The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Makalah ini mencadangkan kaedah baharu untuk memulihkan tanda kelengkungan Gaussian tempatan daripada berbilang (lebih daripada tiga) imej teduhan. Maklumat yang diperlukan untuk memulihkan tanda kelengkungan Gaussian diperoleh dengan menggunakan Analisis Komponen Utama (PCA) kepada ukuran sinaran ternormal. Tanda kelengkungan Gaussian dipulihkan berdasarkan orientasi relatif ukuran yang diperoleh pada corak ujian lima titik tempatan kepada yang berada dalam subruang 2-D yang dipanggil satah eigen. Menggunakan berbilang imej teduhan memberikan hasil yang lebih tepat dan mantap serta meminimumkan kesan bayang-bayang dengan membenarkan kawasan yang lebih besar pada permukaan yang boleh dilihat dianalisis berbanding kaedah yang menggunakan hanya tiga imej lorekan. Tambahan pula, ia membolehkan kaedah itu digunakan pada permukaan spekular. Memandangkan PCA mengalih keluar korelasi linear antara imej, kaedah ini boleh menghasilkan hasil yang berkualiti tinggi walaupun arah sumber cahaya tidak tersebar secara meluas.
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Salinan
Shinji FUKUI, Yuji IWAHORI, Robert J. WOODHAM, Kenji FUNAHASHI, Akira IWATA, "Robust Method for Recovering Sign of Gaussian Curvature from Multiple Shading Images" in IEICE TRANSACTIONS on Information,
vol. E84-D, no. 12, pp. 1633-1641, December 2001, doi: .
Abstract: This paper proposes a new method to recover the sign of local Gaussian curvature from multiple (more than three) shading images. The information required to recover the sign of Gaussian curvature is obtained by applying Principal Components Analysis (PCA) to the normalized irradiance measurements. The sign of the Gaussian curvature is recovered based on the relative orientation of measurements obtained on a local five point test pattern to those in the 2-D subspace called the eigen plane. Using multiple shading images gives a more accurate and robust result and minimizes the effect of shadows by allowing a larger area of the visible surface to be analyzed compared to methods using only three shading images. Furthermore, it allows the method to be applied to specular surfaces. Since PCA removes linear correlation among images, the method can produce results of high quality even when the light source directions are not widely dispersed.
URL: https://global.ieice.org/en_transactions/information/10.1587/e84-d_12_1633/_p
Salinan
@ARTICLE{e84-d_12_1633,
author={Shinji FUKUI, Yuji IWAHORI, Robert J. WOODHAM, Kenji FUNAHASHI, Akira IWATA, },
journal={IEICE TRANSACTIONS on Information},
title={Robust Method for Recovering Sign of Gaussian Curvature from Multiple Shading Images},
year={2001},
volume={E84-D},
number={12},
pages={1633-1641},
abstract={This paper proposes a new method to recover the sign of local Gaussian curvature from multiple (more than three) shading images. The information required to recover the sign of Gaussian curvature is obtained by applying Principal Components Analysis (PCA) to the normalized irradiance measurements. The sign of the Gaussian curvature is recovered based on the relative orientation of measurements obtained on a local five point test pattern to those in the 2-D subspace called the eigen plane. Using multiple shading images gives a more accurate and robust result and minimizes the effect of shadows by allowing a larger area of the visible surface to be analyzed compared to methods using only three shading images. Furthermore, it allows the method to be applied to specular surfaces. Since PCA removes linear correlation among images, the method can produce results of high quality even when the light source directions are not widely dispersed.},
keywords={},
doi={},
ISSN={},
month={December},}
Salinan
TY - JOUR
TI - Robust Method for Recovering Sign of Gaussian Curvature from Multiple Shading Images
T2 - IEICE TRANSACTIONS on Information
SP - 1633
EP - 1641
AU - Shinji FUKUI
AU - Yuji IWAHORI
AU - Robert J. WOODHAM
AU - Kenji FUNAHASHI
AU - Akira IWATA
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E84-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2001
AB - This paper proposes a new method to recover the sign of local Gaussian curvature from multiple (more than three) shading images. The information required to recover the sign of Gaussian curvature is obtained by applying Principal Components Analysis (PCA) to the normalized irradiance measurements. The sign of the Gaussian curvature is recovered based on the relative orientation of measurements obtained on a local five point test pattern to those in the 2-D subspace called the eigen plane. Using multiple shading images gives a more accurate and robust result and minimizes the effect of shadows by allowing a larger area of the visible surface to be analyzed compared to methods using only three shading images. Furthermore, it allows the method to be applied to specular surfaces. Since PCA removes linear correlation among images, the method can produce results of high quality even when the light source directions are not widely dispersed.
ER -