100 Colab's for Computer Vision
100 Google colab's for image processing, pattern recognition and computer vision
by
Domingo Mery ,
Gabriel Garib ,
Christian Pieringer ,
Sebastian Pulgar ,
Javier Tramon
How to read color images (+)
Introductory Example: Rice segmentation (+)
Sampling (spatial and grayscale sampling) (+)
Basic color segmentation (+)
+ Image Processing in Spatial Domain
Geometric Transformation (+)
Equalization of Histogram (+)
Filtering with Masks (kernels) (+)
+ Image Processing in Frequency Domain
Erosion, Dilation, Opening, Closing, Skeletization, Filling-Holes, Gradient (+)
Top Hat Filter (+)
Median Filter (+)
Region Growing (+)
Region Detection using OTSU, MSER, etc. (+)
See Segmentation in Module [ Deep Learning > Segmentation ]
Edge Detection (+)
Hough Transform (+)
Watershed (+)
Motion Segmentation (+)
Color Segmentation using K-means (+)
Color Segmentation using High Contrast Images (+)
Color Image Enhancement (+)
(+) Tested in July, 2023
+ Feature Extraction
Geometric features (basic, elliptical and moments) (+)
Fourier Descriptors (+)
Intensity Features (basic, contrast and Crossing Line Profile) (+)
Face Recognition using Local Binary Patterns (LBP) (+)
Texture Recognition using LBP, Haralick and Gabor features (+)
Pedestrian Detection using Histogram of Gradients (HoG) (+)
Cow Biometrics using SIFT (+)
+ Feature Selection and Transformation
5-Character Recognition using Sequential Forward Selection (SFS) (+)
Basic Feature Selection and Transformation: SFS, Exhaustive Search, PCA, PLSR, ICA, etc. (+)
Face Recognition with LBP using PCA, ICA and PLSR (+)
Collection of Methods for Visualization of Feature Space (+)
Basic Classifiers (KNN, Bayes, LDA, QDA, Mahalanobis) (+)
Setting Hyperparameter K of KNN using Validation Dataset (+)
Neural Networks from scratch (+)
Neural Networks (using sklearn) (+)
Support Vector Machines (SVM) (+)
Exhaustive Search of the Best Classifier (+)
Neural Networks for MNIST dataset (+)
See Bag of Visual Words [ Pattern Recognition > Clustering ]
(+) Tested in July 2023
Geometry in Commputer Vision
Points and lines in homogeneous coordinates (*)
Pose orientation of a face using Landmarks (*)
Parameter estimation with and without outliers (RANSAC) (*)
+ Geometric Transformations
(*) Updated in July-2023, some of them in Spanish
Deep learning in Computer Vision
Classification of Eyes and Noses (two classes) (+)
Defect detection in aluminum wheels (two classes) (+)
Skin Lesion Recognition (two classes) (+)
Classification of Dogs and Cats (two classes) (+)
Covid recognition in Lung X-ray images (three classes) (+)
Classification of Pedestrians with Bikes (three classes) (+)
Skin Lesion Recognition (seven classes) (+)
ARLNET (Attention Residual Learning blocks) (+)
EfficientNet (+)
Visual Transformers (from library HuggingFaces) (+)
Classification examples using pre-trained mmodels (ResNet*, VGG*, ShuffleNet*, etc.) (+)
(+) Tested in July, 2023
Face Detection using MTCNN (+)
Face Recognition (1:1) with ArcFace (face verification) (*)
Face Recognition (1:1) with AdaFace (face verification) (+)
Face Recognition (1:N) with AdaFace (face recognition) (+)
Searching a Face in a Gallery with AdaFace (+)
Face Clustering (+)
Age and Gender Recognition (*)
Face Expression Recognition (+)
Landmark Detection (68 face landmarks) (+)
Face Geometric Mesh (+)
Estimation of Head Pose from Face Landmarks (+)
Mask Detection in Face using YOLOv5 (+)
Eye and Mouth Detection using YOLOv5 (+)
Restoration of Image Faces using GFPGAN (+)
Face Analysis Explanation (Minus, Plus, AVG, SEQ, LIME, RISE) (+)
(+) Tested in July, 2023
(*) There are some version troubles :(
Detection of Eye and Mouth using YOLOv5
Defect Detection in Aluminum Castings using YOLOv5
Detection of Threat Objects in Baggage using YOLOv5
General Object Detection using YOLOv5 (without training)
OCR - Optical Chracter Recognition (OCR) using pytesseract
Tracking of Multiple Objects in Videos using YOLOv5
+ Segmentation (using Deep Learning)
Segmentation of Skin Lesions using UNet
+ Generative adversarial network (GAN)
Basic GAN for Digits Generation using MNIST
DCGAN for Digits Generation using MNIST
SN-GAN for Digits Generation using MNIST
WGAN-GP for Digits Generation using MNIST
DCGAN for Generation of X-ray images of Shuriken (64x64 pixeles)
Anomaly Detection using MNIST(0: normal class, 1,2,..9: anomaly)
Contrastive Learning in CIFAR dataset