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