There are 2 repositories under scikit-image topic.
天池医疗AI大赛[第一季]:肺部结节智能诊断 UNet/VGG/Inception/ResNet/DenseNet
A collection of all projects pertaining to different layers in the SDC software stack
AiAi.care project is teaching computers to "see" chest X-rays and interpret them how a human Radiologist would. We are using 700,000 Chest X-Rays + Deep Learning to build an FDA 💊 approved, open-source screening tool for Tuberculosis and Lung Cancer. After an MRMC clinical trial, AiAi CAD will be distributed for free to emerging nations, charitable hospitals, and organizations like WHO 🌏 We will also release our pretrained models and weights as Medical Imagenet.
Image Processing with Python
A self-explanatory, hands-on intro to bioimage analysis in python. Slightly outdated but still much liked by learners.
High-level API for attractive and descriptive image visualization in Python
A program to align rotated id cards and extract user data from it.
In general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features. Learning problems fall into a few categories: supervised learning, in which the data comes with additional attributes that we want to predict (Click here to go to the scikit-learn supervised learning page).This problem can be either: classification: samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data. An example of a classification problem would be handwritten digit recognition, in which the aim is to assign each input vector to one of a finite number of discrete categories. Another way to think of classification is as a discrete (as opposed to continuous) form of supervised learning where one has a limited number of categories and for each of the n samples provided, one is to try to label them with the correct category or class. regression: if the desired output consists of one or more continuous variables, then the task is called regression. An example of a regression problem would be the prediction of the length of a salmon as a function of its age and weight. unsupervised learning, in which the training data consists of a set of input vectors x without any corresponding target values. The goal in such problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization (Click here to go to the Scikit-Learn unsupervised learning page).
Play around with Pixel in Python
A simple machine learning powered captcha breaker
A versatile, fully open-source pipeline to extract phenotypic measurements from plant images
This repository is mainly about comparing two images. The technique used is SSIM. i.e. Structural Similarity Index Measure We use some of the inbuilt functions available in python's skimage library to measure the SSIM value. Along with SSIM we also measure the MSE ( Mean Square Error ) To know more about the SSIM technique Refer Here: https://en.wikipedia.org/wiki/Structural_similarity
Simple linear iterative clustering (SLIC) in a region of interest (ROI)
Image processing examples with Numpy, Scipy, and Scikit-image
Open-source Platform for Scientific and Technical Data Processing and Visualization
Exercises, data and other material for the DTU course 02502 Image Analysis
Segmentation of Blood Vessels from 3D Medical Image
Object detector from HOG + Linear SVM framework
Application to detect the similarity of two signatures.
A Python package to segment cluttered 2D floor plans based on down-sampling.
See all scikit-image methods for image creation & manipulation and their output at a glance.
Powerful image compression algorithm based on quadtrees
The Indus script optical grapheme recognition engine (from archaeological artifact images)
For correcting / flattening wide-angle image distortion.
Classification of automotive parts as defective and non-defective with transfer learning.
An image analysis tool for measuring microorganism colony growth
Patch-based inpainting Python library
segment a set of rasters using rasterio and skimage
Recognise and Learn American Sign Language