### 抄録

The pyramid transform compresses images while preserving global features such as edges and segments. The pyramid transform is efficiently used in optical flow computation starting from planar images captured by pinhole camera systems, since the propagation of features from coarse sampling to fine sampling allows the computation of both large displacements in low-resolution images sampled by a coarse grid and small displacements in high-resolution images sampled by a fine grid. The image pyramid transform involves the resizing of an image by downsampling after convolution with the Gaussian kernel. Since the convolution with the Gaussian kernel for smoothing is derived as the solution of a linear diffusion equation, the pyramid transform is performed by applying a downsampling operation to the solution of the linear diffusion equation.

元の言語 | English |
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ホスト出版物のタイトル | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

ページ | 78-109 |

ページ数 | 32 |

巻 | 7474 LNCS |

DOI | |

出版物ステータス | Published - 2012 |

イベント | 15th International Workshop on Theoretical Foundations of Computer Vision - Dagstuhl Castle 継続期間: 2011 6 26 → 2011 7 1 |

### 出版物シリーズ

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

巻 | 7474 LNCS |

ISSN（印刷物） | 03029743 |

ISSN（電子版） | 16113349 |

### Other

Other | 15th International Workshop on Theoretical Foundations of Computer Vision |
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市 | Dagstuhl Castle |

期間 | 11/6/26 → 11/7/1 |

### Fingerprint

### ASJC Scopus subject areas

- Computer Science(all)
- Theoretical Computer Science

### これを引用

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)*(巻 7474 LNCS, pp. 78-109). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 巻数 7474 LNCS). https://doi.org/10.1007/978-3-642-34091-8_4

**Pyramid transform and scale-space analysis in image analysis.** / Mochizuki, Yoshihiko; Imiya, Atsushi.

研究成果: Conference contribution

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).*巻. 7474 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 巻. 7474 LNCS, pp. 78-109, 15th International Workshop on Theoretical Foundations of Computer Vision, Dagstuhl Castle, 11/6/26. https://doi.org/10.1007/978-3-642-34091-8_4

}

TY - GEN

T1 - Pyramid transform and scale-space analysis in image analysis

AU - Mochizuki, Yoshihiko

AU - Imiya, Atsushi

PY - 2012

Y1 - 2012

N2 - The pyramid transform compresses images while preserving global features such as edges and segments. The pyramid transform is efficiently used in optical flow computation starting from planar images captured by pinhole camera systems, since the propagation of features from coarse sampling to fine sampling allows the computation of both large displacements in low-resolution images sampled by a coarse grid and small displacements in high-resolution images sampled by a fine grid. The image pyramid transform involves the resizing of an image by downsampling after convolution with the Gaussian kernel. Since the convolution with the Gaussian kernel for smoothing is derived as the solution of a linear diffusion equation, the pyramid transform is performed by applying a downsampling operation to the solution of the linear diffusion equation.

AB - The pyramid transform compresses images while preserving global features such as edges and segments. The pyramid transform is efficiently used in optical flow computation starting from planar images captured by pinhole camera systems, since the propagation of features from coarse sampling to fine sampling allows the computation of both large displacements in low-resolution images sampled by a coarse grid and small displacements in high-resolution images sampled by a fine grid. The image pyramid transform involves the resizing of an image by downsampling after convolution with the Gaussian kernel. Since the convolution with the Gaussian kernel for smoothing is derived as the solution of a linear diffusion equation, the pyramid transform is performed by applying a downsampling operation to the solution of the linear diffusion equation.

UR - http://www.scopus.com/inward/record.url?scp=84867854151&partnerID=8YFLogxK

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U2 - 10.1007/978-3-642-34091-8_4

DO - 10.1007/978-3-642-34091-8_4

M3 - Conference contribution

AN - SCOPUS:84867854151

SN - 9783642340901

VL - 7474 LNCS

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

SP - 78

EP - 109

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

ER -