A Robust Group-Sparse Representation Variational Method with applications to Face Recognition.

In this paper we propose a Group-Sparse Representation based method with applications to Face Recognition (GSR-FR). The novel sparse representation variational model includes a non-convex sparsity-inducing penalty and a robust non-convex loss function. The penalty encourages group sparsity by using approximation of the ℓ0-quasinorm, and the loss function is chosen to make the algorithm robust to noise, occlusions and disguises.

Image enhancement variational methods for enabling strong cost reduction in OLED-based point-of-care immunofluorescent diagnostic systems.

Immunofluorescence diagnostic systems cost is often dominated by high-sensitivity, low-noise CCD-based cameras that are used to acquire the fluorescence images. In this paper, we investigate the use of low-cost CMOS sensors in a point-of-care immunofluorescence diagnostic application for the detection and discrimination of 4 different serotypes of the Dengue virus in a set of human samples. A 2-phase postprocessing software pipeline is proposed, which consists in a first image-enhancement stage for resolution increasing and segmentation and a second diagnosis stage for the computation of the output concentrations.

3D long bone reconstruction based on level sets.

In medical imaging a three-dimensional (3D) object must often be reconstructed from serial cross-sections to aid in the comprehension of the object's structure as well as to facilitate its automatic manipulation and analysis. The most popular interpolation scheme for a sequence of image slices is the shape-based method, where object information extracted from a given 3D volume image is used in guiding the interpolation process. The paper presents a level set reformulation of the well-known shape-based method as well as a new automatic level set method, which offers better performance.