Unsupervised Semantic Discovery Through Visual Patterns Detection

We propose a new fast fully unsupervised method to discover semantic patterns. Our algorithm is able to hierarchically find visual categories and produce a segmentation mask where previous methods fail. Through the modeling of what is a visual pattern in an image, we introduce the notion of “semantic levels" and devise a conceptual framework along with measures and a dedicated benchmark dataset for future comparisons. Our algorithm is composed by two phases. A filtering phase, which selects semantical hotsposts by means of an accumulator space, then a clustering phase which propagates the semantic properties of the hotspots on a superpixels basis. We provide both qualitative and quantitative experimental validation, achieving optimal results in terms of robustness to noise and semantic consistency. We also made code and dataset publicly available

Cylinders extraction in non-oriented point clouds as a clustering problem

Finding geometric primitives in 3D point clouds is a fundamental task in many engineering applications such as robotics, autonomous-vehicles and automated industrial inspection. Among all solid shapes, cylinders are frequently found in a variety of scenes, comprising natural or man-made objects. Despite their ubiquitous presence, automated extraction and fitting can become challenging if performed ”in-the-wild”, when the number of primitives is unknown or the point cloud is noisy and not oriented. In this paper we pose the problem of extracting multiple cylinders in a scene by means of a Game-Theoretic inlier selection process exploiting the geometrical relations between pairs of axis candidates. First, we formulate the similarity between two possible cylinders considering the rigid motion aligning the two axes to the same line. This motion is represented with a unitary dual-quaternion so that the distance between two cylinders is induced by the length of the shortest geodesic path in SE(3). Then, a Game-Theoretical process exploits such similarity function to extract sets of primitives maximizing their inner mutual consensus. The outcome of the evolutionary process consists in a probability distribution over the sets of candidates (ie axes), which in turn is used to directly estimate the final cylinder parameters. An extensive experimental section shows that the proposed algorithm offers a high resilience to noise, since the process inherently discards inconsistent data. Compared to other methods, it does not need point normals and does not require a fine tuning of multiple parameters

One-Shot HDR Imaging via Stereo PFA Cameras

High Dynamic Range (HDR) imaging techniques aim to increase the range of luminance values captured from a scene. The literature counts many approaches to get HDR images out of low-range camera sensors, however most of them rely on multiple acquisitions producing ghosting effects when moving objects are present. In this paper we propose a novel HDR reconstruction method exploiting stereo Polarimetric Filter Array (PFA) cameras to simultaneously capture the scene with different polarized filters, producing intensity attenuations that can be related to the light polarization state. An additional linear polarizer is mounted in front of one of the two cameras, raising the degree of polarization of rays captured by the sensor. This leads to a larger attenuation range between channels regardless the scene lighting condition. By merging the data acquired by the two cameras, we can compute the actual light attenuation observed by a pixel at each channel and derive an equivalent exposure time, producing a HDR picture from a single polarimetric shot. The proposed technique results comparable to classic HDR approaches using multiple exposures, with the advantage of being a one-shot method

A stable graph-based representation for object recognition through high-order matching

Many Object recognition techniques perform some flavour of point pattern matching between a model and a scene. Such points are usually selected through a feature detection algorithm that is robust to a class of image transformations and a suitable descriptor is computed over them in order to get a reliable matching. Moreover, some approaches take an additional step by casting the correspondence problem into a matching between graphs defined over feature points. The motivation is that the relational model would add more discriminative power, however the overall effectiveness strongly depends on the ability to build a graph that is stable with respect to both changes in the object appearance and spatial distribution of interest points. In fact, widely used graph-based representations, have shown to suffer some limitations, especially with respect to changes in the Euclidean organization of the feature points. In this paper we introduce a technique to build relational structures over corner points that does not depend on the spatial distribution of the features

Mergers of double neutron stars with one high-spin component: brighter kilonovae and fallback accretion, weaker gravitational waves

Neutron star mergers where both stars have negligible spins are commonly considered as the most likely, "standard" case. But based on observed systems, we estimate that actually a non-negligible fraction of all double neutron star mergers ($\sim$ 5 %) may contain one millisecond component. We use the Lagrangian Numerical Relativity code SPHINCS_BSSN to simulate mergers where one star has no spin and the other has a dimensionless spin parameter of $\chi=0.5$. These mergers exhibit several distinct signatures compared to irrotational cases. Morphologically, they are similar to unequal mass mergers and they form in particular only one, very pronounced spiral arm. Compared to the non-spinning cases, they dynamically eject an order of magnitude more mass of unshocked material at the original low electron fraction of the neutron stars and therefore produce particularly bright, red kilonovae and brighter kilonova afterglows months after the merger. We also find that the spinning cases have significantly more fallback accretion, with implications for late-time X-ray flares and the duration of the associated gamma-ray burst. Overall, the spinning case collisions are substantially less violent and they emit smaller amounts of shock-generated semi-relativistic material and therefore produce less pronounced blue/UV kilonova precursor signals. Their post-merger gravitational wave signal is weaker and, during the simulated time, substantially smaller amounts of energy and angular momentum are emitted. Therefore the central remnant contains a larger angular momentum reservoir and could remain an "active engine" for a longer time.Comment: 17 pages, 15 figures, submitte

A physics-driven CNN model for real-time sea waves 3D reconstruction

One of the most promising techniques for the analysis of Spatio-Temporal ocean wave fields is stereo vision. Indeed, the reconstruction accuracy and resolution typically outperform other approaches like radars, satellites, etc. However, it is computationally expensive so its application is typically restricted to the analysis of short pre-recorded sequences. What prevents such methodology from being truly real-time is the final 3D surface estimation from a scattered, non-equispaced point cloud. Recently, we studied a novel approach exploiting the temporal dependence of subsequent frames to iteratively update the wave spectrum over time. Albeit substantially faster, the unpre-dictable convergence time of the optimization involved still prevents its usage as a continuously running remote sensing infrastructure. In this work, we build upon the same idea, but investigat-ing the feasibility of a fully data-driven Machine Learning (ML) approach. We designed a novel Convolutional Neural Network that learns how to produce an accurate surface from the scattered elevation data of three subsequent frames. The key idea is to embed the linear dispersion relation into the model itself to physically relate the sparse points observed at different times. Assuming that the scattered data are uniformly distributed in the spatial domain, this has the same effect of increasing the sample density of each single frame. Experiments demonstrate how the proposed technique, even if trained with purely synthetic data, can produce accurate and physically consistent surfaces at five frames per second on a modern PC

Collected world experience about the performance of the snorkel/chimney endovascular technique in the treatment of complex aortic pathologies: The PERICLES registry

Objectives: We sought to analyze the collected worldwide experience with use of snorkel/chimney endovascular aneurysm repair (EVAR) for complex abdominal aneurysm treatment. Background: EVAR has largely replaced open surgery worldwide for anatomically suitable aortic aneurysms. Lack of availability of fenestrated and branched devices has encouraged an alternative strategy utilizing parallel or snorkel/chimney grafts (ch-EVAR). Methods: Clinical and radiographic information was retrospectively reviewed and analyzed on 517 patients treated by ch-EVAR from 2008 from 2014 by prearranged defined and documented protocols. Results: A total of 119 patients in US centers and 398 in European centers were treated during the study period. US centers preferentially used Zenith stent-grafts (54.2%) and European centers Endurant stent-grafts (62.2%) for the main body component. Overall 898 chimney grafts (49.2% balloon expandable, 39.6% self-expanding covered stents, and 11.2% balloon expandable bare metal stents) were placed in 692 renal arteries, 156 superior mesenteric arteries (SMA), and 50 celiac arteries. At a mean follow-up of 17.1 months (range: 1-70 months), primary patency was 94%, with secondary patency of 95.3%. Overall survival of patients in this high-risk cohort for open repair at latest follow-up was 79%. Conclusions: This global experience represents the largest series in the ch-EVAR literature and demonstrates comparable outcomes to those in published reports of branched/fenestrated devices, suggesting the appropriateness of broader applicability and the need for continued careful surveillance. These results support ch-EVAR as a valid off-the-shelf and immediately available alternative in the treatment of complex abdominal EVAR and provide impetus for the standardization of these techniques in the future