. As a result, they exhibit low error detection rate and often fail to catch major errors in the text. This paper proposes a new context-sensitive spelling correction method for detecting and correcting non-word and real-word errors in digital text documents. The approach hinges around data statistics from Google Web 1T 5-gram data set which consists of a big volume of n-gram word sequences, extracted from the World Wide Web. Fundamentally, the proposed method comprises an error detector that detects misspellings, a candidate spellings generator based on a character 2-gram model that generates correction suggestions, and an error corrector that performs contextual error correction. Experiments conducted on a set of text documents from different domains and containing misspellings, showed an outstanding spelling error correction rate and a drastic reduction of both non-word and real-word errors. In a further study, the proposed algorithm is to be parallelized so as to lower the computational cost of the error detection and correction processes.Comment: LACSC - Lebanese Association for Computational Sciences - http://www.lacsc.or

Intrinsic Dynamic Shape Prior for Fast, Sequential and Dense Non-Rigid Structure from Motion with Detection of Temporally-Disjoint Rigidity

While dense non-rigid structure from motion (NRSfM) has been extensively studied from the perspective of the reconstructability problem over the recent years, almost no attempts have been undertaken to bring it into the practical realm. The reasons for the slow dissemination are the severe ill-posedness, high sensitivity to motion and deformation cues and the difficulty to obtain reliable point tracks in the vast majority of practical scenarios. To fill this gap, we propose a hybrid approach that extracts prior shape knowledge from an input sequence with NRSfM and uses it as a dynamic shape prior for sequential surface recovery in scenarios with recurrence. Our Dynamic Shape Prior Reconstruction (DSPR) method can be combined with existing dense NRSfM techniques while its energy functional is optimised with stochastic gradient descent at real-time rates for new incoming point tracks. The proposed versatile framework with a new core NRSfM approach outperforms several other methods in the ability to handle inaccurate and noisy point tracks, provided we have access to a representative (in terms of the deformation variety) image sequence. Comprehensive experiments highlight convergence properties and the accuracy of DSPR under different disturbing effects. We also perform a joint study of tracking and reconstruction and show applications to shape compression and heart reconstruction under occlusions. We achieve state-of-the-art metrics (accuracy and compression ratios) in different scenarios