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Nº de artículo:
4232656
Computer Vision
85% of respondents would recommend this to a friend
Nº de artículo:
4232656
NIO 3019
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This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme.
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What Stands Out
Comprehensive Coverage
This edition provides an in-depth exploration of computer vision fundamentals, covering essential algorithms and techniques that cater to both beginners and professionals in the field.
Practical Applications
Real-world case studies and practical examples enhance understanding, helping readers apply theoretical concepts to actual computer vision problems, making it ideal for students and practitioners.
Updated Content
Incorporating the latest research and advancements, this edition ensures readers stay current with trends and technologies in computer vision, offering valuable insights into future developments.
Detalles de producto
- Focuses on learning and inference in probabilistic models
- Shows how to use training data to learn relationships between image data and aspects of the world
- Starts from basics of probability and model fitting and progresses to real examples
- Covers cutting-edge techniques like graph cuts, machine learning, and multiple view geometry
- Describes over 70 algorithms in sufficient detail to implement
- Includes more than 350 full-color illustrations and background mathematics
| Publisher | Cambridge Univ Pr |
| Publication date | August 30, 2012 |
| Edition | 1st |
| Language | English |
| Print length | 598 pages |
| ISBN-10 | 1107011795 |
| ISBN-13 | 978-1107011793 |
| Item Weight | 3.1 pounds (1.41 kg) |
| Dimensions | 7 x 1.3 x 10.1 inches (17.8 x 3.3 x 25.7 cm) |
Who Should Buy?
-
Students
Ideal for students pursuing computer science or related fields, providing foundational knowledge of computer vision concepts and techniques.
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Research Professionals
Great for researchers in AI or robotics, offering advanced insights into current trends and methodologies in computer vision.
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Developers
Beneficial for software developers looking to implement computer vision applications, supplying practical examples and coding approaches.
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Casual Readers
Not suitable for casual readers due to technical depth, which may be overwhelming without a background in computer science.
DESCRIPCIÓN DEL PRODUCTO
Computer Vision
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Computer Vision & Pattern Recognition Editorial Review
Computer Vision: Models, Learning, and Inference by Simon J. D. Prince is an ideal book for readers who already have some intermediate knowledge of machine learning and estimation theory. It is very well-organized and engagingly written by the author. All topics are presented in a concise and intuitive manner, with beautiful and helpful pictures that enhance the understanding of the concepts. The book never leaves the reader in the middle of the mathematical arguments and always takes them to the end with Consistent notation.
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ventajas
- Well-organized and engagingly written
- Concise and intuitive presentation of topics
- Beautiful and helpful pictures
- Takes readers all the way through mathematical arguments
- Consistent notation
Contras
- Not suitable for readers without prior knowledge of estimation theory and machine learning Overall, Computer Vision: Models, Learning, and Inference is highly recommended for readers looking to expand their knowledge in computer vision and machine learning. Note: One negative review mentions boredom due to lack of real-world examples or intuition, but this seems to be an outlier.
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características y beneficios
- Learn relationships between observed image data and aspects of the world
- Exploit relationships to make new inferences about the world from new image data
- Cutting-edge techniques including graph cuts and machine learning
- Unified approach to common computer vision problems
- More than 70 algorithms for implementation
- Self-contained treatment with all background mathematics
- Over 350 full-color illustrations amplify the text
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