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Orthogonal image moments for human-c...

Rahman, S. M. Mahbubur.

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Orthogonal image moments for human-centric visual pattern recognition

Record Type:	Electronic resources : Monograph/item
Title/Author:	Orthogonal image moments for human-centric visual pattern recognition/ by S. M. Mahbubur Rahman, Tamanna Howlader, Dimitrios Hatzinakos.
Author:	Rahman, S. M. Mahbubur.
other author:	Howlader, Tamanna.
Published:	Singapore :Springer Singapore : : 2019.,
Description:	xii, 149 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	1 Introduction -- 2 Image Moments -- 3 Face Recognition -- 4 Expression Recognition -- 5 Fingerprint Classiﬁcation -- 6 Iris Recognition -- 7 Hand Gesture Recognition -- 8 Conclusion.
Contained By:	Springer eBooks
Subject:	Pattern recognition systems. -
Online resource:	https://doi.org/10.1007/978-981-32-9945-0
ISBN:	9789813299450

Orthogonal image moments for human-centric visual pattern recognition
Rahman, S. M. Mahbubur.

Orthogonal image moments for human-centric visual pattern recognition[electronic resource] /by S. M. Mahbubur Rahman, Tamanna Howlader, Dimitrios Hatzinakos. - Singapore :Springer Singapore :2019. - xii, 149 p. :ill. (some col.), digital ;24 cm. - Cognitive intelligence and robotics,2520-1956. - Cognitive intelligence and robotics..

1 Introduction -- 2 Image Moments -- 3 Face Recognition -- 4 Expression Recognition -- 5 Fingerprint Classiﬁcation -- 6 Iris Recognition -- 7 Hand Gesture Recognition -- 8 Conclusion.

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.

ISBN: 9789813299450

Standard No.: 10.1007/978-981-32-9945-0doiSubjects--Topical Terms:

527885
Pattern recognition systems.

LC Class. No.: TK7882.P3 / R34 2019

Dewey Class. No.: 006.4

Orthogonal image moments for human-centric visual pattern recognition
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245 1 0 $a Orthogonal image moments for human-centric visual pattern recognition $h [electronic resource] / $c by S. M. Mahbubur Rahman, Tamanna Howlader, Dimitrios Hatzinakos.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2019.
300 $a xii, 149 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Cognitive intelligence and robotics, $x 2520-1956
505 0 $a 1 Introduction -- 2 Image Moments -- 3 Face Recognition -- 4 Expression Recognition -- 5 Fingerprint Classiﬁcation -- 6 Iris Recognition -- 7 Hand Gesture Recognition -- 8 Conclusion.
520 $a 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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650 0 $a Computer vision. $3 540671
650 1 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 890871
700 1 $a Howlader, Tamanna. $3 3453421
700 1 $a Hatzinakos, Dimitrios. $3 3453422
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830 0 $a Cognitive intelligence and robotics. $3 3338190
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