Maximized Posteriori Attributes Selection from Facial Salient Landmarks for Face Recognition

This paper presents a robust and dynamic face recognition technique based on the extraction and matching of devised probabilistic graphs drawn on SIFT features related to independent face areas. The face matching strategy is based on matching individual salient facial graph characterized by SIFT features as connected to facial landmarks such as the eyes and the mouth. In order to reduce the face matching errors, the Dempster-Shafer decision theory is applied to fuse the individual matching scores obtained from each pair of salient facial features. The proposed algorithm is evaluated with the ORL and the IITK face databases. The experimental results demonstrate the effectiveness and potential of the proposed face recognition technique also in case of partially occluded faces.Comment: 8 pages, 2 figure

Self-Supervised Relative Depth Learning for Urban Scene Understanding

As an agent moves through the world, the apparent motion of scene elements is (usually) inversely proportional to their depth. It is natural for a learning agent to associate image patterns with the magnitude of their displacement over time: as the agent moves, faraway mountains don't move much; nearby trees move a lot. This natural relationship between the appearance of objects and their motion is a rich source of information about the world. In this work, we start by training a deep network, using fully automatic supervision, to predict relative scene depth from single images. The relative depth training images are automatically derived from simple videos of cars moving through a scene, using recent motion segmentation techniques, and no human-provided labels. This proxy task of predicting relative depth from a single image induces features in the network that result in large improvements in a set of downstream tasks including semantic segmentation, joint road segmentation and car detection, and monocular (absolute) depth estimation, over a network trained from scratch. The improvement on the semantic segmentation task is greater than those produced by any other automatically supervised methods. Moreover, for monocular depth estimation, our unsupervised pre-training method even outperforms supervised pre-training with ImageNet. In addition, we demonstrate benefits from learning to predict (unsupervised) relative depth in the specific videos associated with various downstream tasks. We adapt to the specific scenes in those tasks in an unsupervised manner to improve performance. In summary, for semantic segmentation, we present state-of-the-art results among methods that do not use supervised pre-training, and we even exceed the performance of supervised ImageNet pre-trained models for monocular depth estimation, achieving results that are comparable with state-of-the-art methods

Arsenic induces metabolome remodeling in mature human adipocytes.

Human lifetime exposure to arsenic through drinking water, food supply or industrial pollution leads to its accumulation in many organs such as liver, kidneys, lungs or pancreas but also adipose tissue. Recently, population-based studies revealed the association between arsenic exposure and the development of metabolic diseases such as obesity and type 2 diabetes. To shed light on the molecular bases of such association, we determined the concentration that inhibited 17% of cell viability and investigated the effects of arsenic acute exposure on adipose-derived human mesenchymal stem cells differentiated in vitro into mature adipocytes and treated with sodium arsenite (NaAsO <sub>2</sub> , 10 nM to 10 µM). Untargeted metabolomics and gene expression analyses revealed a strong dose-dependent inhibition of lipogenesis and lipolysis induction, reducing the cellular ability to store lipids. These dysregulations were emphasized by the inhibition of the cellular response to insulin, as shown by the perturbation of several genes and metabolites involved in the mentioned biological pathways. Our study highlighted the activation of an adaptive oxidative stress response with the strong induction of metallothioneins and increased glutathione levels in response to arsenic accumulation that could exacerbate the decreased insulin sensitivity of the adipocytes. Arsenic exposure strongly affected the expression of arsenic transporters, responsible for arsenic influx and efflux, and induced a pro-inflammatory state in adipocytes by enhancing the expression of the inflammatory interleukin 6 (IL6). Collectively, our data showed that an acute exposure to low levels of arsenic concentrations alters key adipocyte functions, highlighting its contribution to the development of insulin resistance and the pathogenesis of metabolic disorders

The taper of cast post preparation measured using innovative image processing technique

<p>Abstract</p> <p>Background</p> <p>No documentation in the literature about taper of cast posts. This study was conducted to measure the degree of cast posts taper, and to evaluate its suitability based on the anatomy aspects of the common candidate teeth for post reconstruction.</p> <p>Methods</p> <p>Working casts for cast posts, prepared using Gates Glidden drills, were collected. Impressions of post spaces were made using polyvinyl siloxan putty/wash technique. Digital camera with a 10' high quality lens was used for capturing two digital images for each impression; one in the Facio-Lingual (FL) and the other in the Mesio-Distal (MD) directions. Automated image processing program was developed to measure the degree of canal taper. Data were analyzed using Statistical Package for Social Sciences software and One way Analysis of Variance.</p> <p>Results</p> <p>Eighty four dies for cast posts were collected: 16 for each maxillary anterior teeth subgroup, and 18 for each maxillary and mandibular premolar subgroup. Mean of total taper for all preparations was 10.7 degree. There were no statistical differences among the total taper of all groups (P = .256) or between the MD and FL taper for each subgroup. Mean FL taper for the maxillary first premolars was lower significantly (P = .003) than the maxillary FL taper of the second premolars. FL taper was higher than the MD taper in all teeth except the maxillary first premolars.</p> <p>Conclusions</p> <p>Taper produced did not reflect the differences among the anatomy of teeth. While this technique deemed satisfactory in the maxillary anterior teeth, the same could not be said for the maxillary first premolars. Careful attention to the root anatomy is mandatory.</p

The Role of Additive Neurogenesis and Synaptic Plasticity in a Hippocampal Memory Model with Grid-Cell Like Input

Recently, we presented a study of adult neurogenesis in a simplified hippocampal memory model. The network was required to encode and decode memory patterns despite changing input statistics. We showed that additive neurogenesis was a more effective adaptation strategy compared to neuronal turnover and conventional synaptic plasticity as it allowed the network to respond to changes in the input statistics while preserving representations of earlier environments. Here we extend our model to include realistic, spatially driven input firing patterns in the form of grid cells in the entorhinal cortex. We compare network performance across a sequence of spatial environments using three distinct adaptation strategies: conventional synaptic plasticity, where the network is of fixed size but the connectivity is plastic; neuronal turnover, where the network is of fixed size but units in the network may die and be replaced; and additive neurogenesis, where the network starts out with fewer initial units but grows over time. We confirm that additive neurogenesis is a superior adaptation strategy when using realistic, spatially structured input patterns. We then show that a more biologically plausible neurogenesis rule that incorporates cell death and enhanced plasticity of new granule cells has an overall performance significantly better than any one of the three individual strategies operating alone. This adaptation rule can be tailored to maximise performance of the network when operating as either a short- or long-term memory store. We also examine the time course of adult neurogenesis over the lifetime of an animal raised under different hypothetical rearing conditions. These growth profiles have several distinct features that form a theoretical prediction that could be tested experimentally. Finally, we show that place cells can emerge and refine in a realistic manner in our model as a direct result of the sparsification performed by the dentate gyrus layer

An Open Source Framework for Standardized Comparisons of Face Recognition Algorithms

In this paper we introduce the facereclib, the first software library that allows to compare a variety of face recognition algorithms on most of the known facial image databases and that permits rapid prototyping of novel ideas and testing of meta-parameters of face recognition algorithms. The facereclib is built on the open source signal processing and machine learning library Bob. It uses well-specified face recognition protocols to ensure that results are comparable and reproducible. We show that the face recognition algorithms implemented in Bob as well as third party face recognition libraries can be used to run face recognition experiments within the framework of the facereclib. As a proof of concept, we execute four different state-of-the-art face recognition algorithms: local Gabor binary pattern histogram sequences (LGBPHS), Gabor graph comparisons with a Gabor phase based similarity measure, inter-session variability modeling (ISV) of DCT block features, and the linear discriminant analysis on two different color channels (LDA-IR) on two different databases: The Good, The Bad, & The Ugly, and the BANCA database, in all cases using their fixed protocols. The results show that there is not one face recognition algorithm that outperforms all others, but rather that the results are strongly dependent on the employed database