whdcumt

whdcumt's Stars

• GokuMohandas/Made-With-ML

  Learn how to design, develop, deploy and iterate on production-grade ML applications.

  Language:Jupyter Notebook37.9k1.2k736k

• MorvanZhou/tutorials

  机器学习相关教程

  Language:Python12k665675.7k

• rasmusbergpalm/DeepLearnToolbox

  Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.

  Language:Matlab3.8k4671382.3k

• divamgupta/image-segmentation-keras

  Implementation of Segnet, FCN, UNet , PSPNet and other models in Keras.

  Language:Python2.9k613471.2k

• PaddlePaddle/book

  Deep Learning 101 with PaddlePaddle （『飞桨』深度学习框架入门教程）

  Language:Jupyter Notebook2.7k2803001.3k

• ShawnBIT/UNet-family

  Paper and implementation of UNet-related model.

  Language:Python2.5k746504

• WXinlong/SOLO

  SOLO and SOLOv2 for instance segmentation, ECCV 2020 & NeurIPS 2020.

  Language:Python1.7k32239307

• orobix/retina-unet

  Retina blood vessel segmentation with a convolutional neural network

  Language:Python1.3k7983468

• Eatzhy/surface-defect-detection

  缺陷检测文献记录

  7642511178

• YuanhaoGong/CurvatureFilter

  Curvature Filters are efficient solvers for Variational Models

  Language:C++386437106

• qinnzou/DeepCrack

  DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection

  Language:Python26132960

• blakeliu/awesome-cell-detection-segmentation

  nucleus/cell and histopathology image classification,detection,segmentation

  25413053

• petercorke/machinevision-toolbox-matlab

  Machine Vision Toolbox for MATLAB

  Language:MATLAB207181997

• theWorldCreator/LSD

  a Line Segment Detector

  Language:C1418237

• LmYjQ/AI_for_everyone

  Language:Jupyter Notebook1355129

• nicolasmetallo/car-damage-detector

  Detect dents and scratches in cars. Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow.

  Language:Jupyter Notebook1309852

• lipiji/PG_Curve

  Matlab code for computing and visualization: Confusion Matrix, Precision/Recall, ROC, Accuracy, F-Measure etc. for Classification.

  Language:Objective-C926094

• mhaghighat/gabor

  Gabor Feature Extraction

  Language:Matlab699233

• ab93/SIFCM

  Image Segmentation using Spatial Intuitionistic Fuzzy C Means Clustering

  Language:Python303218

• aviralchharia/COVID-19

  Research Project for Detection of COVID-19 from X-Ray using Deep Learning methods. Implemented convolutional neural network for classification of X-Ray Images into COVID & non-COVID cases.

  Language:Jupyter Notebook10202

• karanchauhan/Image-Segmentation

  RGB and hyperImage segmentation and evalutation via KMeans, Fuzzy C Means, Self Organizing Map, Spectral Clustering and Gaussian Mixture Models

  Language:MATLAB9207

• liamheng/QGFD

  Quaternion generic Fourier descriptor for color object recognition

  Language:Matlab6104

• taoliq/RubikCubeRobot

  Rubik's Cube Robot using android and ardruino

  Language:Java6101

• liamheng/Deep-learning-on-cell-classification

  Deep learning models for cell classification

  Language:Python4203

• vishnu1729/Hu-s-Invariant-Moments

  Calculates the seven invariant Hu's moment of an image

  Language:MATLAB43

• pitsios-s/ImageSegmentation

  An image segmentation project, using clustering algorithms.

  Language:Matlab3202

• PJ17101/Identifying-Interstitial-Lung-Diseases-using-Image-Processing-

  To design a system that retrieves similar images using the image features such as the Gabor Wavelet and Discrete Wavelet transform. The system is basically built for identifying lung diseases using the CT scans of the lungs, the existing CT scan images of the diseases are taken into the database and the feature extraction process is done upon them the values are stored in a matrix and then in the end these values of database images are compared with the query image and by using the similarity metric (manhattan distance), the most relative image is retrieved.

  Language:Matlab21

• whdcumt/Texture-Features-Based-Image-Retrieval-in-DWT-Domain

  we have presented a Content Based Image Retrieval (CBIR) scheme using color, texture and shape feature information. Firstly an input RGB image is converted into YCbCr image and each part of the i.e. Y, Cb and Cr are extracted from it. Afterword, each components are uniformly quantized. Then BDIP and BVLC are computed over quantized Y-component on block size of 2×2 and receive respective BDIP and BVLC image. Then on these two received image 3-level dwt is implemented and on each sub band some statistical parameters are evaluated to form a part of a feature vector. Now, on extracted quantized Cb and Cr components, 2-level dwt is performed and on each sub band some statistical parameters are calculated and this form second part of the feature vector. Now both parts are concatenated to form final feature vector. To proof that our system is adequate to retrieve good results, we have tested our scheme on benchmark database Coral-1000 . Same processed has been taken place for all the images present in the database and on the basis of the similarity measurement Top-20 results are retrieved and stored and the results are quite satisfying.

  Language:MATLAB2202

• sergiud/elpv-dataset

  A dataset of functional and defective solar cells extracted from EL images of solar modules

  Language:Python120

• whdcumt/machine-learning-notes

  This contains my past machine learning notes

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