50 skills found · Page 1 of 2
xinntao / SFTGANCVPR18 - Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform
helsing-ai / SguabaA Rust crate that provides hard-to-misuse rigid body transforms (aka "spatial math") for engineers with other things to worry about than linear algebra.
VedantC2307 / Ros2 Mobile Sensor BridgeMobile Sensor Bridge for ROS2 transforms your android smartphone into a plug‑and‑play sensor suite—streaming camera, spatial pose data, and bidirectional audio into ROS2 topics via rclnodejs. Whether you’re prototyping perception pipelines or building voice‑driven robots, the package lets you leverage your phone’s sensors without extra hardware.
Utkarsh-Deshmukh / Spatially Varying Blur Detection Pythonpython implementation of the paper "Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes" - cvpr 2017
brunopostle / Homemaker AddonDesign buildings the pointy-clicky way, a blender add-on that transforms spatial geometry into IFC building models
micmic123 / QmapCompressionOfficial implementation of "Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform", ICCV 2021
Robbings / Chatgpt Graph NavigatorTransform ChatGPT into a navigable knowledge graph. Visualize complex branches with the spatial Graph View, manage history via the Git-style Timeline Tree, and enjoy a growing toolkit of workflow utilities.
rakutentech / StAdvSpatially Transformed Adversarial Examples with TensorFlow
isalirezag / HiFSTSpatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes
angrysky56 / Ast MCP ServerBy transforming source code into a queryable Semantic Graph and a structured AST, this tool bridges the gap between "reading text" and "understanding structure." For an AI assistant, it provides the "spatial" awareness needed to navigate deep dependencies without getting lost in large files.
noaa-ngs / HTDPHorizontal Time-Dependent Positioning (HTDP) is a utility that allows users to transform positional coordinates across time and between spatial reference frames.
Beckybams / AI Powered Climate Model DownscalingAI-Powered Climate Model Downscaling uses machine learning to transform low-resolution climate model outputs into high-resolution local climate predictions. By generating and training on synthetic climate data, the system improves spatial detail and accuracy.
ayberkydn / Stadv TorchPyTorch implementation of Spatially Transformed Adversarial Examples
clintonjwang / Spatial Intensity TransformsHigh Fidelity Medical Image-to-Image Translation with Spatial-Intensity Transforms
ma-tech / WoolzWoolz is a set of software libraries and executables for image processing.
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.
jhkim89 / Saliency HDCTMatlab Implementation of the paper "Salient Region Detection via High-Dimensional Color Transform and Local Spatial Support"
mhy9989 / CFD CNNA Flow Field Prediction Program & Multiple Spatio_temporal Attention (MSTA) Network & Multiple Fusion Attention (MFA) & Spatial Transform Gradient Sharpening (STGS) (based on OpenSTL & DeepSpeed)
JinyuTian / SIDThe code of our AAAI 2021 paper "Detecting Adversarial Examples from Sensitivity Inconsistency of Spatial-transform Domain"
zhangjue1993 / A New Saliency Driven Fusion Method Based On Complex Wavelet Transform For Remote Sensing Imagesa new saliency-driven image fusion method based on complex wavelet transform for remote sensing images is proposed to satisfy different needs of spatial and spectral resolution for different regions
