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Logo recognition : theory and practice /
"Used by companies, organizations, and even individuals to promote recognition of their brand, logos can also act as a valuable means of identifying the source of a document. E-business applications can retrieve and catalog products according to their logos. Governmental agencies can easily ins...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, FL :
CRC Press,
©2012.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Chen, Jingying, |d 1973- | |
| 245 | 1 | 0 | |a Logo recognition : |b theory and practice / |c Jingying Chen, Lizhe Wang, Dan Chen. |
| 260 | |a Boca Raton, FL : |b CRC Press, |c ©2012. | ||
| 300 | |a 1 online resource (xvi, 176 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 520 | |a "Used by companies, organizations, and even individuals to promote recognition of their brand, logos can also act as a valuable means of identifying the source of a document. E-business applications can retrieve and catalog products according to their logos. Governmental agencies can easily inspect goods using smart mobile devices that use logo recognition techniques. However, because logos are two-dimensional shapes of varying complexity, the recognition process can be challenging. Although promising results have been found for clean logos, they have not been as robust for noisy logos. Logo Recognition: Theory and Practice is the first book to focus on logo recognition, especially under noisy conditions. Beginning with an introduction to fundamental concepts and methods in pattern and shape recognition, it surveys advances in logo recognition. The authors also propose a new logo recognition system that can be used under adverse conditions such as broken lines, added noise, and occlusion. The proposed system introduces a novel polygonal approximation, a robust indexing scheme, and a new Line Segment Hausdorff Distance (LHD) matching method that can handle more distortion and transformation types than previous techniques. In the first stage, raw logos are transformed into normalized line segment maps. In the second stage, effective line pattern features are used to index the database in order to generate a moderate number of likely models. In the third stage, an improved LHD measure screens and generates the best matches. A comprehensive overview of logo recognition, the book also presents successful applications of the technology and suggests directions for future research."--Provided by publisher | ||
| 520 | |a "Preface Logo recognition is of great interest in the document and shape matching domain. Logos can act as a valuable means of identifying sources of documents. By recognizing the logo, semantic information about the document is obtained which may be useful to decide whether or not to analyze the textual parts. Some promising results have been found for clean logos; however, they can hardly be robust for noisy logos. This book summarizes our recent research on logo recognition. We first in this book (Chapter 2) provide some introduction and fundamental knowledge for pattern recognition. Readers can safely skip reading it if you feel you are familiar with these topics. In order to develop a logo recognition method that is robust to be employed under adverse conditions such as different broken curves, added noise and occlusion, a logo recognition system based on line pattern features is proposed in this book. To achieve the desired accuracy and effciency, the proposed system employs a three-stage hierarchy, polygonal approximation, indexing and matching. In the first stage, the raw logos are transformed into normalized line segment maps (LSM); in the second stage, effective line pattern features are used to index the database to generate a moderate number of likely models with respect to a test image; in the third stage, an improved Line Segment Hausdor Distance (LHD) measure is proposed to screen further and generate the best matches"--Provided by publisher | ||
| 504 | |a Includes bibliographical references and index. | ||
| 500 | |a Title from PDF title page (viewed November 1, 2011). | ||
| 505 | 0 | |a 1. Introduction -- 2. Preliminary knowledge -- 3. Review of shape recognition techniques -- 4. System overview -- 5. Polygonal approximation -- 6. Logo indexing -- 7. Logo matching -- 8. Applications -- 9. Conclusion. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Pattern recognition systems. | |
| 650 | 0 | |a Logos (Symbols) | |
| 650 | 0 | |a Document imaging systems. | |
| 650 | 2 | |a Pattern Recognition, Automated | |
| 650 | 6 | |a Reconnaissance des formes (Informatique) | |
| 650 | 6 | |a Logos (Symboles) | |
| 650 | 6 | |a Gestion électronique de documents. | |
| 650 | 7 | |a logos. |2 aat | |
| 650 | 7 | |a COMPUTERS |x Enterprise Applications |x Business Intelligence Tools. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Document imaging systems. |2 fast |0 (OCoLC)fst00896066 | |
| 650 | 7 | |a Logos (Symbols) |2 fast |0 (OCoLC)fst01002130 | |
| 650 | 7 | |a Pattern recognition systems. |2 fast |0 (OCoLC)fst01055266 | |
| 700 | 1 | |a Wang, Lizhe, |d 1974- | |
| 700 | 1 | |a Chen, Dan, |d 1973- | |
| 776 | 0 | 8 | |i Print version: |a Chen, Jingying, 1973 - |t Logo recognition. |d Boca Raton, FL : CRC Press, ©2012 |z 9781439847756 |w (DLC) 2011030495 |w (OCoLC)606777044 |
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