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Automatic face analysis, including head pose estimation, gender recognition, and expression classification, strongly benefits from an accurate segmentation of the human face. In this paper we present a multi-feature framework which first segments a face image into six parts, and then performs classification tasks on head pose, gender, and expression. Segmentation is achieved by training a discriminative model on a manually labeled face database, namely FASSEG , which we extend from previous versions, and which we publicly share. Three kinds of features accounting for location, shape, and color are extracted from uniformly sampled square image patches. Facial images are then pixel-wise segmented into six semantic classes – hair, skin, nose, eyes, mouth, and background, – using a Random Forest classifier (RF). Then a linear Support Vector Machine (SVM) is trained for each face analysis task i.e., head pose estimation, gender recognition, and expression classification by using the probability maps obtained during the segmentation step. Performance of the proposed framework is evaluated on four face databases, namely Pointing’04, FEI, FERET, and MPI, with results which outperform the current state-of-the-art.

Graphical abstract, In this work we design an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients. Such semi-quantitative scoring system, namely Brixia score, is applied in serial monitoring of such patients, showing significant prognostic value, in one of the hospitals that experienced one of the highest pandemic peaks in Italy. To solve such a challenging visual task, we adopt a weakly supervised learning strategy structured to handle different tasks (segmentation, spatial alignment, and score estimation) trained with a “from-the-part-to-the-whole” procedure involving different datasets. In particular, we exploit a clinical dataset of almost 5,000 CXR annotated images collected in the same hospital. Our BS-Net demonstrates self-attentive behavior and a high degree of accuracy in all processing stages. Through inter-rater agreement tests and a gold standard comparison, we show that our solution outperforms single human annotators in rating accuracy and consistency, thus supporting the possibility of using this tool in contexts of computer-assisted monitoring. Highly resolved (super-pixel level) explainability maps are also generated, with an original technique, to visually help the understanding of the network activity on the lung areas. We also consider other scores proposed in literature and provide a comparison with a recently proposed non-specific approach. We eventually test the performance robustness of our model on an assorted public COVID-19 dataset, for which we also provide Brixia score annotations, observing good direct generalization and fine-tuning capabilities that highlight the portability of BS-Net in other clinical settings. The CXR dataset along with the source code and the trained model are publicly released for research purposes.

The very fundamental operation of even/odd decomposition is at the core of some of the simplest information representation and signal processing tasks. So far most of its use has been for rearranging data to provide fast implementations of various types of transforms (Fourier, DCT, ...) or for achieving elementary data transformation, such as the Walsh–Hadamard transform. This work proposes to look into the decomposition framework to obtain a richer perspective. In the context of an iterated even/odd decomposition, it is possible to pinpoint intermediate layered levels of symmetries which cannot be easily captured in the original data. In addition this determines a hierarchical fingerprinting for any sort of continuous finite support analog signal or for any discrete-time sequence which may turn out useful in several recognition or categorization tasks. It also may help to achieve sparsity within a natural hierarchical framework, which could be easily extended for many other types of orthogonal transformations. This paper also suggests a global measure of the energy imbalance across the hierarchy of the decomposition to capture the overall fingerprinting of this interpretation.

The authors highlight a new perspective to understanding the volcanism in the Mt. Etna eastern sector, inland and offshore, based on original studies of the sea floor off the Ionian coast of Etna by means of various direct surveying methods (underwater explorations) and indirect ones (bathymetric reconstructions using echosounders). They also propose a new interpretation of geophysical, geochemical and structural surveys carried out over the last two decades. Results show that eastern Etnean sector's volcanism extends as far as the Ionian Sea, to a maximum distance from the coast of probably about 20 km. In their opinion, the absence of outcropping apparatuses in the lower eastern flank of Etna is due to these apparatuses being buried by a large detritic formation (“Chiancone”) due to the dismantling of the Ancient Alkaline Centres (AAC) localised to the West. The authors consider the structures highlighted by the study of the Digital Elevation Model (DEM) of the Ionian sea floor and by the Multibeam analysis (Marani et al., 2004. From Seafloor to Deep Mantle: Architecture of the Tyrrhenian Backarc Basin, vol. 44. Mem. Descr. Carta Geol. Ital. pp. 1–2) to be of a volcanic nature. This hypothesis opens up a new field of study within the evolution of the eastern Etnean edge's volcanism, inland and offshore, in the last 500 Ky and would further confirm the eruptive axes migration from East to West.

The reconstruction error due to quantization of wavelet subbands can be modeled as a cyclostationary process because of the linear periodically shift variant property of the inverse wavelet transform. For N-dimensional data, N-dimensional reconstruction error power cyclostationary patterns replicate on the data sample lattice. For audio and image coding applications this fact is of little practical interest since the decoded data is perceived in its wholeness, the error power oscillations on single data elements cannot be seen or heard and a global PSNR error measure is often used to represent the reconstruction quality. A different situation is the one of 3D data (static volumes or video sequences) coding, where decoded data are usually visualized by plane sections and the reconstruction error power is commonly measured by a P SNR [ n ] sequence, with n representing either a spatial slicing plane (for volumetric data) or the temporal reference frame (for video). In this case, the cyclostationary oscillations on single data elements lead to a global P SNR [ n ] oscillation and this effect may become a relevant concern. In this paper we study and describe the above phenomena and evaluate their relevance in concrete coding applications. Our analysis is entirely carried out in the original signal domain and can easily be extended to more than three dimensions. We associate the oscillation pattern with the wavelet filter properties in a polyphase framework and we show that a substantial reduction of the oscillation amplitudes can be achieved under a proper selection of the basis functions. Our quantitative model is initially made under high-resolution conditions and then qualitatively extended to all coding rates for the wide family of bit-plane quantization-based coding techniques. Finally, we experimentally validate the proposed models and we perform a subjective evaluation of the visual relevance of the P SNR [ n ] fluctuations in the cases of medical volumes and video coding.

Keywords: LTS1 Reference LCAV-ARTICLE-1991-004doi:10.1016/1047-3203(91)90023-9 Record created on 2005-04-18, modified on 2017-05-12

This paper presents an approximation algorithm for two-dimensional signals (e.g., images) using polynomial functions. The proposed algorithm is based on an adaptive segmentation of the original signal into adjacent regions and on the approximation of the signal in each region by a two-dimensional polynomial function. The segmentation is obtained by an adaptive region growing technique which allows perfect adaptation between the chosen approximation and the inner structure of the signal. Results of this technique are presented in the context of image coding applications.