Image Processing I: Segmentation and Thresholding

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Segmentation: Theory and Algorithms

Covers the theory and algorithms behind image segmentation, focusing on region identification and evaluation.

K-Means Clustering: Basics and Applications

Introduces K-Means Clustering, a simple yet effective algorithm for grouping data points into clusters.

K-means Clustering: Initialization and Image Segmentation

Explores k-means clustering, emphasizing initialization and image segmentation.

Segmentation Techniques

Explores segmentation techniques in image analysis, including thresholding, clustering, region growing, and machine learning.

K-means Clustering: Lloyd's Algorithm and RGB Space

Explains K-means clustering with Lloyd's algorithm and RGB space for color segmentation.

Clustering: Unsupervised Learning

Covers clustering algorithms, evaluation methods, and practical applications in machine learning.

Image Segmentation: K-means and Color Spaces

Explores image segmentation using K-means clustering and discusses the impact of different color spaces.

JPEG 2000: Image Compression

Explores image compression principles, focusing on JPEG 2000, covering transform-based coding, quantization, entropy coding, region of interest, error resilience, and software implementations.

Image Processing I: Edge Detection and Texture Analysis

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

Evaluating Clustering Quality

Explores the silhouette coefficient for evaluating cluster quality and other cluster evaluation measures.