Name: Computational Intelligence in Multi-Feature Visual Pattern Recognition: Hand Posture and Face Recognition Using Biologically Inspired Approaches (en Inglés)
Price: 633394.00 COP
Availability: InStock
Author: Pisharady, Pramod Kumar ; Vadakkepat, Prahlad ; Poh, Loh Ai
ISBN: 9789811011719

portada Computational Intelligence in Multi-Feature Visual Pattern Recognition: Hand Posture and Face Recognition Using Biologically Inspired Approaches (en Inglés)

Formato

Libro Físico

Autor

Prahlad Vadakkepat

Pramod Kumar Pisharady

Loh Ai Poh

Editorial

Springer

Idioma

Inglés

N° páginas

138

Encuadernación

Tapa Blanda

Dimensiones

23.4 x 15.6 x 0.8 cm

Peso

0.23 kg.

ISBN13

9789811011719

Categorías

Ingeniería: general
Algoritmos y estructuras de datos
Reconocimiento de formas
Visión por ordenador

Computational Intelligence in Multi-Feature Visual Pattern Recognition: Hand Posture and Face Recognition Using Biologically Inspired Approaches (en Inglés)

Prahlad Vadakkepat (Autor) · Pramod Kumar Pisharady (Autor) · Loh Ai Poh (Autor) · Springer · Tapa Blanda

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Reseña del libro "Computational Intelligence in Multi-Feature Visual Pattern Recognition: Hand Posture and Face Recognition Using Biologically Inspired Approaches (en Inglés)"

This book presents a collection of computational intelligence algorithms that addresses issues in visual pattern recognition such as high computational complexity, abundance of pattern features, sensitivity to size and shape variations and poor performance against complex backgrounds. The book has 3 parts. Part 1 describes various research issues in the field with a survey of the related literature. Part 2 presents computational intelligence based algorithms for feature selection and classification. The algorithms are discriminative and fast. The main application area considered is hand posture recognition. The book also discusses utility of these algorithms in other visual as well as non-visual pattern recognition tasks including face recognition, general object recognition and cancer / tumor classification. Part 3 presents biologically inspired algorithms for feature extraction. The visual cortex model based features discussed have invariance with respect to appearance and size of the hand, and provide good inter class discrimination. A Bayesian model of visual attention is described which is effective in handling complex background problem in hand posture recognition. The book provides qualitative and quantitative performance comparisons for the algorithms outlined, with other standard methods in machine learning and computer vision. The book is self-contained with several figures, charts, tables and equations helping the reader to understand the material presented without instruction.