Point-wise mutual information-based video segmentation with high temporal consistency

In this paper, we tackle the problem of temporally consistent boundary detection and hierarchical segmentation in videos. While finding the best high-level reasoning of region assignments in videos is the focus of much recent research, temporal consistency in boundary detection has so far only rarely been tackled. We argue that temporally consistent boundaries are a key component to temporally consistent region assignment. The proposed method is based on the point-wise mutual information (PMI) of spatio-temporal voxels. Temporal consistency is established by an evaluation of PMI-based point affinities in the spectral domain over space and time. Thus, the proposed method is independent of any optical flow computation or previously learned motion models. The proposed low-level video segmentation method outperforms the learning-based state of the art in terms of standard region metrics

Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network

With more and more household objects built on planned obsolescence and consumed by a fast-growing population, hazardous waste recycling has become a critical challenge. Given the large variability of household waste, current recycling platforms mostly rely on human operators to analyze the scene, typically composed of many object instances piled up in bulk. Helping them by robotizing the unitary extraction is a key challenge to speed up this tedious process. Whereas supervised deep learning has proven very efficient for such object-level scene understanding, e.g., generic object detection and segmentation in everyday scenes, it however requires large sets of per-pixel labeled images, that are hardly available for numerous application contexts, including industrial robotics. We thus propose a step towards a practical interactive application for generating an object-oriented robotic grasp, requiring as inputs only one depth map of the scene and one user click on the next object to extract. More precisely, we address in this paper the middle issue of object seg-mentation in top views of piles of bulk objects given a pixel location, namely seed, provided interactively by a human operator. We propose a twofold framework for generating edge-driven instance segments. First, we repurpose a state-of-the-art fully convolutional object contour detector for seed-based instance segmentation by introducing the notion of edge-mask duality with a novel patch-free and contour-oriented loss function. Second, we train one model using only synthetic scenes, instead of manually labeled training data. Our experimental results show that considering edge-mask duality for training an encoder-decoder network, as we suggest, outperforms a state-of-the-art patch-based network in the present application context.Comment: This is a pre-print of an article published in Human Friendly Robotics, 10th International Workshop, Springer Proceedings in Advanced Robotics, vol 7. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-89327-3\_16, Springer Proceedings in Advanced Robotics, Siciliano Bruno, Khatib Oussama, In press, Human Friendly Robotics, 10th International Workshop,

LHC-scale left-right symmetry and unification

We construct a comprehensive list of nonsupersymmetric standard model extensions with a low-scale left-right (LR)-symmetric intermediate stage that may be obtained as simple low-energy effective theories within a class of renormalizable SO(10) grand unified theories. Unlike the traditional minimal LR models many of our example settings support a perfect gauge coupling unification even if the LR scale is in the LHC domain at a price of only (a few copies of) one or two types of extra fields pulled down to the TeV-scale ballpark. We discuss the main aspects of a potentially realistic model building conforming the basic constraints from the quark and lepton sector flavor structure, proton decay limits, etc. We pay special attention to the theoretical uncertainties related to the limited information about the underlying unified framework in the bottom-up approach, in particular, to their role in the possible extraction of the LR-breaking scale. We observe a general tendency for the models without new colored states in the TeV domain to be on the verge of incompatibility with the proton stability constraints

A spatial econometrics application for analyzing extreme poverty in the municipalities of Antioquia Department

Este artículo comprueba la existencia de autocorrelación espacial al considerar la proporción de personas que viven en situación de miseria en los municipios del departamento de Antioquia, Colombia. Para ello se utiliza el test I de Moran y se propone un algoritmo para descartar la posibilidad de que la dependencia espacial sea espuria. Los resultados demuestran la necesidad de tener en cuenta la econometría espacial para determinar la asignación óptima del gasto social, destinado a intervenir en forma efectiva la situación de miseria en los municipios del departamento de AntioquiaThis article proves the existence of a spatial correlation when considering the per­centage of people living in extreme poverty in the municipalities of the Antioquia department. For this purpose, a Moran I test is used and an algorithm is proposed in order to discard the possibility of a spurious spatial dependency. Results prove that spatial econometrics must be bared in mind for an optima allocation of social expenditure, destined to effectively intervene extreme poverty in the Antioquia department.Neste artigo se comprova a existencia de auto correlação espacial ao considerar a proporção de pessoas que vivem em situação de miséria nos municípios do depar­tamento de Antioquia, Colombia. Para isso se utiliza o test I de Moran e se propõe um algoritmo para descartar a possibilidade de que a dependencia espacial seja espúria. Os resultados demostram a necessidade de ter em conta a econometria espacial para determina a designação ótima do gasto social, destinado a intervir em forma efetiva a situação de miséria nos municípios do departamento de Antioqui

INFLUENCE OF AGE AND HEIGHT POSITION ON COLOMBIAN GUADUA ANGUSTIFOLIA BAMBOO MECHANICAL PROPERTIES

The age of bamboo is a key factor that affects its mechanical properties. Bamboo Guadua angustifolia kunt (Guadua a.k.) has been used as a construction material in America, but the influence of the age and height position of the culm on the mechanical properties has not been studied in detail. In this study, selected mechanical properties of Guadua a.k from 2 to 5 year old culms, located at different heights, were investigated using international standard test procedures (ISO 22157). Based on the experimental results, it was found that the top portion (sobrebasa) showed the maximum strength and modulus of elasticity compared to the other portions, since this portion of bamboo has higher density. More over, density of Guadua a.k. culm has more influence in modulus of rupture in bending, than in any of the other studied mechanical properties. Regardless of the culm height, it seems that the mature age of Guadua angustifolia kunt is reached between 3 and 4 years old, because the mechanical properties at those ages were the highest and remained almost constant, whereas the mechanical properties of the culms at the age of 5 were the lowest