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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

Science: general issues --- Neurosciences --- deep learning --- convolutional neural networks --- brain age estimation --- neurodegenerative diseases --- automated diagnosis --- brain image segmentation

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Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification.

Research & information: general --- convolutional neural network --- image segmentation --- multi-scale feature fusion --- semantic features --- Gaofen 6 --- aerial images --- land-use --- Tai’an --- convolutional neural networks (CNNs) --- feature fusion --- ship detection --- optical remote sensing images --- end-to-end detection --- transfer learning --- remote sensing --- single shot multi-box detector (SSD) --- You Look Only Once-v3 (YOLO-v3) --- Faster RCNN --- statistical features --- Gaofen-2 imagery --- winter wheat --- post-processing --- spatial distribution --- Feicheng --- China --- light detection and ranging --- LiDAR --- deep learning --- convolutional neural networks --- CNNs --- mask regional-convolutional neural networks --- mask R-CNN --- digital terrain analysis --- resource extraction --- hyperspectral image classification --- few-shot learning --- quadruplet loss --- dense network --- dilated convolutional network --- artificial neural networks --- classification --- superstructure optimization --- mixed-inter nonlinear programming --- hyperspectral images --- super-resolution --- SRGAN --- model generalization --- image downscaling --- mixed forest --- multi-label segmentation --- semantic segmentation --- unmanned aerial vehicles --- classification ensemble --- machine learning --- Sentinel-2 --- geographic information system (GIS) --- earth observation --- on-board --- microsat --- mission --- nanosat --- AI on the edge --- CNN

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Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification.

convolutional neural network --- image segmentation --- multi-scale feature fusion --- semantic features --- Gaofen 6 --- aerial images --- land-use --- Tai’an --- convolutional neural networks (CNNs) --- feature fusion --- ship detection --- optical remote sensing images --- end-to-end detection --- transfer learning --- remote sensing --- single shot multi-box detector (SSD) --- You Look Only Once-v3 (YOLO-v3) --- Faster RCNN --- statistical features --- Gaofen-2 imagery --- winter wheat --- post-processing --- spatial distribution --- Feicheng --- China --- light detection and ranging --- LiDAR --- deep learning --- convolutional neural networks --- CNNs --- mask regional-convolutional neural networks --- mask R-CNN --- digital terrain analysis --- resource extraction --- hyperspectral image classification --- few-shot learning --- quadruplet loss --- dense network --- dilated convolutional network --- artificial neural networks --- classification --- superstructure optimization --- mixed-inter nonlinear programming --- hyperspectral images --- super-resolution --- SRGAN --- model generalization --- image downscaling --- mixed forest --- multi-label segmentation --- semantic segmentation --- unmanned aerial vehicles --- classification ensemble --- machine learning --- Sentinel-2 --- geographic information system (GIS) --- earth observation --- on-board --- microsat --- mission --- nanosat --- AI on the edge --- CNN

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Monitoring and census of wild animal populations are among the key elements in nature conservation. The use of UAV (Unmanned Aircraft Vehicle) or light aircraft as aerial image acquisition system is a more suitable and cheaper alternative to traditional census methods. However, the manual localization and identification of species within these images can quickly become time-consuming and complex. Detection algorithms, based on Convolutional Neural Networks (CNNs), have shown a good capacity for animal detection based on aerial images. Nevertheless, most of the work is focused on binary detection cases. The main objective of this study is to compare the performances of three recent detection algorithms to detect and identify seven African mammals (Alcelaphinae, buffaloes, elephants, hippopotamuses, kobs, warthogs, and waterbucks) based on high-resolution aerial images of various African landscapes. To do so, the performances of the multi-class CNNs Faster-RCNN, Libra-RCNN and RetinaNet to detect these seven animal species in aerial images from four different datasets were evaluated. The algorithms tested were able to detect 91.8 to 95.5% of the animals, with a ratio of 2.8 to 13.8 false positives per true positive. All three algorithms have generally met the challenges that aerial images can present in animal detection. Libra-RCNN showed the best mean Average Precision (mAP=0.68), the lowest degree of inter-species confusion and a lower sensitivity to variation in prediction thresholding. Hippopotamuses and warthogs were the most difficult species to identify and detect (low precision) by all three algorithms. However, these algorithms present themselves as good future semi-automatic detection tools and each has interesting specificity for a potential practical implementation. La surveillance et le recensement des populations animales sauvages font partie des éléments clefs dans la conservation de la nature. L'utilisation de drones ou d'avions légers comme système d'acquisition d'images aériennes se présente comme une alternative plus adaptée et moins chère que les méthodes traditionnelles d'inventaire. Cependant, la localisation et l'identification manuelles des espèces au sein de ces images peuvent rapidement devenir chronophage et complexe. Des algorithmes de détection, basés sur des réseaux de neurones convolutifs (RNCs), ont montré une bonne capacité à la détection animale sur base d'images aériennes. Néanmoins, la majorité des travaux ont porté sur des cas de détection binaire. L'objectif principal de cette étude est de comparer les performances de trois récents algorithmes à détecter et identifier sept mammifères africains (Alcelaphinae, buffles, éléphants, hippopotames, cobs, des phacochères et cobs à croissant) sur base d'images aériennes à haute résolution de paysages africains variés. Pour ce faire, les performances des RNCs multi-classe Faster-RCNN, Libra-RCNN et RetinaNet à détecter ces sept espèces animales au sein d'images aériennes provenant de quatre jeux de données différents, ont été évaluées. Les algorithmes testés ont réussi à détecter 91,8 à 95,5% des animaux, avec un rapport allant de 2,8 à 13,8 faux positifs par vrai positif. Les trois algorithmes ont globalement relevé les défis que peuvent présenter les images aériennes en détection animale. Libra-RCNN est celui qui a montré la meilleure mean Average Precision (mAP=0.68), le moins de confusion entre les espèces et une moins forte sensibilité à la variation du seuillage des prédictions. Les hippopotames et les phacochères ont été les espèces les plus difficiles à identifier et détecter (faible précision) par les trois algorithmes. Toutefois, ces algorithmes se présentent comme de bons futurs outils de détection semi-automatique et possèdent chacun une spécificité intéressante pour une potentielle implémentation pratique.

object detection --- wildlife monitoring --- unmanned aircraft vehicle --- aerial images --- African mammals --- convolutional neural networks --- computer vision --- deep learning --- détection d'objets --- surveillance de la faune sauvage --- drone --- images aériennes --- mammifères africains --- réseaux de neurones convolutifs --- vision par ordinateur --- apprentissage profond --- Ingénierie, informatique & technologie > Multidisciplinaire, généralités & autres

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Deep learning has been revolutionizing many fields in computer vision, and facial informatics is one of the major fields. Novel approaches and performance breakthroughs are often reported on existing benchmarks. As the performances on existing benchmarks are close to saturation, larger and more challenging databases are being made and considered as new benchmarks, further pushing the advancement of the technologies. Considering face recognition, for example, the VGG-Face2 and Dual-Agent GAN report nearly perfect and better-than-human performances on the IARPA Janus Benchmark A (IJB-A) benchmark. More challenging benchmarks, e.g., the IARPA Janus Benchmark A (IJB-C), QMUL-SurvFace and MegaFace, are accepted as new standards for evaluating the performance of a new approach. Such an evolution is also seen in other branches of face informatics. In this Special Issue, we have selected the papers that report the latest progresses made in the following topics: 1. Face liveness detection 2. Emotion classification 3. Facial age estimation 4. Facial landmark detection We are hoping that this Special Issue will be beneficial to all fields of facial informatics.

deep learning --- RGB --- depth --- facial landmarking --- merging networks --- 3D geometry data --- 2D attribute maps --- fused CNN feature --- coarse-to-fine --- convolutional neural network (CNN) --- deep metric learning --- multi-task learning --- image classification --- age estimation --- generative adversarial network --- emotion classification --- facial key point detection --- facial images processing --- convolutional neural networks --- face liveness detection --- convolutional neural network --- thermal image --- external knowledge