Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

As climate-change- and human-induced floods inflict increasing costs upon the planet, both in terms of lives and environmental damage, flood monitoring tools derived from remote sensing platforms have undergone improvements in their performance and capabilities in terms of spectral, spatial and temporal extents and resolutions. Such improvements raise new challenges connected to data analysis and interpretation, in terms of, e.g., effectively discerning the presence of floodwaters in different land-cover types and environmental conditions or refining the accuracy of detection algorithms. In this sense, high expectations are placed on new methods that integrate information obtained from multiple techniques, platforms, sensors, bands and acquisition times. Moreover, the assessment of such techniques strongly benefits from collaboration with hydrological and/or hydraulic modeling of the evolution of flood events. The aim of this Special Issue is to provide an overview of recent advancements in the state of the art of flood monitoring methods and techniques derived from remotely sensed data.

mobile mapping system --- RRI model --- high-water marks --- inundation --- Northern Kyushu floods --- point clouds --- flood mapping --- temporary flooded vegetation (TFV) --- Sentinel-1 --- time series data --- Synthetic Aperture Radar (SAR) --- sentinel-1 --- SAR --- flood --- image classification --- clustering --- monsoon --- Philippines --- LiDAR --- geometric parameters --- levee stability --- overtopping --- Pearl River Delta --- CYGNSS --- flood detection --- Sistan and Baluchestan --- GNSS-R --- flood monitoring --- ALOS 2 --- multi-sensor integration --- multi-temporal inundation analysis --- Zambesi-Shire river basin --- image processing --- hydrology --- synthetic aperture radar --- n/a

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Photonics has had a decisive influence on recent scientific and technological achievements. It includes aspects of photon generation and photon–matter interaction. Although it finds many applications in the whole optical range of the wavelengths, most solutions operate in the visible and infrared range. Since the invention of the laser, a source of highly coherent optical radiation, optical measurements have become the perfect tool for highly precise and accurate measurements. Such measurements have the additional advantages of requiring no contact and a fast rate suitable for in-process metrology. However, their extreme precision is ultimately limited by, e.g., the noise of both lasers and photodetectors. The Special Issue of the Applied Science is devoted to the cutting-edge uses of optical sources, detectors, and optoelectronics systems in numerous fields of science and technology (e.g., industry, environment, healthcare, telecommunication, security, and space). The aim is to provide detail on state-of-the-art photonic technology for precision metrology and identify future developmental directions. This issue focuses on metrology principles and measurement instrumentation in optical technology to solve challenging engineering problems.

infrared thermometer --- mid-wave infrared --- indium arsenide antimony photodiode --- uncooled thermometer --- fibreoptic coupling --- chopper stabilised op-amp --- zero-drift pre-amplifier --- ammonia detection --- NH3 --- MOX sensors --- polymer sensors --- laser absorption spectroscopy --- CRDS --- CEAS --- MUPASS --- PAS --- HOT IR detectors --- HgCdTe --- P-i-N --- BLIP condition --- 2D material photodetectors --- colloidal quantum dot photodetectors --- low-light photodetectors --- fluorescence microscopy --- time-resolved fluorescence microscopy --- hybrid photodetector (HPD) --- single-molecule fluorescence detection --- fourier ptychography --- image classification --- deep learning --- neural network --- electro-optic modulator --- frequency modulation --- displacement measuring interferometer --- quantum cascade laser --- laser controller --- infrared modulator --- laser spectroscopy --- free space optics --- photonic metrology --- accuracy --- precision --- resolution --- FTIR --- absorption spectroscopy --- gas sensors --- optoelectronic sensors

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Using hyperspectral imaging (HSI) to exploit data has been found in a wide variety of applications. This reprint book only presents a small glimpse of it. Many other important applications using HSI which have emerged in data exploitation are not covered in this reprint book. For example, such applications may include water pollution and toxic waste in environmental monitoring, pesticide residual detection in food safety and inspection, plant and crop disease detection in agriculture, tumor detection and breast cancer detection in medical imaging, drug traffic in law enforcement, etc. Nevertheless, this reprint book provides many techniques which may find their ways in these applications as well.

Technology: general issues --- History of engineering & technology --- hyperspectral image few-shot classification --- deep learning --- meta-learning --- relation network --- convolutional neural network --- constrained-target optimal index factor band selection (CTOIFBS) --- hyperspectral image --- underwater spectral imaging system --- underwater hyperspectral target detection --- band selection (BS) --- constrained energy minimization (CEM) --- lightweight convolutional neural networks --- hyperspectral imagery classification --- transfer learning --- air temperature --- spatial measurement --- FTIR --- MWIR --- carbon dioxide absorption --- target detection --- coffee beans --- insect damage --- hyperspectral imaging --- band selection --- visualization --- color formation models --- multispectral image --- image fusion --- joint tensor decomposition --- anomaly detection --- constrained sparse representation --- hyperspectral imagery --- moving target detection --- spatio-temporal processing --- hyperspectral remote sensing --- image classification --- constraint representation --- superpixel segmentation --- multiscale decision fusion --- plug-and-play --- denoising --- nonlinear unmixing --- spectral reconstruction --- residual augmented attentional u-shape network --- spatial augmented attention --- channel augmented attention --- boundary-aware constraint --- atmospheric transmittance --- temperature --- emissivity --- separation --- midwave infrared --- hyperspectral images --- hyperspectral image super-resolution --- data fusion --- spectral-spatial residual network --- self-supervised training --- hyperspectral --- vegetation --- generative adversarial network --- data augmentation --- classification --- rice leaf blast --- hyperspectral imaging data --- deep convolutional neural networks --- fused features --- evolutionary computation --- heuristic algorithms --- machine learning --- unmanned aerial vehicles (UAVs) --- vegetation mapping --- upland swamps --- mine environment --- rice --- rice leaf folder --- hyperspectral image classification --- change detection --- self-supervised learning --- attention mechanism --- multi-source image fusion --- SFIM --- least square estimation --- spatial filter --- hyperspectral imaging (HSI) --- hyperspectral target detection --- hyperspectral reconstruction --- hyperspectral unmixing

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This Special Issue is intended to lay the foundation of AI applications focusing on oral health, including general dentistry, periodontology, implantology, oral surgery, oral radiology, orthodontics, and prosthodontics, among others.

machine learning --- artificial intelligence --- malocclusion --- diagnostic imaging --- active learning --- maxillary sinusitis --- convolutional neural network --- deep learning --- segmentation --- oral microbiota --- LEfSe --- PCoA --- alloprevotella --- prevotella --- core microbiota --- artificial neural networks --- oral cancer diagnosis --- oral cancer prediction --- pit and fissure sealants --- caries assessment --- visual examination --- clinical evaluation --- convolutional neural networks --- transfer learning --- deep learning network --- YOLOv4 --- mandibular third molar --- inferior alveolar nerve --- contact relationship --- panoramic radiograph --- deep learning methods --- caries diagnosis --- dental panoramic images --- radiography --- Fourier transform infrared spectroscopy --- FTIR imaging --- spectral biomarker --- multivariate analysis --- discriminant model --- oral squamous cell carcinoma --- oral epithelial dysplasia --- oral potentially malignant disorder --- risk stratification --- early oral cancer detection --- dentigerous cysts --- histopathology images --- image classification --- odontogenic keratocysts --- radicular cysts --- AI --- screening --- diagnosis --- dentistry --- ultrasonography --- tongue --- algorithm --- dysphagia --- impacted --- tooth --- detection --- neural networks --- proximal caries --- training strategy --- small dataset --- periapical radiograph --- X-ray --- tooth extraction --- oroantral fistula --- operative planning --- n/a