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Image Processing With Python

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Syllabus

16 - Understanding digital images for Python processing.
17 - Reading images in Python.
18 - Image processing using pillow in Python.
19 - image processing using scipy in Python.
20 - Introduction to image processing using scikit-image in Python.
21 - Scratch assay analysis with just 5 lines code in Python.
22 - Denoising microscope images in Python.
23 - Histogram based image segmentation in Python.
24 - Random Walker segmentation in Python.
25 - Reading Images, Splitting Channels, Resizing using openCV in Python.
26 - Denoising and edge detection using opencv in Python.
27 - CLAHE and Thresholding using opencv in Python.
28 - Thresholding and morphological operations using openCV in Python.
29 - Key points, detectors and descriptors in openCV.
30 - Image registration using homography in openCV.
32 - Grain size analysis in Python using a microscope image.
33 - Grain size analysis in Python using watershed.
34 - Grain size analysis in Python using watershed - multiple images.
35 - Cell Nuclei analysis in Python using watershed segmentation.
94 - Denoising MRI images (also CT & microscopy images).
95 - What is digital image filtering and image convolution?.
96 - What is Gaussian Denoising Filter?.
97 - What is median denoising filter?.
98 - What is bilateral denoising filter?.
99 - What is Non-local means (NLM) denoising filter?.
100 - What is total variation (TV) denoising filter?.
101 - What is block matching and 3D filtering (BM3D)?.
102 - What is unsharp mask?.
103 - Edge filters for image processing.
104 - Ridge Filters to detect tube like structures in images.
105 - What is Fourier Transform?.
106 - Image filters using discrete Fourier transform (DFT).
112 - Averaging image stack in real and DCT space for denoising.
113 - Histogram equalization and CLAHE.
114 - Automatic image quality assessment using BRISQUE.
115 - Auto segmentation using multi-otsu.
Effect of Social Distancing on the spread of COVID-19 pandemic - A quick Python simulation.
107 - Analysis of COVID-19 data using Python - Part 1.
108 - Analysis of COVID-19 data using Python - Part 2.
109 - Predicting COVID-19 cases using Python.
110 - Visualizing COVID-19 cases & death information using Python and plotly.
111 - What are the top 10 countries with highest COVID-19 cases and deaths?.
116 - Measuring properties of labeled / segmented regions.
117 - Shading correction using rolling ball background subtraction.
118 - Object detection by template matching.
119 - Sub-pixel image registration in Python.
123 - Reference based image quality metrics.
124 - Image quality by estimating sharpness.
146 - Raspberry Pi - Learning python and deep learning on a tight budget.
182 - How to batch process multiple images in python?.
183 - OCR in python using keras-ocr.
191 - Measuring image similarity in python.
192 - Working with 3D and multi-dimensional images in python.
199 - Detecting straight lines using Hough transform in python.
200 - Image classification using gray-level co-occurrence matrix (GLCM) features and LGBM classifier.
201 - Working with geotiff files using rasterio in python (also quick demo of NDVI calculation).
202 - Two ways to read HAM10000 dataset into python for skin cancer lesion classification.
203 - Skin cancer lesion classification using the HAM10000 dataset.
204 - U-Net for semantic segmentation of mitochondria.
205 - U-Net plus watershed for instance segmentation.
206 - The right way to segment large images by applying a trained U-Net model on smaller patches.
207 - Using IoU (Jaccard) as loss function to train U-Net for semantic segmentation.
208 - Multiclass semantic segmentation using U-Net.
209 - Multiclass semantic segmentation using U-Net: Large images and 3D volumes (slice by slice).
210 - Multiclass U-Net using VGG, ResNet, and Inception as backbones.
69 - Image classification using Bag of Visual Words (BOVW).
211 - U-Net vs LinkNet for multiclass semantic segmentation.
212 - Classification of mnist sign language alphabets using deep learning.
213 - Ensemble of networks for improved accuracy in deep learning.
214 - Improving semantic segmentation (U-Net) performance via ensemble of multiple trained networks.
215 - 3D U-Net for semantic segmentation.
216 - Semantic segmentation using a small dataset for training (& U-Net).
218 - Difference between UpSampling2D and Conv2DTranspose used in U-Net and GAN.
219 - Understanding U-Net architecture and building it from scratch.
220 - What is the best loss function for semantic segmentation?.
221 - Easy way to split data on your disk into train, test, and validation?.
222 - Working with large data that doesn't fit your system memory - Semantic Segmentation.
223 - Test time augmentation for semantic segmentation.
224 - Recurrent and Residual U-net.
225 - Attention U-net. What is attention and why is it needed for U-Net?.
226 - U-Net vs Attention U-Net vs Attention Residual U-Net - should you care?.
227 - Various U-Net models using keras unet collection library - for semantic image segmentation.
228 - Semantic segmentation of aerial (satellite) imagery using U-net.
229 - Smooth blending of patches for semantic segmentation of large images (using U-Net).
230 - Semantic Segmentation of Landcover Dataset using U-Net.
231 - Semantic Segmentation of BraTS2020 - Part 0 - Introduction (and plan).

Taught by

DigitalSreeni

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