### Abstract

The difficult task of finding a fractal representation of an input shape is called the inverse problem of fractal geometry. Previous attempts at solving this problem have applied techniques from numerical minimization, heuristic search and image compression. The most appropriate domain from which to attack this problem is not numerical analysis nor signal processing, but model-based computer vision. Self-similar objects cause an existing computer vision algorithm called geometric hashing to malfunction. Similarity hashing capitalizes on this observation to not only detect a shape's morphological self-similarity but also find the parameters of its self-transformations.

Original language | English (US) |
---|---|

Pages (from-to) | 39-50 |

Number of pages | 12 |

Journal | Fractals |

Volume | 5 |

Issue number | SUPPL. 1 |

State | Published - Apr 1 1997 |

Externally published | Yes |

### Fingerprint

### ASJC Scopus subject areas

- Modeling and Simulation
- Geometry and Topology
- Applied Mathematics

### Cite this

*Fractals*,

*5*(SUPPL. 1), 39-50.

**Similarity hashing : A computer vision solution to the inverse problem of linear fractals.** / Hart, John C.; Cochran, Wayne O.; Flynn, Patrick J.

Research output: Contribution to journal › Article

*Fractals*, vol. 5, no. SUPPL. 1, pp. 39-50.

}

TY - JOUR

T1 - Similarity hashing

T2 - A computer vision solution to the inverse problem of linear fractals

AU - Hart, John C.

AU - Cochran, Wayne O.

AU - Flynn, Patrick J.

PY - 1997/4/1

Y1 - 1997/4/1

N2 - The difficult task of finding a fractal representation of an input shape is called the inverse problem of fractal geometry. Previous attempts at solving this problem have applied techniques from numerical minimization, heuristic search and image compression. The most appropriate domain from which to attack this problem is not numerical analysis nor signal processing, but model-based computer vision. Self-similar objects cause an existing computer vision algorithm called geometric hashing to malfunction. Similarity hashing capitalizes on this observation to not only detect a shape's morphological self-similarity but also find the parameters of its self-transformations.

AB - The difficult task of finding a fractal representation of an input shape is called the inverse problem of fractal geometry. Previous attempts at solving this problem have applied techniques from numerical minimization, heuristic search and image compression. The most appropriate domain from which to attack this problem is not numerical analysis nor signal processing, but model-based computer vision. Self-similar objects cause an existing computer vision algorithm called geometric hashing to malfunction. Similarity hashing capitalizes on this observation to not only detect a shape's morphological self-similarity but also find the parameters of its self-transformations.

UR - http://www.scopus.com/inward/record.url?scp=2742525576&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=2742525576&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:2742525576

VL - 5

SP - 39

EP - 50

JO - Fractals

JF - Fractals

SN - 0218-348X

IS - SUPPL. 1

ER -