Bibliographic record
Abstract
This book is about the use of modern geometric methods for signal and image analysis. It provides a comprehensive coverage of the subject from the basic principles to state-of-the-art concepts and applications. The objective is to give the reader a sound understanding of the major theoretical concepts and computational approaches for applying geometric techniques and methodologies in solving various problems that arise naturally in signal and image processing, computer graphics, computer-aided design, bioinformatics, and other disciplines. The emphasis throughout is on intuitive and application-driven arguments. All methods are illustrated by well-chosen examples and applications, and are selected from core areas of modern geometric and topological computing. Furthermore, the purpose is for the reader to become aware of some recent developments in this fast-growing field. Audience The book is intended as a comprehensive and concise reference for geometric and topological methods in signal and image processing. The topics covered in this book are essential for research in numerical geometry and computational algebraic topology, and desirable for students, researchers, and practitioners pursuing research in signal and image processing, computer vision, computer graphics, computer-aided design, and other related fields. The content grew from notes developed for graduate and undergraduate courses in signal processing, image processing, and computer graphics given at North Carolina State University and Concordia University, primarily targeted at electrical engineering, computer science, and software engineering students. Chapter organization and topics covered This book abandons the classical definition–theorem–proof model, and instead heavily relies on effective computational techniques with concrete applications to image analysis, computer vision, geometry processing, and computer graphics. The pitfalls of including all the technical details at the expense of foregone physical intuition of many heavily mathematical texts are largely avoided. The first chapter presents a brief motivation behind geometric methods and their various applications in imaging and computer graphics. Chapters 2 and 3 lay the foundations for our coverage of geometry and topology, and are essential to the rest of the book. The remaining three chapters are, however, almost completely independent of each other.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.406 | 0.287 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".