The Cramer-Rao error bound provides a fundamental limit on the expected performance of a statistical estimator. The error bound depends on the general properties of the system, but not on the specific properties of the estimator or the solution. The Cramer-Rao error bound has been applied to scalarand vector-valued estimators and recently to parametric shape estimators. However, nonparametric, ...

PURPOSE Acceleration of magnetic resonance (MR) thermometry is desirable for several applications of MR-guided focused ultrasound, such as those requiring greater volume coverage, higher spatial resolution, or higher frame rates. METHODS We propose and validate a constrained reconstruction method that estimates focal temperature changes directly from k-space without spatial or temporal regula...

In X-ray computed tomography (CT) an important objective is to reduce the radiation dose without significantly degrading the image quality. Compressed sensing (CS) enables the radiation dose to be reduced by producing diagnostic images from a limited number of projections. However, conventional CS-based algorithms are computationally intensive and time-consuming. We propose a new algorithm that...

We describe a novel reconstruction algorithm based on Principal Component Analysis (PCA) applied to multi-spectral imaging data. Using numerical phantoms, based on a two layered skin model developed previously, we found analytical expressions, which convert qualitative PCA results into quantitative blood volume and oxygenation values, assuming the epidermal thickness to be known. We also evalua...

3D facial analysis attracts much interest recently due to the fact that it provides solutions for mitigating confounding factors in 2D image analysis, such as pose, illumination. On the other hand, it also provides enriched representation with more discriminative depth information for applications such as expression or identity analysis. In this paper, we investigate 3D face reconstruction base...

This paper focuses on the unsupervised domain adaptation problem for video-based crowd counting, in which we use labeled data as source and unlabelled video target domain. It is challenging there a huge gap between no annotations of samples are available The key issue how to utilize videos knowledge learning transferring from To tackle this problem, propose novel Error-aware Density Isomorphism...