A deep representation for depth images from synthetic data

Convolutional Neural Networks (CNNs) trained on large scale RGB databases have become the secret sauce in the majority of recent approaches for object categorization from RGB-D data. Thanks to colorization techniques, these methods exploit the filters learned from 2D images to extract meaningful representations in 2.5D. Still, the perceptual signature of these two kind of images is very different, with the first usually strongly characterized by textures, and the second mostly by silhouettes of objects. Ideally, one would like to have two CNNs, one for RGB and one for depth, each trained on a suitable data collection, able to capture the perceptual properties of each channel for the task at hand. This has not been possible so far, due to the lack of a suitable depth database. This paper addresses this issue, proposing to opt for synthetically generated images rather than collecting by hand a 2.5D large scale database. While being clearly a proxy for real data, synthetic images allow to trade quality for quantity, making it possible to generate a virtually infinite amount of data. We show that the filters learned from such data collection, using the very same architecture typically used on visual data, learns very different filters, resulting in depth features (a) able to better characterize the different facets of depth images, and (b) complementary with respect to those derived from CNNs pre-trained on 2D datasets. Experiments on two publicly available databases show the power of our approach

From source to target and back: symmetric bi-directional adaptive GAN

The effectiveness of generative adversarial approaches in producing images according to a specific style or visual domain has recently opened new directions to solve the unsupervised domain adaptation problem. It has been shown that source labeled images can be modified to mimic target samples making it possible to train directly a classifier in the target domain, despite the original lack of annotated data. Inverse mappings from the target to the source domain have also been evaluated but only passing through adapted feature spaces, thus without new image generation. In this paper we propose to better exploit the potential of generative adversarial networks for adaptation by introducing a novel symmetric mapping among domains. We jointly optimize bi-directional image transformations combining them with target self-labeling. Moreover we define a new class consistency loss that aligns the generators in the two directions imposing to conserve the class identity of an image passing through both domain mappings. A detailed qualitative and quantitative analysis of the reconstructed images confirm the power of our approach. By integrating the two domain specific classifiers obtained with our bi-directional network we exceed previous state-of-the-art unsupervised adaptation results on four different benchmark datasets

SwimmerNET: Underwater 2D Swimmer Pose Estimation Exploiting Fully Convolutional Neural Networks

Professional swimming coaches make use of videos to evaluate their athletes' performances. Specifically, the videos are manually analyzed in order to observe the movements of all parts of the swimmer's body during the exercise and to give indications for improving swimming technique. This operation is time-consuming, laborious and error prone. In recent years, alternative technologies have been introduced in the literature, but they still have severe limitations that make their correct and effective use impossible. In fact, the currently available techniques based on image analysis only apply to certain swimming styles; moreover, they are strongly influenced by disturbing elements (i.e., the presence of bubbles, splashes and reflections), resulting in poor measurement accuracy. The use of wearable sensors (accelerometers or photoplethysmographic sensors) or optical markers, although they can guarantee high reliability and accuracy, disturb the performance of the athletes, who tend to dislike these solutions. In this work we introduce swimmerNET, a new marker-less 2D swimmer pose estimation approach based on the combined use of computer vision algorithms and fully convolutional neural networks. By using a single 8 Mpixel wide-angle camera, the proposed system is able to estimate the pose of a swimmer during exercise while guaranteeing adequate measurement accuracy. The method has been successfully tested on several athletes (i.e., different physical characteristics and different swimming technique), obtaining an average error and a standard deviation (worst case scenario for the dataset analyzed) of approximately 1 mm and 10 mm, respectively

Nuclear DNA contents, rDNAs, and karyotype evolution in subgenus Vicia: III. The heterogeneous section Hypechusa.

Abstract: Nuclear DNA contents, automated karyotype analyses, and sequences of internal transcribed spacers from ribosomal genes have been determined in the species belonging to section Hypechusa of the sub-genus Vicia. Karyomorphological results and phylogenetic data generated from the comparison of rDNA ( genes coding for rRNA) sequences showed that sect. Hypechusa is not monophyletic; however, some monophyletic units are apparent ( one including Vicia galeata, V. hyrcanica, V. noeana, and V. tigridis, another including V. assyriaca, V. hybrida, V. melanops, V. mollis, and V. sericocarpa), which partly correspond to morphology-based infrasectional groups. The relationships among these species and the species in sections Faba, Narbonensis, Bithynicae, and Peregrinae have been also investigated. Nuclear DNA contents, automated karyotype analyses, and sequences of internal transcribed spacers from ribosomal genes have been determined in the species belonging to section Hypechusa of the subgenus Vicia. Karyomorphological results and phylogenetic data generated from the comparison of rDNA (genes coding for rRNA) sequences showed that sect. Hypechusa is not monophyletic; however, some monophyletic units are apparent (one including Vicia galeata, V. hyrcanica, V. noeana, and V. tigridis, another including V. assyriaca, V. hybrida, V. melanops, V. mollis, and V. sericocarpa), which partly correspond to morphology-based infrasectional groups. The relationships among these species and the species in sections Faba, Narbonensis, Bithynicae, and Peregrinae have been also investigated

Molecular phylogenetics of Dipsacaceae reveals parallel trends in seed dispersal syndromes

Phylogenetic relationships among 17 taxa of Dipsacaceae were inferred from nucleotide sequence variation in both the internal transcribed spacer (ITS) regions of nuclear ribosomal DNA and the chloroplast trnL (UAA) intron sequences. The combined phylogenetic analysis, carried out by using two taxa from Valerianaceae as an outgroup yielded a single most parsimonious tree, in which Dipsacaceae are divided into two major clades: one including Lomelosia and Pycnocomon, both in a sister group relationship with a clade containing Pterocephalus, Scabiosa and Sixalix; the other including Pseudoscabiosa, Succisa and Succisella is sister group to Knautia, Pterocephalidium, Dipsacus and Cephalaria. The results obtained here greatly differ from previous ones based on classical morphology, but are congruent with recent findings on epicalyx differentiation and with pollen characters. In particular, our results would confirm on molecular grounds the recently restricted circumscription for Scabioseae proposed by other authors. Our phylogenetic hypothesis indicates that adaptations to seed dispersal have been a very strong driving force in Dipsacaceae evolution, with similar selective pressures causing the onset of similar epicalyx shapes and dispersal modes in a parallel fashion in various taxa. For this reason, the gross morphology of the involucel is deceptive in inferring relationships

Leveraging over depth in egocentric activity recognition

Activity recognition from first person videos is a growing research area. The increasing diffusion of egocentric sensors in various devices makes it timely to develop approaches able to recognize fine grained first person actions like picking up, putting down, pouring and so forth. While most of previous work focused on RGB data, some authors pointed out the importance of leveraging over depth information in this domain. In this paper we follow this trend and we propose the first deep architecture that uses depth maps as an attention mechanism for first person activity recognition. Specifically, we blend together the RGB and depth data, so to obtain an enriched input for the network. This blending puts more or less emphasis on different parts of the image based on their distance from the observer, hence acting as an attention mechanism. To further strengthen the proposed activity recognition protocol, we opt for a self labeling approach. This, combined with a Conv-LSTM block for extracting temporal information from the various frames, leads to the new state of the art on two publicly available benchmark databases. An ablation study completes our experimental findings, confirming the effectiveness of our approac

Convex Entropy Decay via the Bochner-Bakry-Emery approach

We develop a method, based on a Bochner-type identity, to obtain estimates on the exponential rate of decay of the relative entropy from equilibrium of Markov processes in discrete settings. When this method applies the relative entropy decays in a convex way. The method is shown to be rather powerful when applied to a class of birth and death processes. We then consider other examples, including inhomogeneous zero-range processes and Bernoulli-Laplace models. For these two models, known results were limited to the homogeneous case, and obtained via the martingale approach, whose applicability to inhomogeneous models is still unclear

Plasma-photon interaction in curved spacetime I: formalism and quasibound states around nonspinning black holes

We investigate the linear dynamics of an electromagnetic field propagating in curved spacetime in the presence of plasma. The dynamical equations are generically more involved and richer than the effective Proca equation adopted as a model in previous work. We discuss the general equations and focus on the case of a cold plasma in the background of a spherically-symmetric black hole, showing that the system admits plasma-driven, quasibound electromagnetic states that are prone to become superradiantly unstable when the black hole rotates. The quasibound states are different from those of the Proca equation and have some similarities with the case of a massive scalar field, suggesting that the linear instability can be strongly suppressed compared to previous estimates. Our framework provides the first step towards a full understanding of the plasma-photon interactions around astrophysical black holes.Comment: 11 pages, 4 figure

Numerical investigation on the residual stresses in welded T-joints made of dissimilar materials

Abstract This study used the Finite Element (FE) method to numerically analyze the thermo-mechanical behavior and residual stresses in dissimilar welded T-joints. Residual stresses induced by the fusion arc-welding of steel joints in power generation plants are a concern to the industry. The structural integrity assessment of welded structures requires the consideration of weld-induced residual stresses for the safe operations in power plants, which may be compromised by their presence. Details on the used thermo-mechanical FE model and the results analysis are herein presented