Feature-based terrain editing from complex sketches

We present a new method for first person sketch-based editing of terrain models. As in usual artistic pictures, the input sketch depicts complex silhouettes with cusps and T-junctions, which typically correspond to non-planar curves in 3D. After analysing depth constraints in the sketch based on perceptual cues, our method best matches the sketched silhouettes with silhouettes or ridges of the input terrain. A deformation algorithm is then applied to the terrain, enabling it to exactly match the sketch from the given perspective view, while insuring that none of the user-defined silhouettes is hidden by another part of the terrain. We extend this sketch-based terrain editing framework to handle a collection of multi-view sketches. As our results show, this method enables users to easily personalize an existing terrain, while preserving its plausibility and style.This work was conducted during an internship of Flora Ponjou Tasse at Inria Rhône-Alpes in Grenoble. It was partly supported by the ERC advanced grant EXPRESSIVE.This is the accepted manuscript. The final version is available from Elsevier at http://www.sciencedirect.com/science/article/pii/S009784931400081

Cluster-based point set saliency

© 2015 IEEE. We propose a cluster-based approach to point set saliency detection, a challenge since point sets lack topological information. A point set is first decomposed into small clusters, using fuzzy clustering. We evaluate cluster uniqueness and spatial distribution of each cluster and combine these values into a cluster saliency function. Finally, the probabilities of points belonging to each cluster are used to assign a saliency to each point. Our approach detects fine-scale salient features and uninteresting regions consistently have lower saliency values. We evaluate the proposed saliency model by testing our saliency-based keypoint detection against a 3D interest point detection benchmark. The evaluation shows that our method achieves a good balance between false positive and false negative error rates, without using any topological information

Intestinal microbiota in human health and disease: the impact of probiotics

The complex communities of microorganisms that colonise the human gastrointestinal tract play an important role in human health. The development of culture-independent molecular techniques has provided new insights in the composition and diversity of the intestinal microbiota. Here, we summarise the present state of the art on the intestinal microbiota with specific attention for the application of high-throughput functional microbiomic approaches to determine the contribution of the intestinal microbiota to human health. Moreover, we review the association between dysbiosis of the microbiota and both intestinal and extra-intestinal diseases. Finally, we discuss the potential of probiotic microorganism to modulate the intestinal microbiota and thereby contribute to health and well-being. The effects of probiotic consumption on the intestinal microbiota are addressed, as well as the development of tailor-made probiotics designed for specific aberrations that are associated with microbial dysbiosis

Learning Attentive and Hierarchical Representations for 3D Shape Recognition

© 2020, Springer Nature Switzerland AG. This paper proposes a novel method for 3D shape representation learning, namely Hyperbolic Embedded Attentive Representation (HEAR). Different from existing multi-view based methods, HEAR develops a unified framework to address both multi-view redundancy and single-view incompleteness. Specifically, HEAR firstly employs a hybrid attention (HA) module, which consists of a view-agnostic attention (VAA) block and a view-specific attention (VSA) block. These two blocks jointly explore distinct but complementary spatial saliency of local features for each single-view image. Subsequently, a multi-granular view pooling (MVP) module is introduced to aggregate the multi-view features with different granularities in a coarse-to-fine manner. The resulting feature set implicitly has hierarchical relations, which are therefore projected into a Hyperbolic space by adopting the Hyperbolic embedding. A hierarchical representation is learned by Hyperbolic multi-class logistic regression based on the Hyperbolic geometry. Experimental results clearly show that HEAR outperforms the state-of-the-art approaches on three 3D shape recognition tasks including generic 3D shape retrieval, 3D shape classification and sketch-based 3D shape retrieval