Hierarchical Modeling of Multidimensional Data in Regularly Decomposed Spaces: Applications in Image Analysis

TL;DR

This paper reviews hierarchical modeling techniques in image analysis across various fields, including industrial pattern recognition, satellite imagery, and face recognition, highlighting their applications and future prospects.

Contribution

It introduces a comprehensive overview of hierarchical modeling methods in image analysis, emphasizing their application in diverse domains and proposing future self-descriptive coding systems.

Findings

Hierarchical models improve pattern recognition accuracy.

Applications span industrial, satellite, and facial image analysis.

Future systems aim for self-descriptive image coding.

Abstract

This last document is showing the gradual introduction of hierarchical modeling techniques in image analysis. The first chapter is dealing with the first works carried out in the field of industrial applications of pattern recognition. The second chapter is focusing on the usage of these techniques in satellite imagery and on the development of a satellite data archiving system in the aim of using it in digital geography. The third chapter is about face recognition based on planar image analysis and about the recognition of partially hidden patterns. The present publication is ending with the description of a future system of self-descriptive coding of still or moving pictures in relation with the current video coding standards. As in the previous documents, it will be found in annex algorithms targeted on image analysis according two complementary approaches: - boundary-based approach for the industrial applications of artificial vision; - region-based approach for satellite image analysis.