Publications

In this paper the problem of Connected Components Labeling (CCL) in binary images using Graphic Processing Units (GPUs) is tackled by a different perspective. In the last decade, many novel algorithms have been released, specifically designed for GPUs. Because CCL literature concerning sequential algorithms is very rich, and includes many efficient solutions, designers of parallel algorithms were often inspired by techniques that had already proved successful in a sequential environment, such as the Union-Find paradigm for solving equivalences between provisional labels. However, the use of decision trees to minimize memory accesses, which is one of the main feature of the best performing sequential algorithms, was never taken into account when designing parallel CCL solutions. In fact, branches in the code tend to cause thread divergence, which usually leads to inefficiency. Anyway, this consideration does not necessarily apply to every possible scenario. Are we sure that the advantages of decision trees do not compensate for the cost of thread divergence? In order to answer this question, we chose three well-known sequential CCL algorithms, which employ decision trees as the cornerstone of their strategy, and we built a data-parallel version of each of them. Experimental tests on real case datasets show that, in most cases, these solutions outperform state-of-the-art algorithms, thus demonstrating the effectiveness of decision trees also in a parallel environment.

State-of-the-art Computer Vision pipelines show poor performances on artworks and data coming from the artistic domain, thus limiting the applicability of current architectures to the automatic understanding of the cultural heritage. This is mainly due to the difference in texture and low-level feature distribution between artistic and real images, on which state-of-the-art approaches are usually trained. To enhance the applicability of pre-trained architectures on artistic data, we have recently proposed an unpaired domain translation approach which can translate artworks to photo-realistic visualizations. Our approach leverages semantically-aware memory banks of real patches, which are used to drive the generation of the translated image while improving its realism. In this paper, we provide additional analyses and experimental results which demonstrate the effectiveness of our approach. In particular, we evaluate the quality of generated results in the case of the translation of landscapes, portraits and of paintings coming from four different styles using automatic distance metrics. Also, we analyze the response of pre-trained architecture for classification, detection and segmentation both in terms of feature distribution and entropy of prediction, and show that our approach effectively reduces the domain shift of paintings. As an additional contribution, we also provide a qualitative analysis of the reduction of the domain shift for detection, segmentation and image captioning.

In this paper we propose a strategy to optimize the performance of thinning algorithms. This solution is obtained by combining three proven strategies for binary images neighborhood exploration, namely modeling the problem with an optimal decision tree, reusing pixels from the previous step of the algorithm, and reducing the code footprint by means of Directed Rooted Acyclic Graphs. A complete and open-source benchmarking suite is also provided. Experimental results confirm that the proposed algorithms clearly outperform classical implementations.

Novelty detection is commonly referred to as the discrimination of observations that do not conform to a learned model of regularity. Despite its importance in different application settings, designing a novelty detector is utterly complex due to the unpredictable nature of novelties and its inaccessibility during the training procedure, factors which expose the unsupervised nature of the problem. In our proposal, we design a general framework where we equip a deep autoencoder with a parametric density estimator that learns the probability distribution underlying its latent representations through an autoregressive procedure. We show that a maximum likelihood objective, optimized in conjunction with the reconstruction of normal samples, effectively acts as a regularizer for the task at hand, by minimizing the differential entropy of the distribution spanned by latent vectors. In addition to providing a very general formulation, extensive experiments of our model on publicly available datasets deliver on-par or superior performances if compared to state-of-the-art methods in one-class and video anomaly detection settings. Differently from prior works, our proposal does not make any assumption about the nature of the novelties, making our work readily applicable to diverse contexts.

Current movie captioning architectures are not capable of mentioning characters with their proper name, replacing them with a generic "someone" tag. The lack of movie description datasets with characters' visual annotations surely plays a relevant role in this shortage. Recently, we proposed to extend the M-VAD dataset by introducing such information. In this paper, we present an improved version of the dataset, namely M-VAD Names, and its semi-automatic annotation procedure. The resulting dataset contains 63k visual tracks and 34k textual mentions, all associated with character identities. To showcase the features of the dataset and quantify the complexity of the naming task, we investigate multimodal architectures to replace the "someone" tags with proper character names in existing video captions. The evaluation is further extended by testing this application on videos outside of the M-VAD Names dataset.

Few works tackle the Human Pose Estimation on depth maps. Moreover, these methods usually rely on automatically annotated datasets, and these annotations are often imprecise and unreliable, limiting the achievable accuracy using this data as ground truth. For this reason, in this paper we propose an annotation refinement tool of human poses, by means of body joints, and a novel set of fine joint annotations for the Watch-n-Patch dataset, which has been collected with the proposed tool. Furthermore, we present a fully-convolutional architecture that performs the body pose estimation directly on depth maps. The extensive evaluation shows that the proposed architecture outperforms the competitors in different training scenarios and is able to run in real-time.

Sistema in grado di rilevare le lesioni delle mezzene al macello attraverso l'utilizzo di tecniche di deep learning per individuazioni del tipo di lesioni presenti

Approximately 18% of acute myeloid leukemia (AML) cases express a fusion transcript. However, few fusions are recurrent across AML and the identification of these rare chimeras is of interest to characterize AML patients. Here, we studied the transcriptome of 8 adult AML patients with poorly described chromosomal translocation(s), with the aim of identifying novel and rare fusion transcripts. We integrated RNA-sequencing data with multiple approaches including computational analysis, Sanger sequencing, fluorescence in situ hybridization and in vitro studies to assess the oncogenic potential of the ZEB2-BCL11B chimera. We detected 7 different fusions with partner genes involving transcription factors (OAZ-MAFK, ZEB2-BCL11B), tumor suppressors (SAV1-GYPB, PUF60-TYW1, CNOT2-WT1) and rearrangements associated with the loss of NF1 (CPD-PXT1, UTP6-CRLF3). Notably, ZEB2-BCL11B rearrangements co-occurred with FLT3 mutations and were associated with a poorly differentiated or mixed phenotype leukemia. Although the fusion alone did not transform murine c-Kit+ bone marrow cells, 45.4% of 14q32 non-rearranged AML cases were also BCL11B-positive, suggesting a more general and complex mechanism of leukemogenesis associated with BCL11B expression. Overall, by combining different approaches, we described rare fusion events contributing to the complexity of AML and we linked the expression of some chimeras to genomic alterations hitting known genes in AML.

We present OpenFACS, an open source FACS-based 3D face animation system. OpenFACS is a software that allows the simulation of realistic facial expressions through the manipulation of specific action units as defined in the Facial Action Coding System. OpenFACS has been developed together with an API which is suitable to generate real-time dynamic facial expressions for a three-dimensional character. It can be easily embedded in existing systems without any prior experience in computer graphics. In this note, we discuss the adopted face model, the implemented architecture and provide additional details of model dynamics. Finally, a validation experiment is proposed to assess the effectiveness of the model.

Wind turbine upgrades have recently been spreading in the wind energy industry for optimizing the efficiency of the wind kinetic energy conversion. These interventions have material and labor costs; therefore, it is fundamental to estimate the production improvement realistically. Furthermore, the retrofitting of the wind turbines sited in complex environments might exacerbate the stress conditions to which those are subjected and consequently might affect the residual life. In this work, a two-step upgrade on a multimegawatt wind turbine is considered from a wind farm sited in complex terrain. First, vortex generators and passive flow control devices have been installed. Second, the management of the revolutions per minute has been optimized. In this work, a general method is formulated for assessing the wind turbine power upgrades using operational data. The method is based on the study of the residuals between the measured power output and a judicious model of the power output itself, before and after the upgrade. Therefore, properly selecting the model is fundamental. For this reason, an automatic feature selection algorithm is adopted, based on the stepwise multivariate regression. This allows identifying the most meaningful input variables for a multivariate linear model whose target is the power of the upgraded wind turbine. For the test case of interest, the adopted upgrade is estimated to increase the annual energy production to 2.660.1%. The aerodynamic and control upgrades are estimated to be 1.8% and 0.8%, respectively, of the production improvement.