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
로컬 설명자와 모양 일치는 모양 분석의 기본 체계입니다. 일치 결과를 시각적으로 확인하고 모양 분류를 위해 평가할 수도 있습니다. 일반적으로 모양 일치는 각각의 샘플링된 점 집합으로 표현되는 모양 간의 대응 관계를 결정하여 구현됩니다. 일부 매칭 방법은 이미 제안되었습니다. 그들 사이의 주요 차이점은 일치하는 비용 함수를 선택하는 것입니다. 이 기능은 한 모양의 초점 주위에 샘플링된 점의 로컬 분포와 다른 모양의 참조점 주위에 샘플링된 점의 로컬 분포 간의 차이점을 측정합니다. 로컬 설명자는 모양 지점 주변의 샘플링된 지점 분포를 설명하는 데 사용됩니다. 본 논문에서는 기존 지역 디스크립터의 오류를 보상할 수 있는 확장된 형태 매칭 기법을 제안한다. 로컬 디스크립터를 수정할 필요가 없기 때문에 로컬 디스크립터가 우리의 방식을 채택하는 것이 편리합니다. 우리 방식의 주요 아이디어는 포커싱 포인트의 일치성을 결정할 때 포커싱 포인트에 대한 인접한 샘플링 포인트의 일치성을 고려하는 것입니다. 이는 적합한 서신을 찾을 가능성을 높이기 때문에 유용합니다. 그러나 이웃점들의 대응성을 고려하게 되면 형상 매칭에서 고려해야 할 가능한 대응점이 상당히 늘어나게 되므로 계산 타당성 측면에서 문제가 발생하게 된다. 효율적인 근사를 위해 분기 경계 알고리즘을 사용하여 이 문제를 해결합니다. 여러 모양 데이터 세트를 사용하여 우리는 우리의 체계가 실행 시간이 약간만 증가하더라도 인접 샘플링 지점의 대응성을 고려하지 않는 기존 방식보다 더 적합한 매칭을 생성한다는 것을 보여줍니다.
Kazunori IWATA
Hiroshima City University
Hiroki YAMAMOTO
Hiroshima City University
Kazushi MIMURA
Hiroshima City University
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부
Kazunori IWATA, Hiroki YAMAMOTO, Kazushi MIMURA, "An Extended Scheme for Shape Matching with Local Descriptors" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 2, pp. 285-293, February 2021, doi: 10.1587/transinf.2020EDP7134.
Abstract: Shape matching with local descriptors is an underlying scheme in shape analysis. We can visually confirm the matching results and also assess them for shape classification. Generally, shape matching is implemented by determining the correspondence between shapes that are represented by their respective sets of sampled points. Some matching methods have already been proposed; the main difference between them lies in their choice of matching cost function. This function measures the dissimilarity between the local distribution of sampled points around a focusing point of one shape and the local distribution of sampled points around a referring point of another shape. A local descriptor is used to describe the distribution of sampled points around the point of the shape. In this paper, we propose an extended scheme for shape matching that can compensate for errors in existing local descriptors. It is convenient for local descriptors to adopt our scheme because it does not require the local descriptors to be modified. The main idea of our scheme is to consider the correspondence of neighboring sampled points to a focusing point when determining the correspondence of the focusing point. This is useful because it increases the chance of finding a suitable correspondence. However, considering the correspondence of neighboring points causes a problem regarding computational feasibility, because there is a substantial increase in the number of possible correspondences that need to be considered in shape matching. We solve this problem using a branch-and-bound algorithm, for efficient approximation. Using several shape datasets, we demonstrate that our scheme yields a more suitable matching than the conventional scheme that does not consider the correspondence of neighboring sampled points, even though our scheme requires only a small increase in execution time.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDP7134/_p
부
@ARTICLE{e104-d_2_285,
author={Kazunori IWATA, Hiroki YAMAMOTO, Kazushi MIMURA, },
journal={IEICE TRANSACTIONS on Information},
title={An Extended Scheme for Shape Matching with Local Descriptors},
year={2021},
volume={E104-D},
number={2},
pages={285-293},
abstract={Shape matching with local descriptors is an underlying scheme in shape analysis. We can visually confirm the matching results and also assess them for shape classification. Generally, shape matching is implemented by determining the correspondence between shapes that are represented by their respective sets of sampled points. Some matching methods have already been proposed; the main difference between them lies in their choice of matching cost function. This function measures the dissimilarity between the local distribution of sampled points around a focusing point of one shape and the local distribution of sampled points around a referring point of another shape. A local descriptor is used to describe the distribution of sampled points around the point of the shape. In this paper, we propose an extended scheme for shape matching that can compensate for errors in existing local descriptors. It is convenient for local descriptors to adopt our scheme because it does not require the local descriptors to be modified. The main idea of our scheme is to consider the correspondence of neighboring sampled points to a focusing point when determining the correspondence of the focusing point. This is useful because it increases the chance of finding a suitable correspondence. However, considering the correspondence of neighboring points causes a problem regarding computational feasibility, because there is a substantial increase in the number of possible correspondences that need to be considered in shape matching. We solve this problem using a branch-and-bound algorithm, for efficient approximation. Using several shape datasets, we demonstrate that our scheme yields a more suitable matching than the conventional scheme that does not consider the correspondence of neighboring sampled points, even though our scheme requires only a small increase in execution time.},
keywords={},
doi={10.1587/transinf.2020EDP7134},
ISSN={1745-1361},
month={February},}
부
TY - JOUR
TI - An Extended Scheme for Shape Matching with Local Descriptors
T2 - IEICE TRANSACTIONS on Information
SP - 285
EP - 293
AU - Kazunori IWATA
AU - Hiroki YAMAMOTO
AU - Kazushi MIMURA
PY - 2021
DO - 10.1587/transinf.2020EDP7134
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2021
AB - Shape matching with local descriptors is an underlying scheme in shape analysis. We can visually confirm the matching results and also assess them for shape classification. Generally, shape matching is implemented by determining the correspondence between shapes that are represented by their respective sets of sampled points. Some matching methods have already been proposed; the main difference between them lies in their choice of matching cost function. This function measures the dissimilarity between the local distribution of sampled points around a focusing point of one shape and the local distribution of sampled points around a referring point of another shape. A local descriptor is used to describe the distribution of sampled points around the point of the shape. In this paper, we propose an extended scheme for shape matching that can compensate for errors in existing local descriptors. It is convenient for local descriptors to adopt our scheme because it does not require the local descriptors to be modified. The main idea of our scheme is to consider the correspondence of neighboring sampled points to a focusing point when determining the correspondence of the focusing point. This is useful because it increases the chance of finding a suitable correspondence. However, considering the correspondence of neighboring points causes a problem regarding computational feasibility, because there is a substantial increase in the number of possible correspondences that need to be considered in shape matching. We solve this problem using a branch-and-bound algorithm, for efficient approximation. Using several shape datasets, we demonstrate that our scheme yields a more suitable matching than the conventional scheme that does not consider the correspondence of neighboring sampled points, even though our scheme requires only a small increase in execution time.
ER -