3D pose estimation from a single image is a challenging task in computer vision. We present a weakly supervised approach to estimate 3D pose points, given only 2D pose landmarks. Our method does not require correspondences between 2D and 3D points to build explicit 3D priors. We utilize an adversarial framework to impose a prior on the 3D structure, learned solely from their random 2D projections. Given a set of 2D pose landmarks, the generator network hypothesizes their depths to obtain a 3D skeleton. We propose a novel Random Projection layer, which randomly projects the generated 3D skeleton and sends the resulting 2D pose to the discriminator. The discriminator improves by discriminating between the generated poses and pose samples from a real distribution of 2D poses. Training does not require correspondence between the 2D inputs to either the generator or the discriminator. We apply our approach to the task of 3D human pose estimation. Results on Human3.6M dataset demonstrates that our approach outperforms many previous supervised and weakly supervised approaches.

In this paper, we address the problem of photometric invariance in multispectral imaging making use of an optimisation approach based upon the dichromatic model. In this manner, we cast the problem of recovering the spectra of the illuminant, the surface reflectance and the shading and specular factors in a structural optimisation setting. Making use of the additional information provided by multispectral imaging and the structure of image patches, we recover the dichromatic parameters of the scene. To do this, we formulate a target cost function combining the dichromatic error and the smoothness priors for the surfaces under study.The dichromatic parameters are recovered through minimising this cost function in a coordinate descent manner. The algorithm is quite general in nature, admitting the enforcement of smoothness constraints and extending in a straightforward manner to trichromatic settings.Moreover, the objective function is convex with respect to the subset of variables to be optimised in each alternating step of the minimisation strategy. This gives rise to an optimal closed-form solution for each of the iterations in our algorithm. We illustrate the effectiveness of our method for purposes of illuminant spectrum recovery, skin recognition, material clustering and specularity removal. We also compare our results to a number of alternatives.

In this paper, we propose an approach to the problem of simultaneous shape and refractive index recovery from multispectral polarisation imagery captured from a single viewpoint. The focus of this paper is on dielectric surfaces which diffusely polarise light transmitted from the dielectric body into the air. The diffuse polarisation of the reflection process is modelled using a Transmitted Radiance Sinusoid curve and the Fresnel transmission theory. We provide a method of estimating the azimuth angle of surface normals from the spectral variation of the phase of polarisation. Moreover, to render the problem of simultaneous estimation of surface orientation and index of refraction well-posed, we enforce a generative model on the material dispersion equations for the index of refraction. This generative model, together with the Fresnel transmission ratio, permit the recovery of the index of refraction and the zenith angle simultaneously. We show results on shape recovery and rendering for real world and synthetic imagery.

Radio connectivity in wireless sensor networks is highly intermittent due to unpredictable and time-varying noise and interference patterns in the environment. Because link qualities are not predictable prior to deployment, current deterministic solutions to unreliable links, such as increasing network density or transmission power, require overprovisioning of network resources and do not always improve reliability.We propose a new dual-radio network architecture to improve communication reliability in wireless sensor networks. Specifically, we show that radio transceivers operating at well-separated frequencies and spatially separated antennas offer robust communication, high link diversity, and better interference mitigation. We derive the optimal parameters for the dual-transceiver setup from frequency and space diversity in theory. We observe that frequency diversity holds the most benefits as long as the antennas are sufficiently separated to prevent coupling. Our experiments on an indoor/outdoor testbed confirm the theoretical predictions and show that radio diversity can significantly improve end-to-end delivery rates and network stability at only a small increase in energy cost over a single radio. Simulation experiments further validate the improvements in multiple topology configurations, but also reveal that the benefits of radio diversity are coupled to the number of available routing paths to the destination.

We present a novel image denoising algorithm that uses external, category specific image database. In contrast to existing noisy image restoration algorithms that search patches either from a generic database or noisy image itself, our method first selects clean images similar to the noisy image from a database that consists of images of the same class. Then, within the spatial locality of each noisy patch, it assembles a set of "support patches" from the selected images. These noisy-free support samples resemble the noisy patch and correspond principally to the identical part of the depicted object. In addition, we employ a content adaptive distribution model for each patch, where we derive the parameters of the distribution from the support patches. We formulate noise removal task as an optimization problem in the transform domain. Our objective function composed of a Gaussian fidelity term that imposes category specific information, and a low-rank term that encourages the similarity between the noisy and the support patches in a robust manner. The denoising process is driven by an iterative selection of support patches and optimization of the objective function. Our extensive experiments on five different object categories confirm the benefit of incorporating category-specific information to noise removal and demonstrate the superior performance of our method over the state-of-the-art alternatives.