Contour-Based Binary Image Orientation Detection by Orientation Context and Roulette Distance

Jian ZHOU; Takafumi MATSUMARU

doi:10.1587/transfun.E99.A.621

Contour-Based Binary Image Orientation Detection by Orientation Context and Roulette Distance

Jian ZHOU, Takafumi MATSUMARU

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This paper proposes a novel technology to detect the orientation of an image relying on its contour which is noised to varying degrees. For the image orientation detection, most methods regard to the landscape image and the image taken of a single object. In these cases, the contours of these images are supposed to be immune to the noise. This paper focuses on the the contour noised after image segmentation. A polar orientation descriptor Orientation Context is viewed as a feature to describe the coarse distribution of the contour points. This descriptor is verified to be independent of translation, isotropic scaling, and rotation transformation by theory and experiment. The relative orientation depends on the minimum distance Roulette Distance between the descriptor of a template image and that of a test image. The proposed method is capable of detecting the direction on the interval from 0 to 359 degrees which is wider than the former contour-based means (Distance Phase [1], from 0 to 179 degrees). What's more, the results of experiments show that not only the normal binary image (Noise-0, Accuracy-1: 84.8%) (defined later) achieves more accurate orientation but also the binary image with slight contour noise (Noise-1, Accuracy-1: 73.5%) could obtain more precise orientation compared to Distance Phase (Noise-0, Accuracy-1: 56.3%; Noise-1, Accuracy-1: 27.5%). Although the proposed method (O(op²)) takes more time to detect the orientation than Distance Phase (O(st)), it could be realized including the preprocessing in real time test with a frame rate of 30.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E99-A No.2 pp.621-633

Publication Date: 2016/02/01

Publicized

Online ISSN: 1745-1337

DOI: 10.1587/transfun.E99.A.621

Type of Manuscript: PAPER

Category: Image

Authors

Jian ZHOU
Waseda University
Takafumi MATSUMARU
Waseda University

Keyword

image orientation, noisy contour, binary image, Orientation Context, Roulette Distance

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Jian ZHOU, Takafumi MATSUMARU, "Contour-Based Binary Image Orientation Detection by Orientation Context and Roulette Distance" in IEICE TRANSACTIONS on Fundamentals, vol. E99-A, no. 2, pp. 621-633, February 2016, doi: 10.1587/transfun.E99.A.621.
Abstract: This paper proposes a novel technology to detect the orientation of an image relying on its contour which is noised to varying degrees. For the image orientation detection, most methods regard to the landscape image and the image taken of a single object. In these cases, the contours of these images are supposed to be immune to the noise. This paper focuses on the the contour noised after image segmentation. A polar orientation descriptor Orientation Context is viewed as a feature to describe the coarse distribution of the contour points. This descriptor is verified to be independent of translation, isotropic scaling, and rotation transformation by theory and experiment. The relative orientation depends on the minimum distance Roulette Distance between the descriptor of a template image and that of a test image. The proposed method is capable of detecting the direction on the interval from 0 to 359 degrees which is wider than the former contour-based means (Distance Phase [1], from 0 to 179 degrees). What's more, the results of experiments show that not only the normal binary image (Noise-0, Accuracy-1: 84.8%) (defined later) achieves more accurate orientation but also the binary image with slight contour noise (Noise-1, Accuracy-1: 73.5%) could obtain more precise orientation compared to Distance Phase (Noise-0, Accuracy-1: 56.3%; Noise-1, Accuracy-1: 27.5%). Although the proposed method (O(op²)) takes more time to detect the orientation than Distance Phase (O(st)), it could be realized including the preprocessing in real time test with a frame rate of 30.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E99.A.621/_p

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@ARTICLE{e99-a_2_621,
author={Jian ZHOU, Takafumi MATSUMARU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Contour-Based Binary Image Orientation Detection by Orientation Context and Roulette Distance},
year={2016},
volume={E99-A},
number={2},
pages={621-633},
abstract={This paper proposes a novel technology to detect the orientation of an image relying on its contour which is noised to varying degrees. For the image orientation detection, most methods regard to the landscape image and the image taken of a single object. In these cases, the contours of these images are supposed to be immune to the noise. This paper focuses on the the contour noised after image segmentation. A polar orientation descriptor Orientation Context is viewed as a feature to describe the coarse distribution of the contour points. This descriptor is verified to be independent of translation, isotropic scaling, and rotation transformation by theory and experiment. The relative orientation depends on the minimum distance Roulette Distance between the descriptor of a template image and that of a test image. The proposed method is capable of detecting the direction on the interval from 0 to 359 degrees which is wider than the former contour-based means (Distance Phase [1], from 0 to 179 degrees). What's more, the results of experiments show that not only the normal binary image (Noise-0, Accuracy-1: 84.8%) (defined later) achieves more accurate orientation but also the binary image with slight contour noise (Noise-1, Accuracy-1: 73.5%) could obtain more precise orientation compared to Distance Phase (Noise-0, Accuracy-1: 56.3%; Noise-1, Accuracy-1: 27.5%). Although the proposed method (O(op²)) takes more time to detect the orientation than Distance Phase (O(st)), it could be realized including the preprocessing in real time test with a frame rate of 30.},
keywords={},
doi={10.1587/transfun.E99.A.621},
ISSN={1745-1337},
month={February},}

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TY - JOUR
TI - Contour-Based Binary Image Orientation Detection by Orientation Context and Roulette Distance
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 621
EP - 633
AU - Jian ZHOU
AU - Takafumi MATSUMARU
PY - 2016
DO - 10.1587/transfun.E99.A.621
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E99-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2016
AB - This paper proposes a novel technology to detect the orientation of an image relying on its contour which is noised to varying degrees. For the image orientation detection, most methods regard to the landscape image and the image taken of a single object. In these cases, the contours of these images are supposed to be immune to the noise. This paper focuses on the the contour noised after image segmentation. A polar orientation descriptor Orientation Context is viewed as a feature to describe the coarse distribution of the contour points. This descriptor is verified to be independent of translation, isotropic scaling, and rotation transformation by theory and experiment. The relative orientation depends on the minimum distance Roulette Distance between the descriptor of a template image and that of a test image. The proposed method is capable of detecting the direction on the interval from 0 to 359 degrees which is wider than the former contour-based means (Distance Phase [1], from 0 to 179 degrees). What's more, the results of experiments show that not only the normal binary image (Noise-0, Accuracy-1: 84.8%) (defined later) achieves more accurate orientation but also the binary image with slight contour noise (Noise-1, Accuracy-1: 73.5%) could obtain more precise orientation compared to Distance Phase (Noise-0, Accuracy-1: 56.3%; Noise-1, Accuracy-1: 27.5%). Although the proposed method (O(op²)) takes more time to detect the orientation than Distance Phase (O(st)), it could be realized including the preprocessing in real time test with a frame rate of 30.
ER -