27 skills found
xinluo2018 / WatNetA deep learning model for surface water mapping based on satellite optical image.
nasa / SALaDSALaD (Semi-Automatic Landslide Detection) is a landslide mapping system. SALaD utilizes Object-based Image Analysis and Random Forest to map landslides. It requires optical imagery, a DEM, corner coordinates of a training area, and manually mapped landslides within the training area. The code is built to run primarily on a Linux.
Hazrat-Ali9 / Continual Domain Adaptation For Flood Mapping Via SAR Optical Fusion And Test Time Adaptation🍊 Continual 🍎Domain 🍏 Adaptation 🫑 Flood 🌽 Mapping 🥔 SAR 🥯 Optical 🥡 Fusion 🍱 Test Time 🥮 Adaptation 🎂 is an advanced 🍔 deep ✈ learning 🚀 framework 🚁 that 🛸 leverages 🛫 optical 🚞 image 🚢 fusion 🚃 continual 🚟 domain 🚠 adaptation ⛱ to achieve 🛺 real time 🛼 flood mapping 🚒 across diverse geographies seasons and disaster events
deep6000 / Embedded Machine Vision And Intelligent AutomationAn introductory course on machine vision and related machine learning used in automation, autopilots, security and inspection systems. Topics covered include theory of computer and machine vision and related algorithms for image capture and processing, filtering, thresholds, edge detection, shape analysis, shape detection, salient object detection, pattern matching, digital image stabilization, stereo ranging, and methods of sensor and information fusion. Machine vision sensors covered include visible to long-wave infrared including passive EO/IR (Electro-Optical/Infrared) as well as active methods such as RGB depth mapping and LIDAR. Embedded and automation topics covered include implementation of these algorithms with FPGA or GP-GPU embedded real-time vision systems for autopilots (intelligent transportation), general machine vision automation and security including methods for detection, classification, recognition of targets, and applications including inspection, surveillance, search and rescue, and machine vision navigation.
Ling-Bao / Papers With CodeLatest papers or codes collection about Localization, Mapping, SLAM(Simultaneous Localization and Mapping), Deepth estimation and Visual Odometry, Optical Flow Estimation, Deep learning theory, Machine Learning theory etc. Including like NeurIPS, CVPR, ICCV, ECCV, IROS, ICRA etc.
wen-biao / OM HiC ScaffoldingWorkflows for correcting and scaffolding long-read (PacBio, nanopore) genome assemblies using optical mapping and/or Dovetail Hi-C data
WUSTL-ORL / NeuroDOTOfficial release of NeuroDOT, an extensible Matlab toolbox for efficient optical brain mapping
AmpliconSuite / AmpliconReconstructorOMReconstructs complex variation using Bionano optical mapping data and breakpoint graph data
JamesGlare / Holo Gen ModelsHolographic wave-shaping has found numerous applications across the physical sciences, especially since the development of digital spatial-light modulators (SLMs). A key challenge in digital holog- raphy consists in finding optimal hologram patterns which transform the incoming laser beam into desired shapes in a conjugate optical plane. The existing repertoire of approaches to solve this inverse problem is built on iterative phase-retrieval algorithms, which do not take optical aberrations and deviations from theoretical models into account. Here, we adopt a physics-free, data-driven, and probabilistic approach to the problem. Using deep conditional Generative-Adversarial-Networks (cGAN) and conditional Variational Autoencoder (cVAE) architectures, we approximate posterior distributions of holograms for a given target laser intensity pattern. In order to reduce the cardinality of the problem, we train our models on a proxy mapping relating an 8 × 8-matrix of complex-valued spatial-frequency coefficients to the ensuing 100 × 100-shaped intensity distribution recorded on a camera. We discuss the degree of ’ill-posedness’ that remains in this reduced problem and challenge our generative models to find holograms that reconstruct given intensity patterns. Finally, we study the ability of the models to generalise to synthetic target intensities, where the existence of matching holograms cannot be guaranteed. We devise a forward-interpolating training scheme aimed at provid- ing models the ability to interpolate in laser intensity space, rather than hologram space and show that this indeed enhances model performance on synthetic data sets.
FadyMohareb / MapopticsMapOptics is a lightweight cross-platform tool that enables the user to visualise and interact with the alignment of Bionano optical mapping data and can be used for in depth exploration of hybrid scaffolding alignments.
Yi-Heng / EarthMissEarthMiss: A multimodal (Optical/SAR) land-cover dataset across 13 global cities + MetaRS code for missing-modality mapping.
WUSTL-ORL / NeuroDOT BetaA beta release of NeuroDOT, an extensible Matlab toolbox for efficient optical brain mapping. Official release located at: https://github.com/WUSTL-ORL/NeuroDOT.
wangyahgui / PDGANThis project first presents a coherent signal demodulation method based on generative adversarial networks (GAN), called a phase demodulation generative adversarial network (PDGAN). We applyed GAN method to the field of Doppler signal demodulation for laser voice detection.Demodulation of the Doppler signal from a coherent signal is accomplished through unsupervised learning within the PDGAN, layer by layer, with global supervised feedback learning for fine-tuning. Drawing upon the adversarial principle of GAN, we let the coherent signal, Z, serve as an input for G and let the generated demodulated Doppler signal G(Z) and the clean Doppler signal X serve as the input for D. By means of alternate training and optimization, G learns the mapping relationship between the coherent signal, Z, and the Doppler signal, X, thereby achieving the goal of demodulating the coherent signal. This project mainly includes related data sets of Doppler signal and corresponding coherent signal. The structure and detailed description of the network are planned to be published in the Journal of optical engineering. The author can also be contacted for information wangyahui@aoe.ac.cn
comediaLKB / Learning With Passive Optical Nonlinear MappingRepository for paper learning with passive optical nonlinear mapping
TF-Chan-Lab / OMBlastAn alignment tool for optical mapping data
TF-Chan-Lab / OMToolsA software package for optical mapping data processing, analysis and visualization
novosadt / Om Annotsv SvcOptical Mapping and AnnotSV Structural Variant Comparator
WUSTL-ORL / NeuroDOT PyRelease of NeuroDOT, an extensible Python toolbox for efficient optical brain mapping. Learn more: https://wustl-orl.github.io/NeuroDOT_py/index.html
jtmff / CosmasCOSMAS: a software toolkit for cardiac optical mapping data analysis (two equivalent implementations provided, one in Matlab, one in Python).
EskedarT / S1 S2 TransformerThis implementation uses Google Earth Engine Python API to process Sentinel-1 SAR and Sentinel-2 optical timeseries for tropical dry forest mapping. It also uses Tensorflow to build a siamese transformer architecture to implicitly learn the seasonality in the two series to make a forest and non-forest inference.
