TVL₁ Planarity Regularization for 3D Shape Approximation

The modern emergence of automation in many industries has given impetus to extensive research into mobile robotics. Novel perception technologies now enable cars to drive autonomously, tractors to till a field automatically and underwater robots to construct pipelines. An essential requirement to facilitate both perception and autonomous navigation is the analysis of the 3D environment using sensors like laser scanners or stereo cameras. 3D sensors generate a very large number of 3D data points when sampling object shapes within an environment, but crucially do not provide any intrinsic information about the environment which the robots operate within. This work focuses on the fundamental task of 3D shape reconstruction and modelling from 3D point clouds. The novelty lies in the representation of surfaces by algebraic functions having limited support, which enables the extraction of smooth consistent implicit shapes from noisy samples with a heterogeneous density. The minimization of total variation of second differential degree makes it possible to enforce planar surfaces which often occur in man-made environments. Applying the new technique means that less accurate, low-cost 3D sensors can be employed without sacrificing the 3D shape reconstruction accuracy

Nonlinear Multilayered Representation of Graph-Signals

We propose a nonlinear multiscale decomposition of signals defined on the vertex set of a general weighted graph. This decomposition is inspired by the hierarchical multiscale (BV, L 2) decomposition of Tadmor, Nezzar, and Vese (Multiscale Model. Simul. 2(4):554–579, 2004). We find the decomposition by iterative regularization using a graph variant of the classical total variation regularization (Rudin et al, Physica D 60(1–4):259–268, 1992). Using tools from convex analysis, and in particular Moreau’s identity, we carry out the mathematical study of the proposed method, proving the convergence of the representation and providing an energy decomposition result. The choice of the sequence of scales is also addressed. Our study shows that the initial scale can be related to a discrete version of Meyer’s norm (Meyer, Oscillating Patterns in Image Processing and Nonlinear Evolution Equations, 2001) which we introduce in the present paper. We propose to use the recent primal-dual algorithm of Chambolle and Pock (J. Math. Imaging Vis. 40:120–145, 2011) in order to compute both the minimizer of the graph total variation and the corresponding dual norm. By applying the graph model to digital images, we investigate the use of nonlocal methods to the multiscale decomposition task. Since the only assumption needed to apply our method is that the input data is living on a graph, we are also able to tackle the task of adaptive multi

Semantic image completion through an adversarial strategy

Comunicació presentada a: VISIGRAPP 2019: Computer Vision, Imaging and Computer Graphics Theory and Applications. 14th International Joint Conference, celebrat del 25 al 27 de febrer de 2019 a Praga, República Txeca.Image completion or image inpainting is the task of filling in missing regions of an image. When those areas are large and the missing information is unique such that the information and redundancy available in the image is not useful to guide the completion, the task becomes even more challenging. This paper proposes an automatic semantic inpainting method able to reconstruct corrupted information of an image by semantically interpreting the image itself. It is based on an adversarial strategy followed by an energy-based completion algorithm. First, the data latent space is learned by training a modified Wasserstein generative adversarial network. Second, the learned semantic information is combined with a novel optimization loss able to recover missing regions conditioned by the available information. Moreover, we present an application in the context of face inpainting, where our method is used to generate a new face by integrating desired facial attributes or expressions from a reference face. This is achieved by slightly modifying the objective energy. Quantitative and qualitative top-tier results show the power and realism of the presented method.The authors acknowledge partial support by MICINN/FEDER UE project, reference PGC2018-098625-B-I00 and by H2020-MSCA-RISE-2017 project, reference 777826 NoMADS. We also thank NVIDIA for the Quadro P6000 GPU donation

Parallel Strip Segment Recognition and Application to Metallic Tubular Object Measure

International audienc

Investigating the anticancer potential of 4-phenylthiazole derived Ru(ii) and Os(ii) metalacycles

In this contribution we report the synthesis, characterization and in vitro anticancer activity of novel cyclometalated 4-phenylthiazole-derived ruthenium(ii) (2a-e) and osmium(ii) (3a-e) complexes. Formation and sufficient purity of the complexes were unambigiously confirmed by H-1-, C-13- and 2D-NMR techniques, X-ray diffractometry, HRMS and elemental analysis. The binding preferences of these cyclometalates to selected amino acids and to DNA models including G-quadruplex structures were analyzed. Additionally, their stability and behaviour in aqueous solutions was determined by UV-Vis spectroscopy. Their cellular accumulation, their ability of inducing apoptosis, as well as their interference in the cell cycle were studied in SW480 colon cancer cells. The anticancer potencies were investigated in three human cancer cell lines and revealed IC50 values in the low micromolar range, in contrast to the biologically inactive ligands