Empirical mode decomposition-based facial pose estimation inside video sequences

We describe a new pose-estimation algorithm via integration of the strength in both empirical mode decomposition (EMD) and mutual information. While mutual information is exploited to measure the similarity between facial images to estimate poses, EMD is exploited to decompose input facial images into a number of intrinsic mode function (IMF) components, which redistribute the effect of noise, expression changes, and illumination variations as such that, when the input facial image is described by the selected IMF components, all the negative effects can be minimized. Extensive experiments were carried out in comparisons to existing representative techniques, and the results show that the proposed algorithm achieves better pose-estimation performances with robustness to noise corruption, illumination variation, and facial expressions

Quantifying the source of enhancement in experimental continuous variable quantum illumination

A quantum illumination protocol exploits correlated light beams to enhance the probability of detection of a partially reflecting object lying in a very noisy background. Recently a simple photon-number-detection based implementation of a quantum illumination-like scheme has been provided in [Lopaeva {\it et al,}, Phys. Rev. Lett. {\bf 101}, 153603 (2013)] where the enhancement is preserved despite the loss of non-classicality. In the present paper we investigate the source for quantum advantage in that realization. We introduce an effective two-mode description of the light sources and analyze the mutual information as quantifier of total correlations in the effective two-mode picture. In the relevant regime of a highly thermalized background, we find that the improvement in the signal-to-noise ratio achieved by the entangled sources over the unentangled thermal ones amounts exactly to the ratio of the effective mutual informations of the corresponding sources. More precisely, both quantities tend to a common limit specified by the squared ratio of the respective cross-correlations. A thorough analysis of the experimental data confirms this theoretical result.Comment: 6 pages, 3 figures. Published versio

Mutual Illumination Photometric Stereo

Many techniques have been developed in computer vision to recover three-dimensional shape from two-dimensional images. These techniques impose various combinations of assumptions/restrictions of conditions to produce a representation of shape (e.g. surface normals or a height map). Although great progress has been made it is a problem which remains far from solved. In this thesis we propose a new approach to shape recovery - namely `mutual illumination photometric stereo'. We exploit the presence of colourful mutual illumination in an environment to recover the shape of objects from a single image

Learning Compositional Visual Concepts with Mutual Consistency

Compositionality of semantic concepts in image synthesis and analysis is appealing as it can help in decomposing known and generatively recomposing unknown data. For instance, we may learn concepts of changing illumination, geometry or albedo of a scene, and try to recombine them to generate physically meaningful, but unseen data for training and testing. In practice however we often do not have samples from the joint concept space available: We may have data on illumination change in one data set and on geometric change in another one without complete overlap. We pose the following question: How can we learn two or more concepts jointly from different data sets with mutual consistency where we do not have samples from the full joint space? We present a novel answer in this paper based on cyclic consistency over multiple concepts, represented individually by generative adversarial networks (GANs). Our method, ConceptGAN, can be understood as a drop in for data augmentation to improve resilience for real world applications. Qualitative and quantitative evaluations demonstrate its efficacy in generating semantically meaningful images, as well as one shot face verification as an example application.Comment: 10 pages, 8 figures, 4 tables, CVPR 201