FuzzyPSReg: Strategies of Fuzzy Cluster-Based Point Set Registration Article Swipe
Qianfang Liao
,
Da Sun
,
Henrik Andreasson
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/tro.2021.3123898
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/tro.2021.3123898
This paper studies the fuzzy cluster-based point set registration (FuzzyPSReg). First, we propose a new metric based on Gustafson-Kessel (GK) fuzzy clustering to measure the alignment of two point clouds. Unlike the metric based on fuzzy c-means (FCM) clustering in our previous work, the GK-based metric includes orientation properties of the point clouds, thereby providing more information for registration. We then develop the registration quality assessment of the GK-based metric, which is more sensitive to small misalignments than that of the FCM-based metric. Next, by effectively combining the two metrics, we design two FuzzyPSReg strategies with global optimization: i). \textit{FuzzyPSReg-SS}, which extends our previous work and aligns two similar-sized point clouds with greatly improved efficiency; ii). \textit{FuzzyPSReg-O2S}, which aligns two point clouds with a relatively large difference in size and can be used to estimate the pose of an object in a scene. In the experiment, we use different point clouds to test and compare the proposed method with state-of-the-art registration approaches. The results demonstrate the advantages and effectiveness of our method.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tro.2021.3123898
- OA Status
- green
- Cited By
- 15
- References
- 42
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3210969740Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tro.2021.3123898Digital Object Identifier
- Title
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FuzzyPSReg: Strategies of Fuzzy Cluster-Based Point Set RegistrationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-11-22Full publication date if available
- Authors
-
Qianfang Liao, Da Sun, Henrik AndreassonList of authors in order
- Landing page
-
https://doi.org/10.1109/tro.2021.3123898Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-95245Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Fuzzy logic, Fuzzy set, Cluster (spacecraft), Point (geometry), Set (abstract data type), Computer vision, Mathematics, Programming language, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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15Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 5, 2023: 4, 2022: 4Per-year citation counts (last 5 years)
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42Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.new | 14 |
| abstract_inverted_index.our | 40, 102, 170 |
| abstract_inverted_index.set | 7 |
| abstract_inverted_index.the | 3, 24, 31, 43, 50, 62, 67, 80, 87, 135, 144, 155, 165 |
| abstract_inverted_index.two | 27, 88, 92, 107, 119 |
| abstract_inverted_index.use | 147 |
| abstract_inverted_index.(GK) | 19 |
| abstract_inverted_index.This | 0 |
| abstract_inverted_index.ii). | 115 |
| abstract_inverted_index.more | 55, 72 |
| abstract_inverted_index.pose | 136 |
| abstract_inverted_index.size | 128 |
| abstract_inverted_index.test | 152 |
| abstract_inverted_index.than | 77 |
| abstract_inverted_index.that | 78 |
| abstract_inverted_index.then | 60 |
| abstract_inverted_index.used | 132 |
| abstract_inverted_index.with | 95, 111, 122, 158 |
| abstract_inverted_index.work | 104 |
| abstract_inverted_index.(FCM) | 37 |
| abstract_inverted_index.Next, | 83 |
| abstract_inverted_index.based | 16, 33 |
| abstract_inverted_index.fuzzy | 4, 20, 35 |
| abstract_inverted_index.large | 125 |
| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.point | 6, 28, 51, 109, 120, 149 |
| abstract_inverted_index.small | 75 |
| abstract_inverted_index.which | 70, 100, 117 |
| abstract_inverted_index.work, | 42 |
| abstract_inverted_index.First, | 10 |
| abstract_inverted_index.Unlike | 30 |
| abstract_inverted_index.aligns | 106, 118 |
| abstract_inverted_index.clouds | 110, 121, 150 |
| abstract_inverted_index.design | 91 |
| abstract_inverted_index.global | 96 |
| abstract_inverted_index.method | 157 |
| abstract_inverted_index.metric | 15, 32, 45 |
| abstract_inverted_index.object | 139 |
| abstract_inverted_index.scene. | 142 |
| abstract_inverted_index.c-means | 36 |
| abstract_inverted_index.clouds, | 52 |
| abstract_inverted_index.clouds. | 29 |
| abstract_inverted_index.compare | 154 |
| abstract_inverted_index.develop | 61 |
| abstract_inverted_index.extends | 101 |
| abstract_inverted_index.greatly | 112 |
| abstract_inverted_index.measure | 23 |
| abstract_inverted_index.method. | 171 |
| abstract_inverted_index.metric, | 69 |
| abstract_inverted_index.metric. | 82 |
| abstract_inverted_index.propose | 12 |
| abstract_inverted_index.quality | 64 |
| abstract_inverted_index.results | 163 |
| abstract_inverted_index.studies | 2 |
| abstract_inverted_index.thereby | 53 |
| abstract_inverted_index.GK-based | 44, 68 |
| abstract_inverted_index.estimate | 134 |
| abstract_inverted_index.improved | 113 |
| abstract_inverted_index.includes | 46 |
| abstract_inverted_index.metrics, | 89 |
| abstract_inverted_index.previous | 41, 103 |
| abstract_inverted_index.proposed | 156 |
| abstract_inverted_index.FCM-based | 81 |
| abstract_inverted_index.alignment | 25 |
| abstract_inverted_index.combining | 86 |
| abstract_inverted_index.different | 148 |
| abstract_inverted_index.providing | 54 |
| abstract_inverted_index.sensitive | 73 |
| abstract_inverted_index.FuzzyPSReg | 93 |
| abstract_inverted_index.advantages | 166 |
| abstract_inverted_index.assessment | 65 |
| abstract_inverted_index.clustering | 21, 38 |
| abstract_inverted_index.difference | 126 |
| abstract_inverted_index.properties | 48 |
| abstract_inverted_index.relatively | 124 |
| abstract_inverted_index.strategies | 94 |
| abstract_inverted_index.approaches. | 161 |
| abstract_inverted_index.demonstrate | 164 |
| abstract_inverted_index.effectively | 85 |
| abstract_inverted_index.efficiency; | 114 |
| abstract_inverted_index.experiment, | 145 |
| abstract_inverted_index.information | 56 |
| abstract_inverted_index.orientation | 47 |
| abstract_inverted_index.registration | 8, 63, 160 |
| abstract_inverted_index.(FuzzyPSReg). | 9 |
| abstract_inverted_index.cluster-based | 5 |
| abstract_inverted_index.effectiveness | 168 |
| abstract_inverted_index.misalignments | 76 |
| abstract_inverted_index.optimization: | 97 |
| abstract_inverted_index.registration. | 58 |
| abstract_inverted_index.similar-sized | 108 |
| abstract_inverted_index.Gustafson-Kessel | 18 |
| abstract_inverted_index.state-of-the-art | 159 |
| abstract_inverted_index.\textit{FuzzyPSReg-SS}, | 99 |
| abstract_inverted_index.\textit{FuzzyPSReg-O2S}, | 116 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.93951431 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |