Multi-scale Orderless Pooling of Deep Convolutional Activation Features

Deep convolutional neural networks (CNN) have shown their promise as a universal representation for recognition. However, global CNN activations lack geometric invariance, which limits their robustness for classification and matching of highly variable scenes. To improve the invariance of CNN activations without degrading their discriminative power, this paper presents a simple but effective scheme called multi-scale orderless pooling (MOP-CNN). This scheme extracts CNN activations for local patches at multiple scale levels, performs orderless VLAD pooling of these activations at each level separately, and concatenates the result. The resulting MOP-CNN representation can be used as a generic feature for either supervised or unsupervised recognition tasks, from image classification to instance-level retrieval; it consistently outperforms global CNN activations without requiring any joint training of prediction layers for a particular target dataset. In absolute terms, it achieves state-of-the-art results on the challenging SUN397 and MIT Indoor Scenes classification datasets, and competitive results on ILSVRC2012/2013 classification and INRIA Holidays retrieval datasets

PlaNet - Photo Geolocation with Convolutional Neural Networks

Is it possible to build a system to determine the location where a photo was taken using just its pixels? In general, the problem seems exceptionally difficult: it is trivial to construct situations where no location can be inferred. Yet images often contain informative cues such as landmarks, weather patterns, vegetation, road markings, and architectural details, which in combination may allow one to determine an approximate location and occasionally an exact location. Websites such as GeoGuessr and View from your Window suggest that humans are relatively good at integrating these cues to geolocate images, especially en-masse. In computer vision, the photo geolocation problem is usually approached using image retrieval methods. In contrast, we pose the problem as one of classification by subdividing the surface of the earth into thousands of multi-scale geographic cells, and train a deep network using millions of geotagged images. While previous approaches only recognize landmarks or perform approximate matching using global image descriptors, our model is able to use and integrate multiple visible cues. We show that the resulting model, called PlaNet, outperforms previous approaches and even attains superhuman levels of accuracy in some cases. Moreover, we extend our model to photo albums by combining it with a long short-term memory (LSTM) architecture. By learning to exploit temporal coherence to geolocate uncertain photos, we demonstrate that this model achieves a 50% performance improvement over the single-image model

Fatigue modelling for gas nitriding

The present study aims to develop an algorithm able to predict the fatigue lifetime of nitrided steels. Linear multi-axial fatigue criteria are used to take into account the gradients of mechanical properties provided by the nitriding process. Simulations on rotating bending fatigue specimens are made in order to test the nitrided surfaces. The fatigue model is applied to the cyclic loading of a gear from a simulation using the finite element software Ansys. Results show the positive contributions of nitriding on the fatigue strength.&nbsp

Early burst detection for memory-efficient image retrieval

International audienceRecent works show that image comparison based on local descriptors is corrupted by visual bursts, which tend to dominate the image similarity. The existing strategies, like power-law normalization, improve the results by discounting the contribution of visual bursts to the image similarity. In this paper, we propose to explicitly detect the visual bursts in an image at an early stage. We compare several detection strategies jointly taking into account feature similarity and geometrical quantities. The bursty groups are merged into meta-features, which are used as input to state-of-the-art image search systems such as VLAD or the selective match kernel. Then, we show the interest of using this strategy in an asymmetrical manner, with only the database features being aggregated but not those of the query. Extensive experiments performed on public benchmarks for visual retrieval show the benefits of our method, which achieves performance on par with the state of the art but with a significantly reduced complexity, thanks to the lower number of features fed to the indexing system

Solution Counting for CSP and SAT with Large Tree-Width

Рассмотрена проблема подсчета количества решений задачи совместимости ограничений (Constraint Satisfaction Problem). Для ее решения был адаптирован метод обратного прослеживания с ацикличным представлением графа ограничений (Backtracking with Tree-Decomposition). Предложен точный алгоритм, сложность которого экспоненциально зависит от ширины дерева, и приближенный алгоритм, экспоненциально зависящий от размера максимальной клики.The problem of counting the number of solutions of a CSP is considered. For solving the problem the Backtracking with a Tree-Decomposition method was adapted. The exact algorithm is suggested which has the worst-time complexity exponential in a tree width, as well as iterative algorithm that has computational complexity exponential in a maximum clique size.Розглянуто проблему підрахунку кількості розв’язків задачі сумісності обмежень (Constraint Satisfaction Problem). Для її розв’язку було адаптовано метод зворотного простеження з ациклічним поданням графа обмежень (Backtracking with Tree-Decomposition). Запропоновано точний алгоритм, складність якого експоненційно залежить від ширини дерева, і наближений алгоритм, експоненційно залежний від розміру максимальної кліки

Influence of oxidizing and Nitriding parameters on nitrogen concentration of electrical steels

The influence of oxidizing and nitriding parameters on the nitrogen concentration of grain-oriented electrical steels preliminary to the development of the final Goss texture was explored. Results show that the nitrogen enrichment is driven by a ferrite to austenite transformation during thermochemical treatments. Such a trans- formation is promoted by (i) a redistribution of ferrite-forming elements close to the surface during oxidizing prior to nitriding, (ii) the oxygen content within the oxide layer prior to nitriding, (iii) the temperature of oxidizing and nitriding, and (iv) the nitrogen enrichment during nitriding. Optimization of the nitrogen content, and thus the precipitation kinetics of alloying elements nitrides (e.g. inhibitors) required for the development of the final Goss texture can be controlled by an optimization of the oxide layer growth, the temperatures and gas mixture of nitriding.collaboration thyssenkrupp Electrical Stee

Diagnosis of focal liver lesions from ultrasound using deep learning

PURPOSE: The purpose of this study was to create an algorithm that simultaneously detects and characterizes (benign vs. malignant) focal liver lesion (FLL) using deep learning. MATERIALS AND METHODS: We trained our algorithm on a dataset proposed during a data challenge organized at the 2018 Journées Francophones de Radiologie. The dataset was composed of 367 two-dimensional ultrasound images from 367 individual livers, captured at various institutions. The algorithm was guided using an attention mechanism with annotations made by a radiologist. The algorithm was then tested on a new data set from 177 patients. RESULTS: The models reached mean ROC-AUC scores of 0.935 for FLL detection and 0.916 for FLL characterization over three shuffled three-fold cross-validations performed with the training data. On the new dataset of 177 patients, our models reached a weighted mean ROC-AUC scores of 0.891 for seven different tasks. CONCLUSION: This study that uses a supervised-attention mechanism focused on FLL detection and characterization from liver ultrasound images. This method could prove to be highly relevant for medical imaging once validated on a larger independent cohort

A Dense-Depth Representation for VLAD descriptors in Content-Based Image Retrieval

The recent advances brought by deep learning allowed to improve the performance in image retrieval tasks. Through the many convolutional layers, available in a Convolutional Neural Network (CNN), it is possible to obtain a hierarchy of features from the evaluated image. At every step, the patches extracted are smaller than the previous levels and more representative. Following this idea, this paper introduces a new detector applied on the feature maps extracted from pre-trained CNN. Specifically, this approach lets to increase the number of features in order to increase the performance of the aggregation algorithms like the most famous and used VLAD embedding. The proposed approach is tested on different public datasets: Holidays, Oxford5k, Paris6k and UKB

Mechanical behavior of polycrystals: Coupled in situ DIC-EBSD analysis of pure copper under tensile test

Understanding the mechanisms at the microstructure scale is of great importance for modeling the behavior of materials at different scales. To this end, digital image correlation (DIC) is an effective measurement method for evaluating the strains generated by various loading conditions. The objective of this paper is to describe the experimental setup and the use of high resolution digital image correlation (HRDIC) during in situ Scanning Electron Microscope (SEM) tests in order to provide a coupling between polycrystalline modeling and experiment in the near future. The HRDIC technique is used to evaluate the tensile behavior of a pure copper polycrystal at room temperature. Several magnitudes are investigated in order to discuss the representativeness of the results with respect to the macroscopic scale. The selected image correlation parameters are discussed regarding the ability of the technique to define inter- and intra- granular strain heterogeneities. Finally, based on EBSD analyzes, the impact of grain orientation on the mechanical behavior is discussed. The Schmid factor, calculated from a macroscopic stress, appears to be the determining factor concerning the orientation of the location bands. On the other hand, it is not sufficient to define the mean strains in the grains