Learning Opencv 5 Computer Vision With Pythonfourth Edition
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Image Processing and Computer Vision Masterclass with Python
DESCRIPTION Image processing and computer vision technologies, combined with the rapid advancements in generative AI, have become foundational to many modern applications. As visual data continues to grow exponentially, the ability to analyze, interpret, and generate images using advanced algorithms and AI is more critical than ever for driving innovation across industries. This book provides a thorough exploration of advanced techniques and practical implementations in the field of computer vision. This book offers a problem-oriented approach that bridges traditional image processing with modern machine learning and generative AI methods. This new edition significantly expands into specialized domains with medical imaging applications using professional libraries like pydicom, ITK, and nnUNet for clinical diagnosis, including COVID-19 detection and brain tumor segmentation, plus remote sensing analysis with satellite processing. By the end of this book, readers will have developed strong practical skills in both classical and cutting-edge image processing and computer vision techniques, empowered to confidently design, implement, and adapt solutions across a wide range of real-world applications. They will emerge with a deep understanding of theory, hands-on coding experience, and the ability to leverage AI and generative models to push the boundaries of visual computing. WHAT YOU WILL LEARN ● Restore and enhance images using classical and deep learning methods. ● Segment images with advanced clustering and neural network techniques. ● Extract and match features for image alignment and recognition. ● Build and train image classifiers with ML and AI. ● Learn advanced restoration and inpainting techniques using cutting-edge deep learning models. ● Explore specialized domain expertise in medical imaging applications using professional libraries. WHO THIS BOOK IS FOR This book is ideal for undergraduate and graduate students, researchers, and professionals in computer vision, image processing, and AI. It also serves computer vision engineers, image analysts, data scientists, software engineers, and industry practitioners seeking practical, hands-on expertise using Python. TABLE OF CONTENTS 1. Image Restoration and Inverse Problems in Image Processing 2. More Image Restoration and Image Inpainting 3. Image Segmentation 4. More Image Segmentation 5. Image Feature Extraction and Its Applications: Image Registration 6. Applications of Image Feature Extraction 7. Image Classification 8. Object Detection and Recognition 9. Application of Image Processing and Computer Vision in Medical Imaging 10. Application of Image Processing and Computer Vision in Medical Imaging and Remote Sensing 11. Miscellaneous Problems in Image Processing and Computer Vision
Algoritmi per l’intelligenza artificiale
Author: Roberto Marmo
language: it
Publisher: HOEPLI EDITORE
Release Date: 2024-09-20T00:00:00+02:00
Cosa è l’intelligenza artificiale? Quali problemi computazionali può risolvere? Quali passi fare per creare un algoritmo? Come organizzare i dati di input e interpretare l’output? Quale modello matematico scegliere e come programmarlo in linguaggio Python? La nuova edizione del volume di Marmo intende rispondere a queste domande in modo pragmatico, per capire come funziona l’algoritmo, risolvere problemi tecnici e creare nuovi utilizzi. Ricca di esempi, consigli, link scelti, codice in linguaggio Python, l’opera è stata aggiornata inserendo alla fine di ogni capitolo una raccolta di prompt da usare in ChatGPT, con i quali sarà possibile approfondire di volta in volta l’argomento trattato.
Object Detection and Recognition in Digital Images
Author: Boguslaw Cyganek
language: en
Publisher: John Wiley & Sons
Release Date: 2013-05-20
Object detection, tracking and recognition in images are key problems in computer vision. This book provides the reader with a balanced treatment between the theory and practice of selected methods in these areas to make the book accessible to a range of researchers, engineers, developers and postgraduate students working in computer vision and related fields. Key features: Explains the main theoretical ideas behind each method (which are augmented with a rigorous mathematical derivation of the formulas), their implementation (in C++) and demonstrated working in real applications. Places an emphasis on tensor and statistical based approaches within object detection and recognition. Provides an overview of image clustering and classification methods which includes subspace and kernel based processing, mean shift and Kalman filter, neural networks, and k-means methods. Contains numerous case study examples of mainly automotive applications. Includes a companion website hosting full C++ implementation, of topics presented in the book as a software library, and an accompanying manual to the software platform.