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 an optimization 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. By leveraging on proximal methods, our algorithm jointly minimizes the reconstruction error of the dictionary and the coding error of its dual; the sparsity of the representation is induced by an $\ell_1$-based penalty on its coefficients. The results obtained on synthetic data and real images show that the algorithm is capable of recovering the expected dictionaries. Furthermore, on a benchmark dataset, we show that the image features obtained from the dual matrix yield state-of-the-art classification performance while being much less computational intensive

Artificial Intelligence Application to Screen Abdominal Aortic Aneurysm Using Computed tomography Angiography

The aim of our study is to validate a totally automated deep learning (DL)-based segmentation pipeline to screen abdominal aortic aneurysms (AAA) in computed tomography angiography (CTA) scans. We retrospectively evaluated 73 thoraco-abdominal CTAs (48 AAA and 25 control CTA) by means of a DL-based segmentation pipeline built on a 2.5D convolutional neural network (CNN) architecture to segment lumen and thrombus of the aorta. The maximum aortic diameter of the abdominal tract was compared using a threshold value (30 mm). Blinded manual measurements from a radiologist were done in order to create a true comparison. The screening pipeline was tested on 48 patients with aneurysm and 25 without aneurysm. The average diameter manually measured was 51.1 ± 14.4 mm for patients with aneurysms and 21.7 ± 3.6 mm for patients without aneurysms. The pipeline correctly classified 47 AAA out of 48 and 24 control patients out of 25 with 97% accuracy, 98% sensitivity, and 96% specificity. The automated pipeline of aneurysm measurements in the abdominal tract reported a median error with regard to the maximum abdominal diameter measurement of 1.3 mm. Our approach allowed for the maximum diameter of 51.2 ± 14.3 mm in patients with aneurysm and 22.0 ± 4.0 mm in patients without an aneurysm. The DL-based screening for AAA is a feasible and accurate method, calling for further validation using a larger pool of diagnostic images towards its clinical use

Separation of identity and expression information in 3D scans of human faces

Our work is motivated by the problem of automatic face recognition, a difficult task, still missing a general solution. Its complexity lies in the wide range of variations presents in the input data, due to different lightings, background scenes and head positions. Moreover, the face appearance is affected by internal sources of variations: on a long temporal scale aging and weight gain, and on a short scale the action of the facial muscles. An effective recognition algorithm should be insensitive to all these sources of variations. During the last decade, good results to the recognition problem have been obtained using 3D Morphable Models (3DMMs). Their use allowed to separate the data variations due to the identity from the ones due to external sources like the lighting conditions. However, other internal sources were not considered. Our goal is to include expressions as an additional source of internal variation in 3DMMs, enabling us to recognize faces not only under different illuminations and pose conditions, but also with different expressions. In general, the construction of a 3DMM requires a corpus of training data; for our task we need a training set including examples of both identity and expression variations. Unfortunately, their acquisition alone is not sufficient, since they have to be previously registered with a reference 3D head model. The registration of 3D scans of expressions is a difficult problem, which could not be solved with the registration algorithm previously used. The main contribution of our work is a new registration algorithm which can cope with arbitrary expressions in the 3D data. Our algorithm is also capable of registering data with missing values, an important property since virtually no 3D acquisition devices is immune to holes and artifacts in the output. Given the training set of registered 3D examples, we construct a 3DMM where identity and expression variations are represented with two separate linear Gaussian models. The two models are then linearly combined, yielding an expression-identity 3DMM which we apply to the problem of 3D face recognition. Although this modeling approach does not take into account the interdependency between expressions and identity, the recognition performance is not negatively affected

Fitting 3D morphable models using implicit representations

We consider the problem of approximating the 3D scan of a real object through an affine combination of examples. Common approaches depend either on the explicit estimation of point-to-point correspondences or on 2-dimensional projections of the target mesh; both present drawbacks. We follow an approach similar to [IF03] by representing the target via an implicit function, whose values at the vertices of the approximation are used to define a robust cost function. The problem is approached in two steps, by approximating first a coarse implicit representation of the whole target, and then finer, local ones; the local approximations are then merged together with a Poisson-based method. We report the results of applying our method on a subset of 3D scans from the Face Recognition Grand Challenge v.1.0

Regularized 3D Morphable Models

Three-dimensional morphable models of object classes are a powerful tool in modeling, animation and recognition. We introduce here 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 ones obtained by a traditional morphable model on the same data set. 1