Orthogonal image moments for human-centric visual pattern recognition
- 作者： Rahman, S. M. Mahbubur, author.
- 其他作者：
- 其他題名：
- Cognitive intelligence and robotics.
- 出版： Singapore : Springer Singapore :Imprint: Springer
- 叢書名： Cognitive intelligence and robotics,
- 主題： Pattern recognition systems. , Computer vision. , Computer Imaging, Vision, Pattern Recognition and Graphics.
- ISBN： 9789813299450 (electronic bk.) 、 9789813299443 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 內容註: 1 Introduction -- 2 Image Moments -- 3 Face Recognition -- 4 Expression Recognition -- 5 Fingerprint Classification -- 6 Iris Recognition -- 7 Hand Gesture Recognition -- 8 Conclusion.
- 摘要註: Instead of focusing on the mathematical properties of moments, this book is a compendium of research that demonstrates the effectiveness of orthogonal moment-based features in face recognition, expression recognition, fingerprint recognition and iris recognition. The usefulness of moments and their invariants in pattern recognition is well known. What is less well known is how orthogonal moments may be applied to specific problems in human-centric visual pattern recognition. Unlike previous books, this work highlights the fundamental issues involved in moment-based pattern recognition, from the selection of discriminative features in a high-dimensional setting, to addressing the question of how to classify a large number of patterns based on small training samples. In addition to offering new concepts that illustrate the use of statistical methods in addressing some of these issues, the book presents recent results and provides guidance on implementing the methods. Accordingly, it will be of interest to researchers and graduate students working in the broad areas of computer vision and visual pattern recognition.
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讀者標籤：
- 系統號： 005466342 | 機讀編目格式
館藏資訊
Instead of focusing on the mathematical properties of moments, this book is a compendium of research that demonstrates the effectiveness of orthogonal moment-based features in face recognition, expression recognition, fingerprint recognition and iris recognition. The usefulness of moments and their invariants in pattern recognition is well known. What is less well known is how orthogonal moments may be applied to specific problems in human-centric visual pattern recognition. Unlike previous books, this work highlights the fundamental issues involved in moment-based pattern recognition, from the selection of discriminative features in a high-dimensional setting, to addressing the question of how to classify a large number of patterns based on small training samples. In addition to offering new concepts that illustrate the use of statistical methods in addressing some of these issues, the book presents recent results and provides guidance on implementing the methods. Accordingly, it will be of interest to researchers and graduate students working in the broad areas of computer vision and visual pattern recognition.