Tikiten

Tikiten's Stars

• extreme-assistant/CVPR2024-Paper-Code-Interpretation

  cvpr2024/cvpr2023/cvpr2022/cvpr2021/cvpr2020/cvpr2019/cvpr2018/cvpr2017 论文/代码/解读/直播合集，极市团队整理

  12.4k371842.3k

• zotero-chinese/styles

  中文 CSL 样式

  Language:XML5.1k17324830

• ericzzj1989/Awesome-Image-Matching

  1601115

• LJY-RS/RIFT-multimodal-image-matching

  Language:MATLAB994319

• antofuller/SatViT

  Project directory for self-supervised training of multi-spectral optical and SAR vision transformers!

  Language:Python65728

• danfenghong/SpectralMamba

  Yao, Jing, Danfeng Hong, Chenyu Li, and Jocelyn Chanussot. "SpectralMamba: Efficient Mamba for Hyperspectral Image Classification." arXiv preprint arXiv:2404.08489, 2024.

  55260

• chenning0115/SpectralDiff

  Language:Python51283

• LiaoYun0x0/Feature-Matching-and-Position-Matching-between-Optical-and-SAR

  Using Deep learning to locate the Synthetic Aperture Radar(SAR) images to the counterpart of Optical images.

  Language:Python511108

• yeyuanxin110/HOPC

  HOPC matching code

  Language:MATLAB483214

• LJY-RS/SRIF

  Language:C35134

• YZCU/OOTB

  [ISPRS 2024] Satellite Video Single Object Tracking: A Systematic Review and An Oriented Object Tracking Benchmark

  Language:C29100

• WenliangDu/Semi-I2I

  Source codes of "A Semi-supervised Image-to-Image Translation Architecture for SAR-Optical Image Matching"

  Language:Python25213

• YZCU/SENSE

  [INF FUS 2024] SENSE: Hyperspectral Video Object Tracker via Fusing Material and Motion Cues

  Language:Python24120

• AmitHasanShuvo/Prediction-of-Clinical-Risk-Factors-of-Diabetes-Using-ML-Resolving-Class-Imbalance

  Being the most common and rapidly growing disease, Diabetes affecting a huge number of people from all span of ages each year that reduces the lifespan. Having a high affecting rate, it increases the significance of initial diagnosis. Diabetes brings other complicated complications like cardiovascular disease, kidney failure, stroke, damaging the vital organs etc. Early diagnosis of diabetes reduces the likelihood of transiting it into a chronic and severe state. The identification and analysis of risk factors of different spinal attributes help to identify the prevalence of diabetes in medical diagnosis. The prevalence measure and identification of diabetes in the early stages reduce the chances of future complications. In this research, the collective NHANES dataset of 1999-2000 to 2015-2016 was used and the purposes of this research were to analyze and ascertain the potential risk factors correlated with diabetes by using Logistic Regression, ANOVA and also to identify the abnormalities by using multiple supervised machine learning algorithms. Class imbalance, outlier problems were handled and experimental results show that age, blood-related diabetes, cholesterol and BMI are the most significant risk factors that associated with diabetes. Along with this, the highest accuracy score .90 was achieved with the random forest classification method.

  Language:Jupyter Notebook21308

• Pyxel0524/HOPC-Optical-to-SAR-registration

  HOPC algorithom for Optical and SAR images registration (IEEE TGRS 2017) implemented by python

  Language:Python18101

• YZCU/REPS

  [JAG 2024] REPS: Rotation Equivariant Siamese Network Enhanced by Probability Segmentation for Satellite Video Tracking

  Language:MATLAB18101

• YZCU/DF

  [IEEE JSTARS 2022] Single Object Tracking in Satellite Videos: A Correlation Filter-Based Dual-Flow Tracker

  14100

• WenliangDu/FM-CycleGAN

  Source codes of "Exploring the Potential of Unsupervised Image Synthesis for SAR-Optical Image Matching" IEEE Access

  Language:Python13212

• WenliangDu/KCGGAN

  Source codes of "K-Means Clustering Guided Generative Adversarial Networks for Automatic SAR-Optical Image Matching" IEEE Access

  Language:Python13221

• MicheleGazzea/Sar_opt_matching

  Code for MARU-Net: Multi-Scale Attention Gated Residual U-Net With Contrastive Loss for SAR-Optical Image Matching, published in https://ieeexplore.ieee.org/abstract/document/10129005

  Language:Python101

• MuhammedM294/SAR2Optical

  This repository hosts a collection of experiments aimed at testing the effectiveness of transcoding Synthetic Aperture Radar (SAR) imagery to optical ones. The focus of these experiments is on solving the challenge of waterbody extraction in arid regions posed by the similarity of the intensity values of waterbodies and sand landcover in SAR image

  Language:Python10102

• zhaolin6/HybridFormer

  Language:Python10131

• YZCU/YZCU.github.io

  Language:HTML9100

• yyb1234-56/FED-HOPC

  The Matlab code for FED-HOPC, a region aware phase descriptor for fast and robust Optical-to-SAR Remote Sensing Image Registration

  Language:MATLAB7101

• jll-maker/OSMNet

  Trained model and test code for high resolution optical and SAR image matching

  Language:Python60

• ZhaohuiXue/SPFormer

  A DEMO for "SPFormer: Self-Pooling Transformer for Few-Shot Hyperspectral Image Classification" (Li et al., TGRS 2024)

  Language:Python6200

• 3M-OS/3MOS

  Multi-sources, Multi-resolution, and Multi-scene dataset for Optical-SAR image matching

  5230

• BorseGaurav95/Diabetes_Prediction_Website

  The first year after diagnosis is a crucial time for patients with Type 2 diabetes. While it’s always important to maintain healthy blood sugar levels, new research shows that better control during the first year can reduce the future risk for complications, including kidney disease, eye disease, stroke, heart failure and poor circulation to the limbs. Diabetes, often referred to by doctors as diabetes mellitus, describes a group of metabolic diseases in which the person has high blood glucose (blood sugar), either because insulin production is insufficient, or because the body's cells do not respond properly to insulin, or both. This project helps in identifying whether a person has diabetes or not, if predicted diabetic the project suggests measures for maintaining normal health and if not, diabetic it predicts the risk of getting diabetic. In this project Classification algorithm was used to classify the Pima Indian diabetes dataset. Results have been obtained using Web Application.

  Language:Jupyter Notebook520

• rookie-YIFAN/CMT

  Implementation of [A Center-masked Transformer for Hyperspectral Image Classification]

  Language:Python422

• Xxin08/ASS

  MATLAB demo code of ASS algorithm for Optical and SAR Image Registration

  Language:MATLAB4100