Automatic reactivity characterisation of char particles from pulverised coal combustion using computer vision

Char morphologies produced during pulverised coal combustion may determine coal reactivity which affects the combustion efficiency and the emissions of CO2 in power plants. Commonly, char samples are characterised manually, but this process is subjective and time-consuming. This work proposes methods to automate the char reactivity characterisation using microscopy images and computer vision techniques. These methods are summarised in three contributions: the localisation of char particles based on candidate regions and deep learning methods; the classification of particles into char reactivity groups using morphological and texture features; and the integration in a system of the two previous proposals to characterise char sample reactivity. The proposed system successfully estimate char reactivity in a fast and accurate way

Análisis de estudio de factibilidad para la producción y comercialización de los derivados de fresa en Villapinzón

Este proyecto tiene como finalidad demostrar que derivados podemos producir con la fresa para que no haya tanto desperdicio en los diferentes cultivos en el municipio de Villapinzón ya que se ha encontrado una problemática y es que solo se está comercializando la mejor fresa (fresa seleccionada), siendo transportada a la ciudad y del resto desechada al pie de pocetas, pastos o como de alimento para algunos animales; debido a lo anterior vemos que se está presentando un gran desperdicio de este producto, puesto que no hay un aprovechamiento del 100% de la producción, por ello el objetivo es producir los derivados de la fresa como lo son: el yogurt, la mermelada, tartaleta y batidos de fresa, siendo productos naturales y saludables; Por eso por medio de este plan de negocios se reflejara los insumos e implementos necesarios para fabricar cada uno de estos productos, igualmente se podrá observar el estudio de mercadeo que se llevara a cabo por medio de degustaciones de los diferentes productos y la aplicación de encuesta en la zona rural o urbana, para de esta manera obtener los resultados, si estos son favorables, se llegaría al mercado con precios justos y al alcance del bolsillo, se realizaría publicidad a través de los diferentes medios comunicativos, ya teniendo el producto es buscar alianzas con canales de distribución como supermercados, tiendas de barrio y negocios intermunicipales; el enfoque principal es crear una empresa que sea reconocida a nivel nacional en un futuro, destacándose por la calidad de producto y el valor que se le va a dar al trabajo campesino, teniendo la oportunidad de que la inversión económica mejore y que hayan mucho más puertas laborales para la comunidad.The purpose of this project is to demonstrate what derivatives we can produce with strawberries so that there is not so much waste in the different crops in the municipality of Villapinzón, since a problem has been found and that is that only the best strawberry (selected strawberry) is being marketed. being transported to the city and the rest discarded at the foot of pools, pastures or as food for some animals; Due to the above we see that a great waste of this product is being presented, since there is not a 100% use of the production, therefore the objective is to produce strawberry derivatives such as: yogurt, jam, strawberry tart and shakes, being natural and healthy products; For this reason, through this business plan, the necessary inputs and implements to manufacture each of these products will be reflected, as well as the marketing study that will be carried out through tastings of the different products and the application of a survey. in the rural or urban area, in order to obtain the results, if these are favorable, the market would be reached with fair prices and within reach of the pocket, advertising would be carried out through the different communication media, already having the product is to look for alliances with distribution channels such as supermarkets, neighborhood stores and inter-municipal businesses; The main focus is to create a company that is recognized nationally in the future, standing out for the quality of the product and the value that will be given to peasant work, having the opportunity for economic investment to improve and for there to be much more labor doors for the community

A data augmentation strategy for improving age estimation to support CSEM detection

[EN] Leveraging image-based age estimation in preventing Child Sexual Exploitation Material (CSEM) content over the internet is not investigated thoroughly in the research community. While deep learning methods are considered state-of-the-art for general age estimation, they perform poorly in predicting the age group of minors and older adults due to the few examples of these age groups in the existing datasets. In this work, we present a data augmentation strategy to improve the performance of age estimators trained on imbalanced data based on synthetic image generation and artificial facial occlusion. Facial occlusion is focused on modelling as CSEM criminals tend to cover certain parts of the victim, such as the eyes, to hide their identity. The proposed strategy is evaluated using the Soft Stagewise Regression Network (SSR-Net), a compact size age estimator and three publicly available datasets composed mainly of non-occluded images. Therefore, we create the Synthetic Augmented with Occluded Faces (SAOF-15K) dataset to assess the performance of eye and mouthoccluded images. Results show that our strategy improves the performance of the evaluated age estimator

A Video Summarization Approach to Speed-up the Analysis of Child Sexual Exploitation Material

[Abstract] Identifying key content from a video is essential for many security applications such as motion/action detection, person re-identification and recognition. Moreover, summarizing the key information from Child Sexual Exploitation Materials, especially videos, which mainly contain distinctive scenes including people’s faces is crucial to speed-up the investigation of Law Enforcement Agencies. In this paper, we present a video summarization strategy that combines perceptual hashing and face detection algorithms to keep the most relevant frames of a video containing people’s faces that may correspond to victims or offenders. Due to legal constraints to access Child Sexual Abuse datasets, we evaluated the performance of the proposed strategy during the detection of adult pornography content with the NDPI-800 dataset. Also, we assessed the capability of our strategy to create video summaries preserving frames with distinctive faces from the original video using ten additional short videos manually labeled. Results showed that our approach can detect pornography content with an accuracy of 84.15% at a speed of 8.05 ms/frame making this appropriate for realtime applications.This work was supported by the framework agreement between the Universidad de León and INCIBE (Spanish National Cybersecurity Institute) under Addendum 01. Also, this research has been funded with support from the European Commission under the 4NSEEK project with Grant Agreement 821966. This publication reflects the views only of the authors, and the European Commission cannot be held responsible for any use which may be made of the information contained therein. Finally, we acknowledge the NVIDIA Corporation for the donation of the TITAN Xp GPU

Supervised ranking approach to identify infLuential websites in the darknet

[EN] The anonymity and high security of the Tor network allow it to host a significant amount of criminal activities. Some Tor domains attract more traffic than others, as they offer better products or services to their customers. Detecting the most influential domains in Tor can help detect serious criminal activities. Therefore, in this paper, we present a novel supervised ranking framework for detecting the most influential domains. Our approach represents each domain with 40 features extracted from five sources: text, named entities, HTML markup, network topology, and visual content to train the learning-to-rank (LtR) scheme to sort the domains based on user-defined criteria. We experimented on a subset of 290 manually ranked drug-related websites from Tor and obtained the following results. First, among the explored LtR schemes, the listwise approach outperforms the benchmarked methods with an NDCG of 0.93 for the top-10 ranked domains. Second, we quantitatively proved that our framework surpasses the link-based ranking techniques. Third, we observed that using the user-visible text feature can obtain comparable performance to all the features with a decrease of 0.02 at NDCG@5. The proposed framework might support law enforcement agencies in detecting the most influential domains related to possible suspicious activities.SIPublicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

Enhancing text recognition on Tor Darknet images

[Abstract] Text Spotting can be used as an approach to retrieve information found in images that cannot be obtained otherwise, by performing text detection rst and then recognizing the located text. Examples of images to apply this task on can be found in Tor network images, which contain information that may not be found in plain text. When comparing both stages, the latter performs worse due to the low resolution of the cropped areas among other problems. Focusing on the recognition part of the pipeline, we study the performance of ve recognition approaches, based on state-ofthe- art neural network models, standalone OCR, and OCR enhancements. We complement them using string-matching techniques with two lexicons and compare computational time on ve di erent datasets, including Tor network images. Our nal proposal achieved 39,70% precision of text recognition in a custom dataset of images taken from Tor domain

Automatic classification of pores in aluminum castings using machine learning

[Resumen] La inspección de la porosidad de piezas fabricadas se ha realizado tradicionalmente mediante el uso de microscopía manipulada por parte de un técnico humano. Sin embargo, la persona involucrada necesita experiencia en esta tarea, y la cantidad de piezas que se pueden inspeccionar por unidad de tiempo es limitada. La presencia de porosidad en el material es crítica, ya que puede afectar negativamente a las propiedades mecánicas y la calidad de la pieza. En este trabajo se propone automatizar la clasificación de los defectos de porosidad que aparecen en el interior de las piezas fabricadas por fundición. En primer lugar, adquirimos imágenes a partir de piezas de aluminio fabricadas por dos métodos de fundición: uno tradicional usando molde de arena y otro más moderno con la técnica de fabricación aditiva Binder Jetting (BJ). Luego, recortamos regiones con y sin poros, que posteriormente caracterizamos usando descriptores SIFT codificados en características de BoVW para alimentar y entrenar dos clasificadores SVM: uno para predecir si la imagen contiene poro o no, y el otro para indicar si el poro detectado es debido al efecto de gases o por contracción durante la solificación.[Abstract] Porosity inspection of manufactured parts has traditionally been performed using microscopy manipulated by a human technician. However, the person involved needs experience in this task, and the number of parts that can be inspected per unit of time is limited. The presence of porosity in the material is critical, as it can negatively affect the mechanical properties and the quality of the part. In this paper, we propose to automate the classification of the porosity defects that appear inside the parts manufactured by casting. First, we acquire images from aluminum parts manufactured by two casting methods: a traditional one using sand molding and a more modern one with the Binder Jetting (BJ) additive manufacturing technique. Then, we crop regions with and without pores we later describe using SIFT descriptors encoded into BoVW features to feed and train two SVM classifiers: one for predicting if the image contains a pore or not, and the other for also indicating if the pore detected is due to the effect of gases or by shrinkage during solidification.Ministerio de Ciencia, Innovación y Universidades; DPI2017-89840-

Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications

Face recognition is a valuable forensic tool for criminal investigators since it certainly helps in identifying individuals in scenarios of criminal activity like fugitives or child sexual abuse. It is, however, a very challenging task as it must be able to handle low-quality images of real world settings and fulfill real time requirements. Deep learning approaches for face detection have proven to be very successful but they require large computation power and processing time. In this work, we evaluate the speed-accuracy tradeoff of three popular deep-learning-based face detectors on the WIDER Face and UFDD data sets in several CPUs and GPUs. We also develop a regression model capable to estimate the performance, both in terms of processing time and accuracy. We expect this to become a very useful tool for the end user in forensic laboratories in order to estimate the performance for different face detection options. Experimental results showed that the best speed-accuracy tradeoff is achieved with images resized to50%of the original size in GPUs and images resized to25%of the original size in CPUs. Moreover, performance can be estimated using multiple linear regression models with a Mean Absolute Error (MAE) of 0.113, which is very promising for the forensic field