Get Free Shipping on orders over $79
Image Processing and Machine Learning, Volume 2 : Advanced Topics in Image Analysis and Machine Learning - Erik Cuevas

Image Processing and Machine Learning, Volume 2

Advanced Topics in Image Analysis and Machine Learning

By: Erik Cuevas, Alma Nayeli Rodríguez

Hardcover | 16 February 2024 | Edition Number 1

At a Glance

Hardcover


RRP $183.00

$162.75

11%OFF

or 4 interest-free payments of $40.69 with

 or 

Ships in 3 to 5 business days

Image processing and machine learning are used in conjunction to analyze and understand images. Where image processing is used to pre-process images using techniques such as filtering, segmentation, and feature extraction, machine learning algorithms are used to interpret the processed data through classification, clustering, and object detection. This book serves as a textbook for students and instructors of image processing, covering the theoretical foundations and practical applications of some of the most prevalent image processing methods and approaches.

Divided into two volumes, this second installment explores the more advanced concepts and techniques in image processing, including morphological filters, color image processing, image matching, feature-based segmentation utilizing the mean shift algorithm, and the application of singular value decomposition for image compression. This second volume also incorporates several important machine learning techniques applied to image processing, building on the foundational knowledge introduced in Volume 1.

Written with instructors and students of image processing in mind, this book's intuitive organization also contains appeal for app developers and engineers.

More in Artificial Intelligence

What Art Is Now : Creativity in the Age of AI - Michael E. Jones
Agentic AI For Dummies : For Dummies (Computer/Tech) - Pam Baker
AI for Business : A Guide to AI Adoption - Jon Whittle

RRP $49.99

$40.75

18%
OFF
Bandit Convex Optimisation - Tor Lattimore

RRP $99.95

$89.75

10%
OFF
AI Engineering : Building Applications with Foundation Models - Chip Huyen
Handbook of Reinforcement Learning - Todd Mcmullen