Lectures related to Image Processing Basics

Image Processing in Python: Manipulation and Convolution Techniques

Introduces image processing techniques in Python, focusing on manipulation and convolution operations using NumPy and Pillow.

Image Processing: Neighborhood Averaging & Gaussian Smoothing

Explores neighborhood averaging, Gaussian smoothing, median filtering, contrast enhancement, and edge detection in image processing.

Image Filtering: Classic Filters

Explores classic image filters like Moving Average, Gaussian Blur, Sobel filter, and Laplacian operator.

Image Processing: Enhancement and Analysis

Explores image processing techniques such as contrast manipulation, enhancement, and sharpening using Fiji software.

Computer Vision Basics: Image Processing and Feature Detection

Covers the basics of computer vision, focusing on image processing techniques and feature detection.

Image Processing Techniques

Covers image processing techniques including noise addition, filtering, and image enhancement using various filters and tools.

Image Processing: Basics and Techniques

Introduces image processing basics, covering vector vs matrix images, RGB color system, grayscale, and image manipulation libraries.

Mercer Theorem and Kernels

Explores the Mercer Theorem, Kernels, and their role in machine learning applications.

Image Processing: Smoothing and Filtering

Covers image smoothing, noise reduction, and segmentation techniques using filters and transforms.

Image Processing I: Edge Detection and Texture Analysis

Covers edge detection, texture analysis, Canny's algorithm, filterbanks, and segmentation techniques.

Kernel Regression: Basics and Applications

Explores kernel regression, the curse of dimensionality, and random features in neural networks.

Data Science Visualization with Pandas

Covers data manipulation and exploration using Python with a focus on visualization techniques.

Image Processing Fundamentals

Covers the basics of image processing for microscopy, including acquiring, correcting defects, enhancing images, and extracting information.

Edge and Contour

Covers edge and contour detection in images, including gradient-based methods, Laplacian operator, and more complex methods.

Feature Expansion: Kernels and KNN

Covers feature expansion, kernels, and K-nearest neighbors, including non-linearity, SVM, and Gaussian kernels.

Deep Learning: Edge Detection and Neural Networks

Discusses edge detection techniques and the evolution of deep learning in neural networks.

Image Processing in Fiji

Covers the basics of image processing using Fiji software, focusing on managing files and applying filters.

X-ray Tomography Practical Analysis

Covers the practical analysis of X-ray tomography images using Fiji software.

Kernels: Nonlinear Transformations

Explores kernels for simplifying data representation and making it linearly separable in feature spaces, including popular functions and practical exercises.

Linear Algebra in 3D: Images and Kernels

Explores linear applications in 3D, emphasizing images, kernels, and solution uniqueness in systems.