Pattern recognition with hierarchical networks

US7308134B2 · kind B2 · utility

Assignee

• Honda Research Institute Europe GmbH · DE

Inventors

• Heiko Wersing · Frankfurt, DE
• Edgar Körner · Seligenstadt, DE

Key dates

• Filing date: May 24, 2002
• Grant date: Dec 11, 2007
• Expiry date: Apr 21, 2024

Classification

• Technology area (CPC G): Physics
• CPC primary: G06V10/454
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Within the frameworks of hierarchical neural feed-forward architectures for performing real-world 3D invariant object recognition a technique is proposed that shares components like weight-sharing (2), and pooling stages (3, 5) with earlier approaches, but focuses on new methods for determining optimal feature-detecting units in intermediate stages (4) of the hierarchical network. A new approach for training the hierarchical network is proposed which uses statistical means for (incrementally) learning new feature detection stages and significantly reduces the training effort for complex pattern recognition tasks, compared to the prior art. The incremental learning is based on detecting increasingly statistically independent features in higher stages of the processing hierarchy. Since this learning is unsupervised, no teacher signal is necessary and the recognition architecture can be pre-structured for a certain recognition scenario. Only a final classification step must be trained with supervised learning, which reduces significantly the effort for adaptation to a recognition task.