Curzio Basso

Reanimating Faces in Images and Video

This paper presents a method for photo‐realistic animation that can be applied to any face shown in a single imageor a video. The technique does not require example data of the persons mouth movements, and the image to beanimated is not restricted in pose or illumination. Video reanimation allows for head rotations and speech in theoriginal sequence, but neither of these motions is required.

Dynamic contrast-enhanced magnetic resonance imaging in the assessment of disease activity in patients with juvenile idiopathic arthritis

OBJECTIVE To determine the capability and reliability of dynamic contrast-enhanced MRI (DCE-MRI) in the assessment of disease activity in juvenile idiopathic arthritis (JIA). METHODS DCE-MRI of the clinically more affected wrist or hip joints was undertaken in 21 patients, coupled with standard clinical assessment and biochemical analysis. Synovial inflammation was assessed by computing the maximum level of synovial enhancement (ME), the maximum rate of enhancement (MV) and the rate of early enhancement (REE) from the enhancement curves generated from region of interest independently delineated by two readers in the area of the ME. Correlations between dynamic parameters and clinical measures of disease activity, and static MRI synovitis score were investigated. RESULTS In patients with wrist arthritis, REE correlated with the wrist swelling score (r(s) = 0.72), ESR (r(s) = 0.69), pain assessment scale (r(s) = 0.63) and childhood HAQ (r(s) = 0.60). In patients with hip arthritis, ME correlated with the hip limitation of motion (r(s) = 0.69). Static MRI synovitis score based on post-gadolinium enhancement correlated with MV (r(s) = 0.63) in patients with wrist arthritis and with ME (r = 0.68) in those with hip arthritis. The inter-reader agreement assessed by intra-class correlation coefficient (ICC) for ME, MV and REE (ICC = 0.98, 0.97 and 0.84, respectively) was excellent. CONCLUSIONS DCE-MRI represents a promising method for the assessment of disease activity in JIA, especially in patients with wrist arthritis. As far as we know, this study is the first to demonstrate the feasibility, reliability and construct validity of DCE-MRI in JIA. These results should be confirmed in large-scale longitudinal studies in view of its further application in therapeutic decision making and in clinical trials.

Registration of Expressions Data using a 3D Morphable Model

The registration of 3D scans of faces is a key step for many applications, in particular for building 3D Morphable Models. Although a number of algorithms are already available for registering data with neutral expression, the registration of scans with arbitrary expressions is typically performed under the assumption of a known, fixed identity. We present a novel algorithm which breaks this restriction, allowing to register 3D scans of faces with arbitrary identity and expression. Furthermore, our algorithm can process incomplete data, yielding results which are both continuous and with low reconstruction error. Even in the case of complete, expression-less data, our method can yield better results than previous algorithms, due to an adaptive smoothing, which regularizes the results surface only where the estimated correspondence is unreliable.

Representing and recognizing visual dynamic events with support vector machines

Support vector machines (SVM) have been recently introduced as techniques for solving pattern recognition and regression estimation problems. SVM are derived within the framework of statistical learning theory and combine a solid theoretical foundation with very good performances in several applications. In this paper we describe a system able to detect, represent, and recognize visual dynamic events from an image sequence. While the events are initially detected by means of low-level visual processing, both the representation and recognition stages are performed with SVM. Therefore, the system is trained, instead of programmed, to perform the required tasks. The very good results obtained on real image sequences indicate that SVM can be profitably used for the construction of flexible and effective systems based on computer vision.

Regularized 3D morphable models

Three-dimensional morphable models of objects classes are a powerful tool in modeling, animation and recognition. We introduce the new concept of regularized 3D morphable models, along with an iterative learning algorithm, by adding in the statistical model a noise/regularization term which is estimated from the examples set. With regularized 3D morphable models we are able to handle missing information, as it often occurs with data obtained by 3D acquisition systems; additionally, the new models are less complex than, but as powerful as the non-regularized ones. We present the results obtained for a set of 3D face models and a comparison with the new ones obtained by a traditional morphable model on the same data set.

Registration of expressions data using a 3D morphable model

The registration of 3D scans of faces is a key step for many applications, in particular for building 3D morphable models. Although a number of algorithms are already available for registering data with neutral expression, the registration of scans with arbitrary expressions is typically performed under the assumption of a known, fixed identity. We present a novel algorithm which breaks this restriction, allowing to register 3D scans of faces with arbitrary identity and expression. Furthermore, our algorithm can process incomplete data, yielding results which are both continuous and with low reconstruction error. Even in the case of complete, expression-less data, our method can yield better results than previous algorithms, due to an adaptive smoothing, which regularizes the results surface only where the estimated correspondence is unreliable

PADDLE: proximal algorithm for dual dictionaries learning

Recently, considerable research efforts have been devoted to the design of methods to learn from data overcomplete dictionaries for sparse coding. However, learned dictionaries require the solution of a sparse approximation problem for coding new data. In order to overcome this drawback, we propose an algorithm aimed at learning both a dictionary and its dual: a linear mapping directly performing the coding. Our algorithm is based on proximal methods and jointly minimizes the reconstruction error of the dictionary and the coding error of its dual; the sparsity of the representation is induced by an l1-based penalty on its coefficients. Experimental results show that the algorithm is capable of recovering the expected dictionaries. Furthermore, on a benchmark dataset the image features obtained from the dual matrix yield state-of-the-art classification performance while being much less computational intensive.

Learning adaptive and sparse representations of medical images

In this paper we discuss the impact of using algorithms for dictionary learning to build adaptive and sparse representations of medical images. The effectiveness of coding data as sparse linear combinations of the elements of an over-complete dictionary is well assessed in the medical context. Confirming what has been observed for natural images, we show the benefits of using adaptive dictionaries, directly learned from a set of training images, that better capture the distribution of the data. The experiments focus on the specific task of image denoising and produce clear evidence of the benefits obtained with the proposed approach.

DCE-MRI analysis using sparse adaptive representations

Dynamic contrast-enhanced MRI (DCE-MRI) plays an important role as an imaging method for the diagnosis and evaluation of several diseases. Indeed, clinically relevant, per-voxel quantitative information may be extracted through the analysis of the enhanced MR signal. This paper presents a method for the automated analysis of DCE-MRI data that works by decomposing the enhancement curves as sparse linear combinations of elementary curves learned without supervision from the data. Experimental results show that performances in denoising and unsupervised segmentation improve over parametric methods.

Unsupervised tissue segmentation from dynamic contrast-enhanced magnetic resonance imaging

OBJECTIVE Design, implement, and validate an unsupervised method for tissue segmentation from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). METHODS For each DCE-MRI acquisition, after a spatial registration phase, the time-varying intensity of each voxel is represented as a sparse linear combination of adaptive basis signals. Both the basis signals and the sparse coefficients are learned by minimizing a functional consisting of a data fidelity term and a sparsity inducing penalty. Tissue segmentation is then obtained by applying a standard clustering algorithm to the computed representation. RESULTS Quantitative estimates on two real data sets are presented. In the first case, the overlap with expert annotation measured with the DICE metric is nearly 90% and thus 5% more accurate than state-of-the-art techniques. In the second case, assessment of the correlation between quantitative scores, obtained by the proposed method against imagery manually annotated by two experts, achieved a Pearson coefficient of 0.83 and 0.87, and a Spearman coefficient of 0.83 and 0.71, respectively. CONCLUSIONS The sparse representation of DCE MRI signals obtained by means of adaptive dictionary learning techniques appears to be well-suited for unsupervised tissue segmentation and applicable to different clinical contexts with little effort.