COVID-19 Detection in Chest X-ray Images using a Deep Learning Approach

The Corona Virus Disease (COVID-19) is an infectious disease caused by a new virus that has not been detected in humans before. The virus causes a respiratory illness like the flu with various symptoms such as cough or fever that, in severe cases, may cause pneumonia. The COVID-19 spreads so quickly between people, affecting to 1,200,000 people worldwide at the time of writing this paper (April 2020). Due to the number of contagious and deaths are continually growing day by day, the aim of this study is to develop a quick method to detect COVID-19 in chest X-ray images using deep learning techniques. For this purpose, an object detection architecture is proposed, trained and tested with a public available dataset composed with 1500 images of non-infected patients and infected with COVID-19 and pneumonia. The main goal of our method is to classify the patient status either negative or positive COVID-19 case. In our experiments using SDD300 model we achieve a 94.92% of sensibility and 92.00% of specificity in COVID-19 detection, demonstrating the usefulness application of deep learning models to classify COVID-19 in X-ray images

Marching cubes in an unsigned distance field for surface reconstruction from unorganized point sets

Surface reconstruction from unorganized point set is a common problem in computer graphics -- Generation of the signed distance field from the point set is a common methodology for the surface reconstruction -- The reconstruction of implicit surfaces is made with the algorithm of marching cubes, but the distance field of a point set can not be processed with marching cubes because the unsigned nature of the distance -- We propose an extension to the marching cubes algorithm allowing the reconstruction of 0-level iso-surfaces in an unsigned distance field -- We calculate more information inside each cell of the marching cubes lattice and then we extract the intersection points of the surface within the cell then we identify the marching cubes case for the triangulation -- Our algorithm generates good surfaces but the presence of ambiguities in the case selection generates some topological mistakesWorkflow Management Coalitio

Generative Adversarial Networks to Improve the Robustness of Visual Defect Segmentation by Semantic Networks in Manufacturing Components

This paper describes the application of Semantic Networks for the detection of defects in images of metallic manufactured components in a situation where the number of available samples of defects is small, which is rather common in real practical environments. In order to overcome this shortage of data, the common approach is to use conventional data augmentation techniques. We resort to Generative Adversarial Networks (GANs) that have shown the capability to generate highly convincing samples of a specific class as a result of a game between a discriminator and a generator module. Here, we apply the GANs to generate samples of images of metallic manufactured components with specific defects, in order to improve training of Semantic Networks (specifically DeepLabV3+ and Pyramid Attention Network (PAN) networks) carrying out the defect detection and segmentation. Our process carries out the generation of defect images using the StyleGAN2 with the DiffAugment method, followed by a conventional data augmentation over the entire enriched dataset, achieving a large balanced dataset that allows robust training of the Semantic Network. We demonstrate the approach on a private dataset generated for an industrial client, where images are captured by an ad-hoc photometric-stereo image acquisition system, and a public dataset, the Northeastern University surface defect database (NEU). The proposed approach achieves an improvement of 7% and 6% in an intersection over union (IoU) measure of detection performance on each dataset over the conventional data augmentation

Photometric Stereo-Based Defect Detection System for Steel Components Manufacturing Using a Deep Segmentation Network

This paper presents an automatic system for the quality control of metallic components using a photometric stereo-based sensor and a customized semantic segmentation network. This system is designed based on interoperable modules, and allows capturing the knowledge of the operators to apply it later in automatic defect detection. A salient contribution is the compact representation of the surface information achieved by combining photometric stereo images into a RGB image that is fed to a convolutional segmentation network trained for surface defect detection. We demonstrate the advantage of this compact surface imaging representation over the use of each photometric imaging source of information in isolation. An empirical analysis of the performance of the segmentation network on imaging samples of materials with diverse surface reflectance properties is carried out, achieving Dice performance index values above 0.83 in all cases. The results support the potential of photometric stereo in conjunction with our semantic segmentation network

Tuning of Adaptive Weight Depth Map Generation Algorithms Exploratory Data Analysis and Design of Computer Experiments (DOCE)

In depth map generation algorithms, parameters settings to yield an accurate disparity map estimation are usually chosen empirically or based on un planned experiments -- Algorithms' performance is measured based on the distance of the algorithm results vs. the Ground Truth by Middlebury's standards -- This work shows a systematic statistical approach including exploratory data analyses on over 14000 images and designs of experiments using 31 depth maps to measure the relative inf uence of the parameters and to fine-tune them based on the number of bad pixels -- The implemented methodology improves the performance of adaptive weight based dense depth map algorithms -- As a result, the algorithm improves from 16.78% to 14.48% bad pixels using a classical exploratory data analysis of over 14000 existing images, while using designs of computer experiments with 31 runs yielded an even better performance by lowering bad pixels from 16.78% to 13

Realtime dense stereo matching with dynamic programming in CUDA

Real-time depth extraction from stereo images is an important process in computer vision -- This paper proposes a new implementation of the dynamic programming algorithm to calculate dense depth maps using the CUDA architecture achieving real-time performance with consumer graphics cards -- We compare the running time of the algorithm against CPU implementation and demonstrate the scalability property of the algorithm by testing it on different graphics card

Extending Marching Cubes with Adaptative Methods to obtain more accurate iso-surfaces

This work proposes an extension of the Marching Cubes algorithm, where the goal is to represent implicit functions with higher accuracy using the same grid size -- The proposed algorithm displaces the vertices of the cubes iteratively until the stop condition is achieved -- After each iteration, the difference between the implicit and the explicit representations is reduced, and when the algorithm finishes, the implicit surface representation using the modified cubical grid is more accurate, as the results shall confirm -- The proposed algorithm corrects some topological problems that may appear in the discretization process using the original gridp.35-4

Face Reconstruction with structured light

This article presents a methodology for reconstruction of 3D faces which is based on stereoscopic images of the scene using active and passive surface reconstruction -- A sequence of gray patterns is generated, which are projected onto the scene and their projection recorded by a pair of stereo cameras -- The images are rectified to make coincident their epipolar planes and so to generate a stereo map of the scene -- An algorithm for stereo matching is applied, whose result is a bijective mapping between subsets of the pixels of the images -- A particular connected subset of the images (e.g. the face) is selected by a segmentation algorithm -- The stereo mapping is applied to such a subset and enables the triangulation of the two image readings therefore rendering the (x;y; z) points of the face, which in turn allow the reconstruction of the triangular mesh of the face -- Since the surface might have holes, bilateral filters are applied to have the holes filled -- The algorithms are tested in real conditions and we evaluate their performance with virtual datasets -- Our results show a good reconstruction of the faces and an improvement of the results of passive system

Adaptative cubical grid for isosurface extraction

This work proposes a variation on the Marching Cubes algorithm, where the goal is to represent implicit functions with higher resolution and better graphical quality using the same grid size -- The proposed algorithm displaces the vertices of the cubes iteratively until the stop condition is achieved -- After each iteration, the difference between the implicit and the explicit representations are reduced, and when the algorithm finishes, the implicit surface representation using the modified cubical grid is more detailed, as the results shall confirm -- The proposed algorithm corrects some topological problems that may appear in the discretisation process using the original gri

An Inspection and Classification System for Automotive Component Remanufacturing Industry Based on Ensemble Learning

This paper presents an automated inspection and classification system for automotive component remanufacturing industry, based on ensemble learning. The system is based on different stages allowing to classify the components as good, rectifiable or rejection according to the manufacturer criteria. A study of two deep learning-based models’ performance when used individually and when using an ensemble of them is carried out, obtaining an improvement of 7% in accuracy in the ensemble. The results of the test set demonstrate the successful performance of the system in terms of component classification