This document details the ALISA Shape Module, a system developed in 2005 for adaptive shape recognition. It employs a Radial Feature Token (RFT) method, offering high robustness against boundary gaps, occlusion, and noise. The module provides translation, scale, and rotation invariance, making it a versatile tool for various applications. Its supervised learning capability, effective even with small training sets, and its superior performance compared to the Generalized Hough Transform highlight its advanced design.
This manual serves as a comprehensive guide to understanding and utilizing the ALISA Shape Module. It covers the core technology, including the Radial Feature Token recognition method and the General Shape Classifier. Detailed explanations of its invariance properties and robustness features are provided, alongside insights into its learning capabilities and data representation. The manual is intended for researchers, developers, and anyone seeking to implement or study advanced shape recognition techniques.
Shape classification is a challenging image processing problem because shapes can occur in any position, at any orientation, and at any scale in an image. Shapes can also be obscured by gaps in their boundaries, occlusions, and noise. General shape classifiers often suffer from low precision, and specialized shape classifiers rely on specific features, like vertices or connected boundaries, making them difficult to generalize. The objective of this research is to design, implement, and test a general, high-precision two-dimensional shape classifier that is invariant to translation, scale, and rotation, as well as robust to gaps in the shape boundary, occlusions, and noise. To achieve this objective, the radial feature token (RFT) is implemented as the ALISA Shape Module, which learns to classify shapes in ALISA geometry maps derived from a supervised set of training images. These learned shapes are stored as a set of vectors that are then used to classify shapes in test images. Experiments have demonstrated that this method can learn to classify general shapes from small training sets, as well as effectively classify similar shapes independent of their position, scale, and orientation. The Shape Module is also robust to gaps in shape boundaries, occlusions, and noise. The Shape Module is also shown to outperform some established shape recognition techniques, such as the Generalized Hough Transform.
Author: Becker, Glenn C.
Publisher: Dissertation.Com
Illustration: N
Language: ENG
Title: The ALISA Shape Module: Adaptive Shape Recognition using a Radial Feature Token
Pages: 00340 (Encrypted PDF)
On Sale: 2005-10-31
SKU-13/ISBN: 9781581121520
Category: Computers : General
Shape classification is a challenging image processing problem because shapes can occur in any position, at any orientation, and at any scale in an image. Shapes can also be obscured by gaps in their boundaries, occlusions, and noise. General shape classifiers often suffer from low precision, and specialized shape classifiers rely on specific features, like vertices or connected boundaries, making them difficult to generalize. The objective of this research is to design, implement, and test a general, high-precision two-dimensional shape classifier that is invariant to translation, scale, and rotation, as well as robust to gaps in the shape boundary, occlusions, and noise. To achieve this objective, the radial feature token (RFT) is implemented as the ALISA Shape Module, which learns to classify shapes in ALISA geometry maps derived from a supervised set of training images. These learned shapes are stored as a set of vectors that are then used to classify shapes in test images. Experiments have demonstrated that this method can learn to classify general shapes from small training sets, as well as effectively classify similar shapes independent of their position, scale, and orientation. The Shape Module is also robust to gaps in shape boundaries, occlusions, and noise. The Shape Module is also shown to outperform some established shape recognition techniques, such as the Generalized Hough Transform.
Author: Becker, Glenn C.
Publisher: Dissertation.Com
Illustration: N
Language: ENG
Title: The ALISA Shape Module: Adaptive Shape Recognition using a Radial Feature Token
Pages: 00340 (Encrypted PDF)
On Sale: 2005-10-31
SKU-13/ISBN: 9781581121520
Category: Computers : General
