Employing deep learning for sex estimation of adult individuals using 2D images of the humerus

Biological profile estimation, of which sex estimation is a fundamental first stage, is a really important task in forensic human identification. Although there are a large number of methods that address this problem from different bone structures, mainly using the pelvis and the skull, it has been shown that the humerus presents significant sexual dimorphisms that can be used to estimate sex in their absence. However, these methods are often too subjective or costly, and the development of new methods that avoid these problems is one of the priorities in forensic anthropology research. In this respect, the use of artificial intelligence may allow to automate and reduce the subjectivity of biological profile estimation methods. In fact, artificial intelligence has been successfully applied in sex estimation tasks, but most of the previous work focuses on the analysis of the pelvis and the skull. More importantly, the humerus, which can be useful in some situations due to its resistance, has never been used in the development of an automatic sex estimation method. Therefore, this paper addresses the use of machine learning techniques to the task of image classification, focusing on the use of images of the distal epiphysis of the humerus to classify whether it belongs to a male or female individual. To address this, we have used a set of humerus photographs of 417 adult individuals of Mediterranean origin to validate and compare different approaches, using both deep learning and traditional feature extraction techniques. Our best model obtains an accuracy of 91.03% in test, correctly estimating the sex of 92.68% of the males and 89.19% of the females. These results are superior to the ones obtained by the state of the art and by a human expert, who has achieved an accuracy of 83.33% using a state-ofthe- art method on the same data. In addition, the visualization of activation maps allows us to confirm not only that the neural network observes the sexual dimorphisms that have been proposed by the forensic anthropology literature, but also that it has been capable of finding a new region of interest.European Commission FORAGE (B-TIC-456-UGR20

A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human Identification

This paper represents the first survey on the application of AI techniques for the analysis of biomedical images with forensic human identification purposes. Human identification is of great relevance in today’s society and, in particular, in medico-legal contexts. As consequence, all technological advances that are introduced in this field can contribute to the increasing necessity for accurate and robust tools that allow for establishing and verifying human identity. We first describe the importance and applicability of forensic anthropology in many identification scenarios. Later, we present the main trends related to the application of computer vision, machine learning and soft computing techniques to the estimation of the biological profile, the identification through comparative radiography and craniofacial superimposition, traumatism and pathology analysis, as well as facial reconstruction. The potentialities and limitations of the employed approaches are described, and we conclude with a discussion about methodological issues and future research.Spanish Ministry of Science, Innovation and UniversitiesEuropean Union (EU) PGC2018-101216-B-I00Regional Government of Andalusia under grant EXAISFI P18-FR-4262Instituto de Salud Carlos IIIEuropean Union (EU) DTS18/00136European Commission H2020-MSCA-IF-2016 through the Skeleton-ID Marie Curie Individual Fellowship 746592Spanish Ministry of Science, Innovation and Universities-CDTI, Neotec program 2019 EXP-00122609/SNEO-20191236European Union (EU)Xunta de Galicia ED431G 2019/01European Union (EU) RTI2018-095894-B-I0

A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends

Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, imagerelated tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research

Herramientas de gamificación en las enseñanzas y evaluación de las Técnicas de los Sistemas Inteligentes

Memoria del proyecto de innovación y buenas prácticas docentes titulado "Herramientas de gamificación en las enseñanzas y evaluación de las Técnicas de los Sistemas Inteligentes

Automatic landmark annotation in 3D surface scans of skulls: Methodological proposal and reliability study

Dr. Bermejo's work has been supported by the Japan Society for the Promotion of Science (JSPS) as International Research Fellow (Standard Fellowship) . Dr. Mesejo's work is funded by the European Commission H2020-MSCA-IF-2016 through the Skeleton-ID Marie Curie Individ-ual Fellowship [Ref: 746592] . Dr. Valsecchi's work is funded by the Spanish Ministry of Sci-ence and Innovat19F19119ion grant [Ref: PTQ-17-09306] Drs. Ibanez work is funded by Spanish Ministry of Science, In-novation and Universities-CDTI, Neotec program 2019 [Ref: EXP-00122609/SNEO-20191236] . Additionally, This work was supported by the Grant-in-Aid for JSPS Fellows [Ref: 19F19119] , by the Spanish Ministry of Science, Innovation and Universities, and European Regional Development Funds (ERDF) , under grant EXASOCO [Ref: PGC2018-101216-B-I0 0] , and by the Regional Government of Andalusia under grant EXAISFI [Ref: P18-FR-4262] . Funding for open access publication was pro-vided by the University of Granada: CBUA.Background and Objectives: Craniometric landmarks are essential in many biomedical applications, such as morphometric analysis or forensic identification. The process of locating landmarks is usually a manual and slow task, highly influenced by fatigue, skills and the experience of the practitioner. Localization errors are propagated and magnified in subsequent steps, which can result in incorrect measurements or assumptions. Thereby, standardization, reliability and reproducibility lay the foundations for the necessary accuracy in subsequent measurements or anatomical analysis. In this paper, we present an automatic method to annotate 3D surface skull models taking into account anatomical and geometrical features. Methods: The proposed method follows a hybrid structure where a deformable template is used to initialize the landmark positions. Then, a refinement stage is applied using prior anatomical knowledge to ensure a correct placement. Our proposal is validated over thirty 3D skull scans of male Caucasians, acquired by hand-held surface scanning, and a set of 58 craniometric landmarks. A statistical analysis was carried out to analyze the inter-and intra-observer variability of manual annotations and the automatic results, along with a visual assessment of the final results. Results: Inter-observer errors show significant differences, which are reflected in the expert consensus used as reference. The average localization error was 2 . 19 +/- 1 . 5 mm when comparing the automatic landmarks to the reference location. The subsequent visual analysis confirmed the reliability of the refinement method for most landmarks. Conclusions: Repeated manual annotations show a high variability depending on both skills and expertise of the observer, and landmarks' location and characteristics. In contrast, the automatic method provides an accurate, robust and reproducible alternative to the tedious and error-prone task of manual landmarking.European CommissionEuropean Commission Joint Research Centre 746592Spanish Government PTQ-17-09306Spanish Ministry of Science, In-novation and Universities-CDTI, Neotec program 2019 EXP-00122609/SNEO-20191236Spanish Ministry of Science, Innovation and UniversitiesEuropean Regional Development Funds (ERDF) under grant EXASOCO PGC2018-101216-B-I0 0Regional Government of Andalusia under grant EXAISFI P18-FR-4262Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) Japan Society for the Promotion of Science19F1911

Segmentation of Histological Images using a Metaheuristic-based Level Set Approach

This paper presents a two-phase method to segment the hippocampus in histological images. The first phase represents a training stage where, from a training set of manually labelled images, the hippocampus representative shape and texture are derived. The second one, the proper segmentation, uses a metaheuristic to evolve the contour of a geometric deformable model using region and texture information. Three different metaheuristics (real-coded GA, Particle Swarm Optimization and Dierential Evolution) and two classical segmentation algorithms (Chan & Vese model and Geodesic Active Contours) were compared over a test set of 10 histological images. The best results were attained by the real-coded GA, achieving an average and median Dice Coefficient of 0.72 and 0.77, respectively

GPU-based Automatic Configuration of Differential Evolution: a case study

The performance of an evolutionary algorithm strongly depends on the choice of the parameters which regulate its behavior. In this paper, two evolutionary algorithms (Particle Swarm Optimization and Differential Evolution) are used to nd the optimal configuration of parameters for Dierential Evolution. We tested our approach on four benchmark functions, and the comparison with an exhaustive search demonstrated its eectiveness. Then, the same method was used to tune the parameters of Dierential Evolution in solving a real-world problem: the automatic localization of the hippocampus in histological brain images. The results obtained consistently outperformed the ones achieved using manually-tuned parameters. Thanks to a GPU-based implementation, our tuner is up to 8 times faster than the corresponding sequential version

A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges

This publication is supported by ArcelorMittal, Luxembourg Global R&D, specifically the project granted by ArcelorMittal Global R&D Digital Portfolio in collaboration with the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) , University of Granada. This publication is supported by the Andalusian Excel-lence, Spain project P18-FR-4961 and SOMM17/6110/UGR. D. Charte is supported by the Spanish Ministry of Universities, Spain under the FPU program (Ref. FPU17/04069) . Funding for open access charge: Universidad de Granada/CBUA.Image segmentation is an important issue in many industrial processes, with high potential to enhance the manufacturing process derived from raw material imaging. For example, metal phases contained in microstructures yield information on the physical properties of the steel. Existing prior literature has been devoted to develop specific computer vision techniques able to tackle a single problem involving a particular type of metallographic image. However, the field lacks a comprehensive tutorial on the different types of techniques, methodologies, their generalizations and the algorithms that can be applied in each scenario. This paper aims to fill this gap. First, the typologies of computer vision techniques to perform the segmentation of metallographic images are reviewed and categorized in a taxonomy. Second, the potential utilization of pixel similarity is discussed by introducing novel deep learning-based ensemble techniques that exploit this information. Third, a thorough comparison of the reviewed techniques is carried out in two openly available real-world datasets, one of them being a newly published dataset directly provided by ArcelorMittal, which opens up the discussion on the strengths and weaknesses of each technique and the appropriate application framework for each one. Finally, the open challenges in the topic are discussed, aiming to provide guidance in future research to cover the existing gaps.ArcelorMittal, Luxembourg Global RDArcelorMittal Global R&D Digital Portfolio in collaboration with the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of GranadaAndalusian Excellence, Spain project P18-FR-4961 SOMM17/6110/UGRSpanish Ministry of Universities, Spain under the FPU program FPU17/04069Universidad de Granada/CBU