A study on giant panda recognition based on images of a large proportion of captive pandas
As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of co...
Uloženo v:
| Vydáno v: | Ecology and evolution Ročník 10; číslo 7; s. 3561 - 3573 |
|---|---|
| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
England
John Wiley & Sons, Inc
01.04.2020
John Wiley and Sons Inc Wiley |
| Témata: | |
| ISSN: | 2045-7758, 2045-7758 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image‐based panda face recognition method.
In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established.
The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection.
This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost‐effective than the approaches used in the previous panda surveys.
This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behaviour. This noninvasive approach that leverages on deep learning is much more cost‐effective than the approaches used in the previous panda surveys. |
|---|---|
| AbstractList | Abstract As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image‐based panda face recognition method. In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established. The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection. This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost‐effective than the approaches used in the previous panda surveys. As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image‐based panda face recognition method. In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established. The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection. This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost‐effective than the approaches used in the previous panda surveys. As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image-based panda face recognition method.In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established.The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection.This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost-effective than the approaches used in the previous panda surveys. As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image‐based panda face recognition method. In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established. The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection. This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost‐effective than the approaches used in the previous panda surveys. This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behaviour. This noninvasive approach that leverages on deep learning is much more cost‐effective than the approaches used in the previous panda surveys. As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image‐based panda face recognition method.In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established.The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection.This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost‐effective than the approaches used in the previous panda surveys. This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behaviour. This noninvasive approach that leverages on deep learning is much more cost‐effective than the approaches used in the previous panda surveys. As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image-based panda face recognition method.In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established.The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection.This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost-effective than the approaches used in the previous panda surveys.As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image-based panda face recognition method.In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established.The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection.This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost-effective than the approaches used in the previous panda surveys. |
| Author | Swarup, Pranjal Matkowski, Wojciech Michal Han, Su Chen, Peng Zhang, Zhihe Rong, Hou Kong, Adams Wai Kin |
| AuthorAffiliation | 3 Sichuan Academy of Giant Panda Chengdu China 5 College of Computer Science Sichuan Normal University Chengdu China 4 School of Computer Science and Engineering Nanyang Technological University Singapore City Singapore 2 Sichuan Key Laboratory of Conservation Biology for Endangered Wildlife Chengdu China 1 Chengdu Research Base of Giant Panda Breeding Chengdu China |
| AuthorAffiliation_xml | – name: 2 Sichuan Key Laboratory of Conservation Biology for Endangered Wildlife Chengdu China – name: 1 Chengdu Research Base of Giant Panda Breeding Chengdu China – name: 4 School of Computer Science and Engineering Nanyang Technological University Singapore City Singapore – name: 3 Sichuan Academy of Giant Panda Chengdu China – name: 5 College of Computer Science Sichuan Normal University Chengdu China |
| Author_xml | – sequence: 1 givenname: Peng surname: Chen fullname: Chen, Peng organization: Sichuan Academy of Giant Panda – sequence: 2 givenname: Pranjal orcidid: 0000-0002-1766-7413 surname: Swarup fullname: Swarup, Pranjal email: pswarup@ntu.edu.sg organization: Nanyang Technological University – sequence: 3 givenname: Wojciech Michal surname: Matkowski fullname: Matkowski, Wojciech Michal organization: Nanyang Technological University – sequence: 4 givenname: Adams Wai Kin surname: Kong fullname: Kong, Adams Wai Kin organization: Nanyang Technological University – sequence: 5 givenname: Su surname: Han fullname: Han, Su organization: Sichuan Normal University – sequence: 6 givenname: Zhihe surname: Zhang fullname: Zhang, Zhihe email: zzh@panda.org.cn organization: Sichuan Academy of Giant Panda – sequence: 7 givenname: Hou surname: Rong fullname: Rong, Hou organization: Sichuan Academy of Giant Panda |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32274009$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kk1r3DAQhkVJadI0h_6BYuilPWyiD-vrUgjLNgkEesmpFyHLY1eLV3Ilb8L--8q7aUgCrUBIjJ55eUcz79FRiAEQ-kjwOcGYXoADdi4Ip2_QCcU1X0jJ1dGz-zE6y3mNyxKY1li-Q8eMUlljrE_Qz8sqT9t2V8VQ9d6GqRptaG2VwMU--MmXeGMztDPgN7aHXMWustVgUw_VmOIY054qUWfHyd_DQSJ_QG87O2Q4ezxP0d331d3yenH74-pmeXm7cFxyulB102orLNPQtR1RjkKnGVAmlWtdq7iUwkkBuK6d6IR0WinVEKsEb6zk7BTdHGTbaNdmTMVk2plovdkHYuqNLQ7dAIbzUrjuOqEZrwlnSmJR15Q2WILFdNb6dtAat80GWgdhSnZ4IfryJfhfpo_3RpK6NEMUgS-PAin-3kKezMZnB8NgA8RtNpQV80RjrQv6-RW6jtsUyk8VSmOKSdmF-vTc0ZOVvx0swNcD4FLMOUH3hBBs5gEx84CYeUAKe_GKdX6yc_dKMX74X8aDH2D3b2mzWq7YPuMPa2LJnA |
| CitedBy_id | crossref_primary_10_3390_s21186109 crossref_primary_10_1111_2041_210X_14181 crossref_primary_10_1016_j_eswa_2024_126206 crossref_primary_10_3390_s22208015 crossref_primary_10_1016_j_ecoinf_2024_102950 crossref_primary_10_1002_ece3_8851 crossref_primary_10_1002_ece3_9507 crossref_primary_10_1016_j_ecoinf_2024_102797 crossref_primary_10_3390_ani14152247 crossref_primary_10_1016_j_eswa_2022_119431 crossref_primary_10_1111_1365_2656_14139 crossref_primary_10_1038_s41598_023_49522_2 crossref_primary_10_1038_s41598_025_89075_0 crossref_primary_10_1016_j_ecoinf_2022_101874 crossref_primary_10_1016_j_ecoinf_2023_102225 crossref_primary_10_1016_j_ecoinf_2023_102388 crossref_primary_10_1002_ecs2_70012 crossref_primary_10_1016_j_ecoinf_2022_101892 crossref_primary_10_1016_j_eswa_2025_128466 crossref_primary_10_1111_2041_210X_13577 crossref_primary_10_1007_s42991_021_00223_1 crossref_primary_10_1038_s41598_025_07040_3 crossref_primary_10_5187_jast_2025_e4 crossref_primary_10_1111_2041_210X_14044 crossref_primary_10_1016_j_compag_2021_106675 crossref_primary_10_3390_ani12131711 crossref_primary_10_1007_s11263_024_02006_w crossref_primary_10_1002_ece3_6840 crossref_primary_10_1093_icb_icab107 crossref_primary_10_3390_s23187928 crossref_primary_10_1007_s42991_021_00168_5 crossref_primary_10_1016_j_ecoinf_2025_103379 crossref_primary_10_3390_fishes7050219 crossref_primary_10_1007_s10344_021_01549_4 crossref_primary_10_1093_icb_icae127 crossref_primary_10_1111_2041_210X_13901 crossref_primary_10_1109_TIM_2022_3232093 crossref_primary_10_1016_j_neucom_2024_127640 crossref_primary_10_1038_s41598_023_40814_1 crossref_primary_10_1038_s41598_022_11842_0 crossref_primary_10_1002_zoo_70008 crossref_primary_10_1007_s42991_021_00215_1 crossref_primary_10_1038_s41598_021_02506_6 crossref_primary_10_1186_s40850_024_00195_y |
| Cites_doi | 10.1371/journal.pone.0159738 10.1111/j.1365-294X.2005.02577.x 10.1038/nature14539 10.1016/j.compind.2018.02.016 10.1126/sciadv.aaw0736 10.1038/s41598-019-44565-w 10.1109/CVPR.2016.90 10.1109/CVPR.2009.5206848 10.1049/iet-bmt.2016.0017 10.1007/978-3-319-46448-0_2 10.1109/ICCV.2017.74 10.1109/ICIP.2019.8803125 10.1109/ICCV.2015.169 10.1111/2041-210X.13099 10.1073/pnas.1719367115 10.2192/08PER010.1 10.1109/CISP.2012.6469668 10.1109/ICCPS.2012.6384309 10.1016/j.cub.2006.05.042 10.1111/j.1365-2664.2010.01851.x 10.1109/ICCV.2017.322 10.1111/2041-210X.13133 10.1109/TIP.2010.2099126 10.1109/TPAMI.2016.2577031 10.1007/978-3-319-10602-1_48 10.1109/CVPR.2016.91 10.1109/CVPR.2015.7298965 10.1007/978-3-319-24574-4_28 10.1007/s004420050400 10.1111/jzo.12377 10.1007/978-3-319-71273-4_3 10.1007/978-3-319-45886-1_5 10.1109/BTAS.2018.8698538 10.1111/j.1469-1795.2008.00179.x 10.1109/CISP.2012.6469751 10.1016/j.biocon.2005.09.025 |
| ContentType | Journal Article |
| Copyright | 2020 The Authors. published by John Wiley & Sons Ltd. 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2020 The Authors. published by John Wiley & Sons Ltd. – notice: 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. – notice: 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | 24P AAYXX CITATION NPM 3V. 7SN 7SS 7ST 7X2 8FD 8FE 8FH 8FK ABUWG AEUYN AFKRA ATCPS AZQEC BBNVY BENPR BHPHI C1K CCPQU DWQXO FR3 GNUQQ HCIFZ LK8 M0K M7P P64 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS RC3 SOI 7X8 5PM DOA |
| DOI | 10.1002/ece3.6152 |
| DatabaseName | Wiley Online Library Open Access CrossRef PubMed ProQuest Central (Corporate) Ecology Abstracts Entomology Abstracts (Full archive) Environment Abstracts Agricultural Science Collection Technology Research Database ProQuest SciTech Collection ProQuest Natural Science Collection ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni Edition) ProQuest One Sustainability ProQuest Central UK/Ireland Agricultural & Environmental Science Collection ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection Environmental Sciences and Pollution Management ProQuest One Community College ProQuest Central Korea Engineering Research Database ProQuest Central Student SciTech Premium Collection ProQuest Biological Science Collection Agricultural Science Database Biological Science Database Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database (ProQuest) ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Genetics Abstracts Environment Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed Agricultural Science Database Publicly Available Content Database ProQuest Central Student Technology Research Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Natural Science Collection ProQuest Central China Environmental Sciences and Pollution Management ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability Genetics Abstracts Natural Science Collection ProQuest Central Korea Agricultural & Environmental Science Collection Biological Science Collection ProQuest Central (New) ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Agricultural Science Collection Biological Science Database ProQuest SciTech Collection Ecology Abstracts Biotechnology and BioEngineering Abstracts Entomology Abstracts ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic Environment Abstracts ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | CrossRef PubMed MEDLINE - Academic Agricultural Science Database |
| Database_xml | – sequence: 1 dbid: 24P name: Wiley Online Library Open Access url: https://authorservices.wiley.com/open-science/open-access/browse-journals.html sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 4 dbid: PIMPY name: ProQuest Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Ecology Forestry |
| DocumentTitleAlternate | CHEN et al |
| EISSN | 2045-7758 |
| EndPage | 3573 |
| ExternalDocumentID | oai_doaj_org_article_553229ff6935415387064422b07ea025 PMC7141006 32274009 10_1002_ece3_6152 ECE36152 |
| Genre | article Journal Article |
| GeographicLocations | China |
| GeographicLocations_xml | – name: China |
| GrantInformation_xml | – fundername: Sichuan Science and Technology Program funderid: 2018JY0096 – fundername: Panda International Foundation of the National Forestry Administration, China funderid: AD1417; CM1422 – fundername: National Natural Science Foundation of China funderid: 31300306 – fundername: Chengdu Research Base of Giant Panda Breeding funderid: CPB2018‐01; CPB2018‐02 – fundername: Chengdu Giant Panda Breeding Research Foundation funderid: 2014‐02; 2014‐05 – fundername: Chengdu Giant Panda Breeding Research Foundation grantid: 2014‐02; 2014‐05 – fundername: Panda International Foundation of the National Forestry Administration, China grantid: AD1417; CM1422 – fundername: Sichuan Science and Technology Program grantid: 2018JY0096 – fundername: National Natural Science Foundation of China grantid: 31300306 – fundername: Chengdu Research Base of Giant Panda Breeding grantid: CPB2018‐01; CPB2018‐02 |
| GroupedDBID | 0R~ 1OC 24P 53G 5VS 7X2 8-0 8-1 8FE 8FH AAFWJ AAHBH AAHHS AAZKR ACCFJ ACCMX ACGFO ACPRK ACXQS ADBBV ADKYN ADRAZ ADZMN ADZOD AEEZP AENEX AEQDE AEUYN AFKRA AFRAH AIAGR AIWBW AJBDE ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN AOIJS ATCPS AVUZU BAWUL BBNVY BCNDV BENPR BHPHI CCPQU D-8 D-9 DIK EBS ECGQY EJD GODZA GROUPED_DOAJ GX1 HCIFZ HYE IAO IEP ITC KQ8 LK8 M0K M48 M7P M~E OK1 PIMPY PROAC RNS ROL RPM SUPJJ WIN AAMMB AAYXX AEFGJ AFFHD AFPKN AGXDD AIDQK AIDYY CITATION PHGZM PHGZT PQGLB NPM 3V. 7SN 7SS 7ST 8FD 8FK ABUWG AZQEC C1K DWQXO FR3 GNUQQ P64 PKEHL PQEST PQQKQ PQUKI PRINS PUEGO RC3 SOI 7X8 5PM |
| ID | FETCH-LOGICAL-c5752-84bd9a6a39efdf18c2ef93e2378cdcd85776c76e044c6f67c9888b1a865ba753 |
| IEDL.DBID | M7P |
| ISICitedReferencesCount | 56 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000524417200033&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2045-7758 |
| IngestDate | Fri Oct 03 12:34:55 EDT 2025 Tue Nov 04 01:46:42 EST 2025 Fri Sep 05 13:54:56 EDT 2025 Mon Sep 29 17:10:40 EDT 2025 Wed Feb 19 02:11:46 EST 2025 Sat Nov 29 04:31:22 EST 2025 Tue Nov 18 20:58:08 EST 2025 Wed Jan 22 16:33:53 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 7 |
| Keywords | population estimation giant panda individual identification panda face recognition |
| Language | English |
| License | Attribution 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c5752-84bd9a6a39efdf18c2ef93e2378cdcd85776c76e044c6f67c9888b1a865ba753 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Chen and Swarup contributed equally to this work. |
| ORCID | 0000-0002-1766-7413 |
| OpenAccessLink | https://www.proquest.com/docview/2390201020?pq-origsite=%requestingapplication% |
| PMID | 32274009 |
| PQID | 2390201020 |
| PQPubID | 2034651 |
| PageCount | 13 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_553229ff6935415387064422b07ea025 pubmedcentral_primary_oai_pubmedcentral_nih_gov_7141006 proquest_miscellaneous_2388819099 proquest_journals_2390201020 pubmed_primary_32274009 crossref_primary_10_1002_ece3_6152 crossref_citationtrail_10_1002_ece3_6152 wiley_primary_10_1002_ece3_6152_ECE36152 |
| PublicationCentury | 2000 |
| PublicationDate | April 2020 |
| PublicationDateYYYYMMDD | 2020-04-01 |
| PublicationDate_xml | – month: 04 year: 2020 text: April 2020 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England – name: Bognor Regis – name: Hoboken |
| PublicationTitle | Ecology and evolution |
| PublicationTitleAlternate | Ecol Evol |
| PublicationYear | 2020 |
| Publisher | John Wiley & Sons, Inc John Wiley and Sons Inc Wiley |
| Publisher_xml | – name: John Wiley & Sons, Inc – name: John Wiley and Sons Inc – name: Wiley |
| References | 2019; 9 2019; 5 2009; 20 2012 2015; 521 2019; 10 2006; 16 2009 2016; 300 2007 2006 2008; 11 2015b 1998; 113 2016; 11 2016; 6 2010; 20 2010; 47 1990 2015a 2018; 115 2019 2018 2017 2012b 2016 2012a 2015 2014 2018; 98 2006; 128 2005; 14 Burghardt T. (e_1_2_10_2_1) 2007 e_1_2_10_24_1 e_1_2_10_21_1 e_1_2_10_44_1 e_1_2_10_22_1 e_1_2_10_43_1 e_1_2_10_42_1 e_1_2_10_20_1 e_1_2_10_41_1 e_1_2_10_40_1 State Forestry Administration (e_1_2_10_37_1) 2006 People's Daily Online (e_1_2_10_27_1) 2018 e_1_2_10_4_1 e_1_2_10_18_1 e_1_2_10_3_1 e_1_2_10_19_1 e_1_2_10_6_1 Jaderberg M. (e_1_2_10_15_1) 2015 e_1_2_10_16_1 e_1_2_10_5_1 e_1_2_10_17_1 e_1_2_10_8_1 e_1_2_10_14_1 e_1_2_10_7_1 e_1_2_10_36_1 e_1_2_10_12_1 e_1_2_10_35_1 e_1_2_10_9_1 e_1_2_10_13_1 e_1_2_10_34_1 State Forestry Administration (e_1_2_10_39_1) 2015 e_1_2_10_10_1 e_1_2_10_33_1 e_1_2_10_11_1 e_1_2_10_32_1 e_1_2_10_31_1 e_1_2_10_30_1 McNeely J. A. (e_1_2_10_23_1) 1990 State Forestry Administration (e_1_2_10_38_1) 2015 e_1_2_10_29_1 e_1_2_10_28_1 e_1_2_10_25_1 e_1_2_10_26_1 |
| References_xml | – start-page: 234 year: 2015 end-page: 241 – volume: 14 start-page: 1991 issue: 7 year: 2005 end-page: 2005 article-title: A new method for estimating the size of small populations from genetic mark–recapture data publication-title: Molecular Ecology – volume: 300 start-page: 247 issue: 4 year: 2016 end-page: 256 article-title: Individual identification of wild giant pandas from camera trap photos–a systematic and hierarchical approach publication-title: Journal of Zoology – volume: 5 start-page: eaaw0736 issue: 9 year: 2019 article-title: Chimpanzee face recognition from videos in the wild using deep learning publication-title: Science Advances – start-page: 779 year: 2016 end-page: 788 – start-page: 1 year: 2018 end-page: 10 – volume: 98 start-page: 145 year: 2018 end-page: 152 article-title: Towards on‐farm pig face recognition using convolutional neural networks publication-title: Computers in Industry – start-page: 21 year: 2016 end-page: 37 – start-page: 2961 year: 2017 end-page: 2969 – volume: 11 start-page: 182 issue: 3 year: 2008 end-page: 184 article-title: Design, evaluate, refine: Camera trap studies for elusive species publication-title: Animal Conservation – volume: 128 start-page: 158 issue: 2 year: 2006 end-page: 168 article-title: An evaluation of field and non‐invasive genetic methods to estimate brown bear ( ) population size publication-title: Biological Conservation – start-page: 770 year: 2016 end-page: 778 – year: 2007 – volume: 10 start-page: 80 issue: 1 year: 2019 end-page: 91 article-title: Identifying animal species in camera trap images using deep learning and citizen science publication-title: Methods in Ecology and Evolution – start-page: 27 year: 2017 end-page: 38 – start-page: 248 year: 2009 end-page: 255 – start-page: 1680 year: 2019 end-page: 1684 – start-page: 51 year: 2016 end-page: 63 – volume: 11 issue: 8 year: 2016 article-title: Role of new nature reserve in assisting endangered species conservation‐case study of giant pandas in the northern Qionglai Mountains, China publication-title: PLoS ONE – start-page: 3431 year: 2015 end-page: 3440 – volume: 16 start-page: R451 issue: 12 year: 2006 end-page: R452 article-title: Molecular censusing doubles giant panda population estimate in a key nature reserve publication-title: Current Biology – year: 1990 – year: 2018 – volume: 47 start-page: 1103 issue: 5 year: 2010 end-page: 1109 article-title: Pre‐screening acoustic and other natural signatures for use in noninvasive individual identification publication-title: Journal of Applied Ecology – year: 2015a – volume: 20 start-page: 56 issue: 1 year: 2009 end-page: 63 article-title: Accurate population size estimates are vital parameters for conserving the giant panda publication-title: Ursus – volume: 20 start-page: 1696 issue: 6 year: 2010 end-page: 1708 article-title: From tiger to panda: Animal head detection publication-title: IEEE Transactions on Image Processing – volume: 115 start-page: E5716 issue: 25 year: 2018 end-page: E5725 article-title: Automatically identifying, counting, and describing wild animals in camera‐trap images with deep learning publication-title: Proceedings of the National Academy of Sciences of the United States of America – start-page: 1440 year: 2015 end-page: 1448 – start-page: 358 year: 2012b end-page: 362 – volume: 6 start-page: 139 issue: 3 year: 2016 end-page: 156 article-title: Visual animal biometrics: Survey publication-title: IET Biometrics – start-page: 1137 year: 2017 end-page: 1149 – start-page: 618 year: 2017 end-page: 626 – volume: 10 start-page: 461 issue: 4 year: 2019 end-page: 470 article-title: Past, present and future approaches using computer vision for animal re‐identification from camera trap data publication-title: Methods in Ecology and Evolution – year: 2006 – volume: 521 start-page: 436 issue: 7553 year: 2015 article-title: Deep learning publication-title: Nature – start-page: 2017 year: 2015 end-page: 2025 – volume: 113 start-page: 474 issue: 4 year: 1998 end-page: 491 article-title: Study design and interpretation of mammalian carnivore density estimates publication-title: Oecologia – start-page: 740 year: 2014 end-page: 755 – start-page: 911 year: 2012 end-page: 915 – start-page: 814 year: 2012a end-page: 818 – year: 2015b – volume: 9 start-page: 8137 issue: 1 year: 2019 article-title: Insights and approaches using deep learning to classify wildlife publication-title: Scientific Reports – ident: e_1_2_10_11_1 doi: 10.1371/journal.pone.0159738 – ident: e_1_2_10_25_1 doi: 10.1111/j.1365-294X.2005.02577.x – volume-title: Captive panadas rise to 548 globally year: 2018 ident: e_1_2_10_27_1 – ident: e_1_2_10_18_1 doi: 10.1038/nature14539 – ident: e_1_2_10_12_1 doi: 10.1016/j.compind.2018.02.016 – ident: e_1_2_10_33_1 doi: 10.1126/sciadv.aaw0736 – ident: e_1_2_10_24_1 doi: 10.1038/s41598-019-44565-w – ident: e_1_2_10_14_1 doi: 10.1109/CVPR.2016.90 – ident: e_1_2_10_8_1 doi: 10.1109/CVPR.2009.5206848 – ident: e_1_2_10_17_1 doi: 10.1049/iet-bmt.2016.0017 – ident: e_1_2_10_20_1 doi: 10.1007/978-3-319-46448-0_2 – volume-title: International Conference on Computer Vision Systems: Proceedings 2007 year: 2007 ident: e_1_2_10_2_1 – ident: e_1_2_10_34_1 doi: 10.1109/ICCV.2017.74 – ident: e_1_2_10_22_1 doi: 10.1109/ICIP.2019.8803125 – ident: e_1_2_10_10_1 doi: 10.1109/ICCV.2015.169 – ident: e_1_2_10_40_1 doi: 10.1111/2041-210X.13099 – ident: e_1_2_10_26_1 doi: 10.1073/pnas.1719367115 – ident: e_1_2_10_41_1 doi: 10.2192/08PER010.1 – ident: e_1_2_10_4_1 doi: 10.1109/CISP.2012.6469668 – volume-title: The giant panda of Sichuan ‐ The fourth giant panda survey of Sichuan Province year: 2015 ident: e_1_2_10_38_1 – ident: e_1_2_10_6_1 doi: 10.1109/ICCPS.2012.6384309 – ident: e_1_2_10_42_1 doi: 10.1016/j.cub.2006.05.042 – ident: e_1_2_10_28_1 doi: 10.1111/j.1365-2664.2010.01851.x – ident: e_1_2_10_13_1 doi: 10.1109/ICCV.2017.322 – volume-title: The third national survey report on giant panda in China year: 2006 ident: e_1_2_10_37_1 – ident: e_1_2_10_32_1 doi: 10.1111/2041-210X.13133 – ident: e_1_2_10_43_1 doi: 10.1109/TIP.2010.2099126 – ident: e_1_2_10_30_1 doi: 10.1109/TPAMI.2016.2577031 – ident: e_1_2_10_19_1 doi: 10.1007/978-3-319-10602-1_48 – ident: e_1_2_10_29_1 doi: 10.1109/CVPR.2016.91 – ident: e_1_2_10_21_1 doi: 10.1109/CVPR.2015.7298965 – ident: e_1_2_10_31_1 doi: 10.1007/978-3-319-24574-4_28 – ident: e_1_2_10_35_1 doi: 10.1007/s004420050400 – ident: e_1_2_10_44_1 doi: 10.1111/jzo.12377 – ident: e_1_2_10_3_1 doi: 10.1007/978-3-319-71273-4_3 – volume-title: Release of the fourth national survey report on giant panda in China year: 2015 ident: e_1_2_10_39_1 – ident: e_1_2_10_9_1 doi: 10.1007/978-3-319-45886-1_5 – start-page: 2017 volume-title: Advances in neural information processing systems year: 2015 ident: e_1_2_10_15_1 – ident: e_1_2_10_7_1 doi: 10.1109/BTAS.2018.8698538 – volume-title: Conserving the world's biological diversity year: 1990 ident: e_1_2_10_23_1 – ident: e_1_2_10_16_1 doi: 10.1111/j.1469-1795.2008.00179.x – ident: e_1_2_10_5_1 doi: 10.1109/CISP.2012.6469751 – ident: e_1_2_10_36_1 doi: 10.1016/j.biocon.2005.09.025 |
| SSID | ssj0000602407 |
| Score | 2.440772 |
| Snippet | As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda... Abstract As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on... |
| SourceID | doaj pubmedcentral proquest pubmed crossref wiley |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 3561 |
| SubjectTerms | Algorithms Animals Artificial neural networks Biometrics Cameras Computer vision Conservation Datasets Deep learning Digital imaging Ecology Endangered species Face recognition Forestry giant panda Identification Identification methods Image segmentation individual identification Machine learning Methods Molecular biology Neural networks Object recognition Original Research panda face recognition Pandas Pattern recognition Population population estimation Population number Researchers Studies Wildlife conservation |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1La9wwEBYltNBLafp0kxS19NCLG69eYx3TsKGHEnrIIfQiZFlqF1LvsrsJ5N9nRvaaXZrQSy_GSIMtj-YlafwNY590gzGIhraM9P-W0j6U1qhYThJolWTwpslVS77D-Xl9eWl_bJX6opywHh64Z9yx1ihyNiVjpVaknoBOVAnRVBA9Omyyvhj1bC2mehtM2F2wgRKqxHEMUX5B9y12HFDG6b8vuPw7R3I7ds3O5-w5ezZEjfykH-0-exS7F-zJNCNO375kP094honl847_wvle8wVtEPAxOwjbyV21RDD7gyZkxeeJe35FeeB8QZUSlpkKW4NfkAnsH7F6xS7Ophen38qhaEIZMPJC66aa1nrjpY2pTZM6iJisjEJCHdrQ1hrABDCxUiqYZCBYXAM3E18b3Xhcu7xme928i28ZFzIB6msdwFulJV59kqquWqCzzWZSsM8bRrowAIpTXYsr10MhC0c8d8Tzgn0cSRc9isZ9RF9pNkYCAr7ODSgObhAH9y9xKNjhZi7doI0rJ6St6NRfVAX7MHajHtHhiO_i_JpokA8YHVlbsDf91I8jwVcC2jrsgR2h2Bnqbk83-52xuoHyaCuDvMri8_DXu-npVNLNu__BhgP2VNDGQE4xOmR76-V1PGKPw816tlq-z7pyBxypFAs priority: 102 providerName: Directory of Open Access Journals – databaseName: Wiley Online Library Open Access dbid: 24P link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELaqAhIXypuUggziwCU08dvqqVRbcUBVDz1UXCLHsctKbbLabJH67zvjZAOrFgmJSxTZk4edmfEXe_wNIZ9kDRhE6iYPuH9LSOdzq0TIy6iliNw7VaesJd_1yYk5P7enW-RgvRdm4IeYJtzQMpK_RgN3db__mzQ0-MC_wHgM_vdBWXKDeRuYOJ0mWAqF9F24XRoZ1wFFSrNmFirY_nT1xniUaPvvw5p3Qyb_hLJpLDre-a9WPCVPRghKDwedeUa2QvucPJol-uqbF-THIU2cs7Rr6QUoz4oucLaBTqFGUI5jX4MC8yvwRz3tInX0EoPK6QLTLiyTFJR6t0B_Otyif0nOjmdnR9_yMQND7gHGgasUdWOdctyG2MTSeBai5YFxbXzjGyO1Vl6rUAjhVVTaW_ihrktnlKwd_Ai9Ittt14Y3hDIeNRi_8dpZITkcXeTCFI3GhdK6zMjn9Weo_MhOjkkyLquBV5lV2FMV9lRGPk6ii4GS4z6hr_gtJwFk0U4F3fKiGo2ykhLcmY1RWS4Fun4NAE0wVhc6OACDGdlba0I1mnZfMW4LDCFgRUY-TNVglLjS4trQXaMM9ANALWsz8npQnOlN4JEaHCfU6A2V2njVzZp2_jMRf2sMyi0U9FVSqb-3vpodzTie7P676FvymOFcQopK2iPbq-V1eEce-l-reb98n2zrFlnuI2Y priority: 102 providerName: Wiley-Blackwell |
| Title | A study on giant panda recognition based on images of a large proportion of captive pandas |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fece3.6152 https://www.ncbi.nlm.nih.gov/pubmed/32274009 https://www.proquest.com/docview/2390201020 https://www.proquest.com/docview/2388819099 https://pubmed.ncbi.nlm.nih.gov/PMC7141006 https://doaj.org/article/553229ff6935415387064422b07ea025 |
| Volume | 10 |
| WOSCitedRecordID | wos000524417200033&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2045-7758 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000602407 issn: 2045-7758 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2045-7758 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000602407 issn: 2045-7758 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Agriculture Science Database customDbUrl: eissn: 2045-7758 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000602407 issn: 2045-7758 databaseCode: M0K dateStart: 20110901 isFulltext: true titleUrlDefault: https://search.proquest.com/agriculturejournals providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 2045-7758 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000602407 issn: 2045-7758 databaseCode: M7P dateStart: 20110901 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2045-7758 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000602407 issn: 2045-7758 databaseCode: BENPR dateStart: 20110901 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content Database customDbUrl: eissn: 2045-7758 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000602407 issn: 2045-7758 databaseCode: PIMPY dateStart: 20110901 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVWIB databaseName: Wiley Online Library Free Content customDbUrl: eissn: 2045-7758 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000602407 issn: 2045-7758 databaseCode: WIN dateStart: 20110101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell – providerCode: PRVWIB databaseName: Wiley Online Library Open Access customDbUrl: eissn: 2045-7758 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000602407 issn: 2045-7758 databaseCode: 24P dateStart: 20110101 isFulltext: true titleUrlDefault: https://authorservices.wiley.com/open-science/open-access/browse-journals.html providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELZoC4gLj_IKlJVBHLiEZm3Hjk-orbaigq4iVImFS-Q4dlmpJGGzReLfM-PNBlYULlysxB4ldmY8Ho8n3xDyMi3BBklVFTv8f0ukxsZaChePvUqF59bIMmQtea-m02w203nvcOv6sMq1TgyKumos-sj3GWzO8eSWJW_abzFmjcLT1T6FxhbZQZQEHkL38sHHkkhE8FJrQKGE7Tvr-GtYxNnGMhTQ-q8yMf-MlPzdgg1L0PGd_-38XXK7Nz7pwUpa7pFrrt4lNyYBuPrHLrmJeTox-dt98vmABuBZ2tT0HCRoSVt0OdAh3gjqcQGskGD-FZRSRxtPDb3AyHLaYu6FRaCCWmtaVKqrR3QPyNnx5OzobdynYYgt2HKgL0VZaSMN185XfpxZ5rzmjnGV2cpWWaqUtEq6RAgrvVRWw666HJtMpqWB3dBDsl03tXtMKONegQbIrDJapBxK47nIkkrhaWk5jsirNVMK20OUY6aMi2IFrswK5F-B_IvIi4G0XeFyXEV0iJwdCBBKO1Q0i_Oin5lFmoJO095LzVOB-l-BlSYYKxPlDFiEEdlb87To53dX_GJoRJ4PzTAz8bjF1K65RBr4DmBvaR2RRysxGnoCr1SgPaFFbQjYRlc3W-r5l4D-rTAyN5HwrYIo_n30xeRowvHiyb9H8JTcYuhECOFIe2R7ubh0z8h1-3057xYjssVEDqWaZSOycziZ5h9GwXEB5WnybhRmHLTkJ6f5J7j7eDL9CdVCL9o |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB5V5XnhUV4LBQwCqZfQrJPY8QGhUrZq1WXVwx4qLpbj2GWlkiy7W1B_FP-RGWcTWFG49cBlFdmjbOzM47M9mQ_gVVYgBslkGTn6fivNjI2USF3U9zJLfWKNKAJryVCORvnxsTpagx_ttzCUVtn6xOCoy9rSHvk2x8U5ndzy-N30a0SsUXS62lJoNGpx6M6_45Jt_vbgA77f15zvDca7-9GSVSCyCE3Q_NOiVEaYRDlf-n5uufMqcTyRuS1tmWdSCiuFi9PUCi-kVbhILPomF1lhJJFEoMe_giiC5yFT8Kjb0okFFQyTbf2imG8765I3iBn4StQL5AAXIdo_EzN_B8wh4u3d_s_m6g7cWkJrttPYwl1Yc9UGXBuEstznG3CdWEiJ2u4efNphoawuqyt2gvaxYFPaUGFdNhW2U3gvSWDyBV3unNWeGXZKefNsSswSsyCFrdZMKWQ0t5jfh_FlDPEBrFd15R4B44mX6N9yK41KswR_jU_SPC4lnQUX_R5stTqg7bIAO_GAnOqmdDTXpC6a1KUHLzvRaVN15CKh96RInQAVCg8N9exEL_2OzjL02Mp7oZIspegmEYOmnBexdAbxbg82WxXSS-8117_0pwcvum70O3SYZCpXn5EMzgOiSaV68LDR2u5J8C8lxgbskSv6vPKoqz3V5HOobS4p7zgWOFdB8_8-ej3YHSR08fjfI3gON_bHH4d6eDA6fAI3OW2XhMSrTVhfzM7cU7hqvy0m89mzYMwM9CUbxE_QDoRV |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELaqAhUXHuUVKGAQSFzCZp3Ejg8IlXZXVK1We-ih4mI5jl1WKknYbEH9afw7ZpwHrCjceuCyiuxRNnbmZXvyfYS8SnPIQVJRhBa_30pSbULJExuOnUgTFxvNc89aciRms-zkRM43yI_-Wxgsq-x9onfURWVwj3zEYHGOJ7csGrmuLGK-P31ffw2RQQpPWns6jVZFDu3Fd1i-Ne8O9uFdv2ZsOjne-xh2DAOhgTQFXEGSF1JzHUvrCjfODLNOxpbFIjOFKbJUCG4Et1GSGO64MBIWjPlYZzzNtUDCCPD-1wRilvuqwfmwvRNxBA8TPZZRxEbW2Pgt5A9sLQJ6ooDLsts_izR_T5599Jve_o_n7Q651aXcdLe1kbtkw5bb5MbEw3VfbJMtZCdFyrt75NMu9XC7tCrpKdjNita40UKHKitox7BfoMDiC7jihlaOanqG9fS0RsaJpZeCVqNrDCXtLZr75PgqhviAbJZVaR8RymInwO9lRmiZpDH8ahcnWVQIPCPOxwF50-uDMh0wO_KDnKkWUpopVB2FqhOQl4No3aKRXCb0AZVqEEAAcd9QLU9V549UmoInl85xGacJRj0BuWnCWB4JqyEPDshOr06q82qN-qVLAXkxdIM_wkMmXdrqHGVgHiDLlDIgD1sNHp4E_lJAzIAesabba4-63lMuPnvMc4H1yBGHufJW8PfRq8neJMaLx_8ewXOyBXagjg5mh0_ITYa7KL4ea4dsrpbn9im5br6tFs3ymbdrStQV28NPBoWNEg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+study+on+giant+panda+recognition+based+on+images+of+a+large+proportion+of+captive+pandas&rft.jtitle=Ecology+and+evolution&rft.au=Chen%2C+Peng&rft.au=Swarup%2C+Pranjal&rft.au=Wojciech+Michal+Matkowski&rft.au=Adams+Wai+Kin+Kong&rft.date=2020-04-01&rft.pub=John+Wiley+%26+Sons%2C+Inc&rft.eissn=2045-7758&rft.volume=10&rft.issue=7&rft.spage=3561&rft.epage=3573&rft_id=info:doi/10.1002%2Fece3.6152&rft.externalDBID=HAS_PDF_LINK |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2045-7758&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2045-7758&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2045-7758&client=summon |