### Abstract

In this paper, we proposed a novel corner detection algorithm, based on statistical properties of corners, to detect corners of both polygonal and polyhedral objects. By means of local histogram analysis, we first bilevel the subimage within a circular window, then compute the intensity mean for the bileveled subimage. From the intensity mean we can estimate the corner angle. We then calculate the theoretical position variance from the estimated angle. Comparing the position variance from the bileveled subimage with its theoretical value, we can identify whether or not the pixel at the center of the subimage is a corner. Finally, the corner orientation can be obtained from the position mean. Our algorithm can detect corners of both polygons and polyhedra, even if they appear in an image at the same time. It is superior to conventional detection algorithms that it can detect corners over a large range of angles.

Original language | English |
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Title of host publication | Computer Analysis of Images and Patterns - 5th International Conference, CAIP 1993, Proceedings |

Editors | Walter G. Kropatsch, Dmitry Chetverikov |

Publisher | Springer Verlag |

Pages | 237-244 |

Number of pages | 8 |

ISBN (Print) | 9783540572336 |

Publication status | Published - 1993 Jan 1 |

Event | 5th International Conference on Computer Analysis of Images and Patterns, CAIP 1993 - Budapest, Hungary Duration: 1993 Sep 13 → 1993 Sep 15 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 719 LNCS |

ISSN (Print) | 0302-9743 |

ISSN (Electronic) | 1611-3349 |

### Other

Other | 5th International Conference on Computer Analysis of Images and Patterns, CAIP 1993 |
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Country | Hungary |

City | Budapest |

Period | 93-09-13 → 93-09-15 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Theoretical Computer Science
- Computer Science(all)

### Cite this

*Computer Analysis of Images and Patterns - 5th International Conference, CAIP 1993, Proceedings*(pp. 237-244). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 719 LNCS). Springer Verlag.

}

*Computer Analysis of Images and Patterns - 5th International Conference, CAIP 1993, Proceedings.*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 719 LNCS, Springer Verlag, pp. 237-244, 5th International Conference on Computer Analysis of Images and Patterns, CAIP 1993, Budapest, Hungary, 93-09-13.

**Detecting corners of polygonal and polyhedral objects.** / Guo, Jinn Kwei; Hsu, Rein Lien; Chen, Chin-Hsing; Sun, Yung-Nien.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Detecting corners of polygonal and polyhedral objects

AU - Guo, Jinn Kwei

AU - Hsu, Rein Lien

AU - Chen, Chin-Hsing

AU - Sun, Yung-Nien

PY - 1993/1/1

Y1 - 1993/1/1

N2 - In this paper, we proposed a novel corner detection algorithm, based on statistical properties of corners, to detect corners of both polygonal and polyhedral objects. By means of local histogram analysis, we first bilevel the subimage within a circular window, then compute the intensity mean for the bileveled subimage. From the intensity mean we can estimate the corner angle. We then calculate the theoretical position variance from the estimated angle. Comparing the position variance from the bileveled subimage with its theoretical value, we can identify whether or not the pixel at the center of the subimage is a corner. Finally, the corner orientation can be obtained from the position mean. Our algorithm can detect corners of both polygons and polyhedra, even if they appear in an image at the same time. It is superior to conventional detection algorithms that it can detect corners over a large range of angles.

AB - In this paper, we proposed a novel corner detection algorithm, based on statistical properties of corners, to detect corners of both polygonal and polyhedral objects. By means of local histogram analysis, we first bilevel the subimage within a circular window, then compute the intensity mean for the bileveled subimage. From the intensity mean we can estimate the corner angle. We then calculate the theoretical position variance from the estimated angle. Comparing the position variance from the bileveled subimage with its theoretical value, we can identify whether or not the pixel at the center of the subimage is a corner. Finally, the corner orientation can be obtained from the position mean. Our algorithm can detect corners of both polygons and polyhedra, even if they appear in an image at the same time. It is superior to conventional detection algorithms that it can detect corners over a large range of angles.

UR - http://www.scopus.com/inward/record.url?scp=85028888650&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85028888650&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9783540572336

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 237

EP - 244

BT - Computer Analysis of Images and Patterns - 5th International Conference, CAIP 1993, Proceedings

A2 - Kropatsch, Walter G.

A2 - Chetverikov, Dmitry

PB - Springer Verlag

ER -