Image Segmentation: K-means and Color Spaces

Related lectures (30)

Page 1 of 3

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

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

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

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

Compares random and symmetrical graph coloring in terms of cluster colorability and equilibrium.

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

Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.

Delves into various color spaces, Color Deconvolution, and analyzing Whole Slide Images.

Explores advanced Adobe color settings and PDF optimization techniques.

Covers the energy and radiation of LEDs, transitions in energy levels, and color synthesis.