Two-dimensional PCA : a new approach to appearance-based face representation and recognition

2003-2004 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Symbiosis between the TRECVid benchmark and video libraries at the Netherlands Institute for Sound and Vision

Audiovisual archives are investing in large-scale digitisation efforts of their analogue holdings and, in parallel, ingesting an ever-increasing amount of born- digital files in their digital storage facilities. Digitisation opens up new access paradigms and boosted re-use of audiovisual content. Query-log analyses show the shortcomings of manual annotation, therefore archives are complementing these annotations by developing novel search engines that automatically extract information from both audio and the visual tracks. Over the past few years, the TRECVid benchmark has developed a novel relationship with the Netherlands Institute of Sound and Vision (NISV) which goes beyond the NISV just providing data and use cases to TRECVid. Prototype and demonstrator systems developed as part of TRECVid are set to become a key driver in improving the quality of search engines at the NISV and will ultimately help other audiovisual archives to offer more efficient and more fine-grained access to their collections. This paper reports the experiences of NISV in leveraging the activities of the TRECVid benchmark

KPCA Plus LDA : a complete kernel Fisher discriminant framework for feature extraction and recognition

2004-2005 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Phenotypic and molecular assessment of seven patients with 6p25 deletion syndrome: Relevance to ocular dysgenesis and hearing impairment

BACKGROUND: Thirty-nine patients have been described with deletions involving chromosome 6p25. However, relatively few of these deletions have had molecular characterization. Common phenotypes of 6p25 deletion syndrome patients include hydrocephalus, hearing loss, and ocular, craniofacial, skeletal, cardiac, and renal malformations. Molecular characterization of deletions can identify genes that are responsible for these phenotypes. METHODS: We report the clinical phenotype of seven patients with terminal deletions of chromosome 6p25 and compare them to previously reported patients. Molecular characterization of the deletions was performed using polymorphic marker analysis to determine the extents of the deletions in these seven 6p25 deletion syndrome patients. RESULTS: Our results, and previous data, show that ocular dysgenesis and hearing impairment are the two most highly penetrant phenotypes of the 6p25 deletion syndrome. While deletion of the forkhead box C1 gene (FOXC1) probably underlies the ocular dysgenesis, no gene in this region is known to be involved in hearing impairment. CONCLUSIONS: Ocular dysgenesis and hearing impairment are the two most common phenotypes of 6p25 deletion syndrome. We conclude that a locus for dominant hearing loss is present at 6p25 and that this locus is restricted to a region distal to D6S1617. Molecular characterization of more 6p25 deletion patients will aid in refinement of this locus and the identification of a gene involved in dominant hearing loss

Semi-supervised assessment of incomplete LV coverage in cardiac MRI using generative adversarial nets

Cardiac magnetic resonance (CMR) images play a growing role in diagnostic imaging of cardiovascular diseases. Ensuring full coverage of the Left Ventricle (LV) is a basic criteria of CMR image quality. Complete LV coverage, from base to apex, precedes accurate cardiac volume and functional assessment. Incomplete coverage of the LV is identified through visual inspection, which is time-consuming and usually done retrospectively in large imaging cohorts. In this paper, we propose a novel semi-supervised method to check the coverage of LV from CMR images by using generative adversarial networks (GAN), we call it Semi-Coupled-GANs (SCGANs). To identify missing basal and apical slices in a CMR volume, a two-stage framework is proposed. First, the SCGANs generate adversarial examples and extract high-level features from the CMR images; then these image attributes are used to detect missing basal and apical slices. We constructed extensive experiments to validate the proposed method on UK Biobank with more than 6000 independent volumetric MR scans, which achieved high accuracy and robust results for missing slice detection, comparable with those of state of the art deep learning methods. The proposed method, in principle, can be adapted to other CMR image data for LV coverage assessment

3D Face Reconstruction from Light Field Images: A Model-free Approach

Reconstructing 3D facial geometry from a single RGB image has recently instigated wide research interest. However, it is still an ill-posed problem and most methods rely on prior models hence undermining the accuracy of the recovered 3D faces. In this paper, we exploit the Epipolar Plane Images (EPI) obtained from light field cameras and learn CNN models that recover horizontal and vertical 3D facial curves from the respective horizontal and vertical EPIs. Our 3D face reconstruction network (FaceLFnet) comprises a densely connected architecture to learn accurate 3D facial curves from low resolution EPIs. To train the proposed FaceLFnets from scratch, we synthesize photo-realistic light field images from 3D facial scans. The curve by curve 3D face estimation approach allows the networks to learn from only 14K images of 80 identities, which still comprises over 11 Million EPIs/curves. The estimated facial curves are merged into a single pointcloud to which a surface is fitted to get the final 3D face. Our method is model-free, requires only a few training samples to learn FaceLFnet and can reconstruct 3D faces with high accuracy from single light field images under varying poses, expressions and lighting conditions. Comparison on the BU-3DFE and BU-4DFE datasets show that our method reduces reconstruction errors by over 20% compared to recent state of the art

Gate-controlled Guiding of Electrons in Graphene

Ballistic semiconductor structures have allowed the realization of optics-like phenomena in electronics, including magnetic focusing and lensing. An extension that appears unique to graphene is to use both n and p carrier types to create electronic analogs of optical devices having both positive and negative indices of refraction. Here, we use gate-controlled density with both p and n carrier types to demonstrate the analog of the fiber-optic guiding in graphene. Two basic effects are investigated: (1) bipolar p-n junction guiding, based on the principle of angle-selective transmission though the graphene p-n interface, and (2) unipolar fiber-optic guiding, using total internal reflection controlled by carrier density. Modulation of guiding efficiency through gating is demonstrated and compared to numerical simulations, which indicates that interface roughness limits guiding performance, with few-nanometer effective roughness extracted. The development of p-n and fiber-optic guiding in graphene may lead to electrically reconfigurable wiring in high-mobility devices.Comment: supplementary materal at http://marcuslab.harvard.edu/papers/OG_SI.pd