20.7.3.7.16 Birds, Detection, Identification

Tao, Y., Chen, Z., Griffis, C.L.,
Chick feather pattern recognition,
VISP(151), No. 5, October 2004, pp. 337-344.
IEEE Abstract. 0501
BibRef

Song, D., Xu, Y.,
A Low False Negative Filter for Detecting Rare Bird Species From Short Video Segments Using a Probable Observation Data Set-Based EKF Method,
IP(19), No. 9, September 2010, pp. 2321-2331.
IEEE DOI 1008
BibRef

Huang, C.[Chao], Meng, F.M.[Fan-Man], Luo, W.[Wang], Zhu, S.Y.[Shu-Yuan],
Bird breed classification and annotation using saliency based graphical model,
JVCIR(25), No. 6, 2014, pp. 1299-1307.
Elsevier DOI 1407
Graphical model BibRef

Gavves, E.[Efstratios], Fernando, B.[Basura], Snoek, C.G.M.[Cees G.M.], Smeulders, A.W.M.[Arnold W.M.], Tuytelaars, T.[Tinne],
Local Alignments for Fine-Grained Categorization,
IJCV(111), No. 2, January 2015, pp. 191-212.
Springer DOI 1502
BibRef
Earlier:
Fine-Grained Categorization by Alignments,
ICCV13(1713-1720)
IEEE DOI 1403
fine-grained categorization. Locate distinctive details by alignment to general models. Apply to birds and dogs. BibRef

Zhang, X.P.[Xiao-Peng], Xiong, H.K.[Hong-Kai], Zhou, W.G.[Wen-Gang], Tian, Q.[Qi],
Fused One-vs-All Features With Semantic Alignments for Fine-Grained Visual Categorization,
IP(25), No. 2, February 2016, pp. 878-892.
IEEE DOI 1601
Birds BibRef

Zhang, X.P.[Xiao-Peng], Xiong, H.K.[Hong-Kai], Zhou, W.G.[Wen-Gang], Lin, W.Y.[Wei-Yao], Tian, Q.[Qi],
Picking Neural Activations for Fine-Grained Recognition,
MultMed(19), No. 12, December 2017, pp. 2736-2750.
IEEE DOI 1712
BibRef
Earlier:
Picking Deep Filter Responses for Fine-Grained Image Recognition,
CVPR16(1134-1142)
IEEE DOI 1612
Automobiles, Birds, Detectors, Dogs, Neurons, Testing, Training, Fine-grained recognition, weakly supervised part discovery BibRef

Zhang, L., Yang, Y., Wang, M., Hong, R., Nie, L., Li, X.,
Detecting Densely Distributed Graph Patterns for Fine-Grained Image Categorization,
IP(25), No. 2, February 2016, pp. 553-565.
IEEE DOI 1601
Birds BibRef

Atanbori, J.[John], Duan, W.T.[Wen-Ting], Murray, J.[John], Appiah, K.[Kofi], Dickinson, P.[Patrick],
Automatic classification of flying bird species using computer vision techniques,
PRL(81), No. 1, 2016, pp. 53-62.
Elsevier DOI 1609
Fine-grained classification BibRef

Scholz, N.[Nikolas], Moll, J.[Jochen], Mälzer, M.[Moritz], Nagovitsyn, K.[Konstantin], Krozer, V.[Viktor],
Random bounce algorithm: Real-time image processing for the detection of bats and birds,
SIViP(10), No. 8, November 2016, pp. 1449-1456.
Springer DOI 1610
BibRef

Wei, X.S.[Xiu-Shen], Xie, C.W.[Chen-Wei], Wu, J.X.[Jian-Xin], Shen, C.H.[Chun-Hua],
Mask-CNN: Localizing parts and selecting descriptors for fine-grained bird species categorization,
PR(76), No. 1, 2018, pp. 704-714.
Elsevier DOI 1801
Fine-grained image recognition BibRef

Peng, Y.X.[Yu-Xin], He, X.T.[Xiang-Teng], Zhao, J.J.[Jun-Jie],
Object-Part Attention Model for Fine-Grained Image Classification,
IP(27), No. 3, March 2018, pp. 1487-1500.
IEEE DOI 1801
Automobiles, Birds, Feature extraction, Image classification, Noise measurement, Redundancy, Visualization, weakly supervised learning BibRef

Xiao, T.J.[Tian-Jun], Xu, Y.C.[Yi-Chong], Yang, K.Y.[Kui-Yuan], Zhang, J.X.[Jia-Xing], Peng, Y.X.[Yu-Xin], Zhang, Z.[Zheng],
The application of two-level attention models in deep convolutional neural network for fine-grained image classification,
CVPR15(842-850)
IEEE DOI 1510
BibRef

Zhao, J.J.[Jun-Jie], Peng, Y.X.[Yu-Xin],
Cost-Sensitive Deep Metric Learning for Fine-Grained Image Classification,
MMMod18(I:130-141).
Springer DOI 1802
BibRef

Tian, S., Cao, X., Li, Y., Zhen, X., Zhang, B.,
Glance and Stare: Trapping Flying Birds in Aerial Videos by Adaptive Deep Spatio-Temporal Features,
CirSysVideo(29), No. 9, September 2019, pp. 2748-2759.
IEEE DOI 1909
Birds, Proposals, Videos, Feature extraction, Object detection, Flying bird detection, glance-and-stare detection, joint localization and classification BibRef

Richter, R.[Ronny], Heim, A.[Arend], Heim, W.[Wieland], Kamp, J.[Johannes], Vohland, M.[Michael],
Combining Multiband Remote Sensing and Hierarchical Distance Sampling to Establish Drivers of Bird Abundance,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Simon, M.[Marcel], Rodner, E.[Erik], Darrell, T.J.[Trevor J.], Denzler, J.[Joachim],
The Whole Is More Than Its Parts? From Explicit to Implicit Pose Normalization,
PAMI(42), No. 3, March 2020, pp. 749-763.
IEEE DOI 2002
Task analysis, Detectors, Analytical models, Visualization, Encoding, Proposals, Birds, Fine-grained classification, object recognition, convolutional neural networks BibRef

Freytag, A.[Alexander], Rodner, E.[Erik], Darrell, T.J.[Trevor J.], Denzler, J.[Joachim],
Exemplar-Specific Patch Features for Fine-Grained Recognition,
GCPR14(144-156).
Springer DOI 1411
BibRef

He, X., Peng, Y.,
Fine-Grained Visual-Textual Representation Learning,
CirSysVideo(30), No. 2, February 2020, pp. 520-531.
IEEE DOI 2002
Visualization, Detectors, Feature extraction, Birds, Beak, Data mining, Proposals, Fine-grained visual categorization, visual-textual representation learning BibRef

Kranstauber, B.[Bart], Bouten, W.[Willem], Leijnse, H.[Hidde], Wijers, B.C.[Berend-Christiaan], Verlinden, L.[Liesbeth], Shamoun-Baranes, J.[Judy], Dokter, A.M.[Adriaan M.],
High-Resolution Spatial Distribution of Bird Movements Estimated from a Weather Radar Network,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Francis, R.J.[Roxane J.], Lyons, M.B.[Mitchell B.], Kingsford, R.T.[Richard T.], Brandis, K.J.[Kate J.],
Counting Mixed Breeding Aggregations of Animal Species Using Drones: Lessons from Waterbirds on Semi-Automation,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Jalil, N.[Nauman], Smith, S.C.[Scott C.], Green, R.[Roger],
Performance optimization of rotation-tolerant Viola-Jones-based blackbird detection,
RealTimeIP(17), No. 3, June 2020, pp. 471-478.
Springer DOI 2006
BibRef

Bowler, E.[Ellen], Fretwell, P.T.[Peter T.], French, G.[Geoffrey], Mackiewicz, M.[Michal],
Using Deep Learning to Count Albatrosses from Space: Assessing Results in Light of Ground Truth Uncertainty,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Regos, A.[Adrián], Gómez-Rodríguez, P.[Pablo], Arenas-Castro, S.[Salvador], Tapia, L.[Luis], Vidal, M.[María], Domínguez, J.[Jesús],
Model-Assisted Bird Monitoring Based on Remotely Sensed Ecosystem Functioning and Atlas Data,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Clipp, H.L.[Hannah L.], Cohen, E.B.[Emily B.], Smolinsky, J.A.[Jaclyn A.], Horton, K.G.[Kyle G.], Farnsworth, A.[Andrew], Buler, J.J.[Jeffrey J.],
Broad-Scale Weather Patterns Encountered during Flight Influence Landbird Stopover Distributions,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Nussbaumer, R.[Raphaël], Schmid, B.[Baptiste], Bauer, S.[Silke], Liechti, F.[Felix],
Technical a Gaussian Mixture Model to Separate Birds and Insects in Single-Polarization Weather Radar Data,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Moreira, F.S.[Francisco S.], Regos, A.[Adrián], Gonçalves, J.F.[João F.], Rodrigues, T.M.[Tiago M.], Verde, A.[André], Pagès, M.[Marc], Pérez, J.A.[José A.], Meunier, B.[Bruno], Lepetit, J.P.[Jean-Pierre], Honrado, J.P.[João P.], Gonçalves, D.[David],
Combining Citizen Science Data and Satellite Descriptors of Ecosystem Functioning to Monitor the Abundance of a Migratory Bird during the Non-Breeding Season,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Wu, E.[Entao], Wang, H.C.[Hong-Chang], Lu, H.X.[Hua-Xiang], Zhu, W.Q.[Wen-Qi], Jia, Y.F.[Yi-Fei], Wen, L.[Li], Choi, C.Y.[Chi-Yeung], Guo, H.M.[Hui-Min], Li, B.[Bin], Sun, L.[Lili], Lei, G.C.[Guang-Chun], Lei, J.L.[Jia-Lin], Jian, H.F.[Hai-Fang],
Unlocking the Potential of Deep Learning for Migratory Waterbirds Monitoring Using Surveillance Video,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Chen, X.L.[Xiao-Long], Zhang, H.[Hai], Song, J.[Jie], Guan, J.[Jian], Li, J.F.[Jie-Fang], He, Z.[Ziwen],
Micro-Motion Classification of Flying Bird and Rotor Drones via Data Augmentation and Modified Multi-Scale CNN,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
BibRef

Weisshaupt, N.[Nadja], Leskinen, M.[Matti], Moisseev, D.N.[Dmitri N.], Koistinen, J.[Jarmo],
Anthropogenic Illumination as Guiding Light for Nocturnal Bird Migrants Identified by Remote Sensing,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Dai, T.[Ting], Xu, S.[Shiyou], Tian, B.[Biao], Hu, J.[Jun], Zhang, Y.[Yue], Chen, Z.P.[Zeng-Ping],
Extraction of Micro-Doppler Feature Using LMD Algorithm Combined Supplement Feature for UAVs and Birds Classification,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Washburn, B.E.[Brian E.], Maher, D.[David], Beckerman, S.F.[Scott F.], Majumdar, S.[Siddhartha], Pullins, C.K.[Craig K.], Guerrant, T.L.[Travis L.],
Monitoring Raptor Movements with Satellite Telemetry and Avian Radar Systems: An Evaluation for Synchronicity,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Shi, Q.Q.[Qian-Qian], Fan, J.S.[Jun-Song], Wang, Z.[Zuoren], Zhang, Z.X.[Zhao-Xiang],
Multimodal channel-wise attention transformer inspired by multisensory integration mechanisms of the brain,
PR(130), 2022, pp. 108837.
Elsevier DOI 2206
Multisensory integration, Top-down attention, Multimodal transformer, Fine-grained bird recognition, Emotion recognition BibRef

Arroyo, G.M.[Gonzalo Muñoz], Mateos-Rodríguez, M.[María],
Do Seabirds Control Wind Drift during Their Migration across the Strait of Gibraltar? A Study Using Remote Tracking by Radar,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Campbell, L.P.[Lindsay P.], Guralnick, R.P.[Robert P.], Giordano, B.V.[Bryan V.], Sallam, M.F.[Mohamed F.], Bauer, A.M.[Amely M.], Tavares, Y.[Yasmin], Allen, J.M.[Julie M.], Efstathion, C.[Caroline], Bartlett, S.[Suzanne], Wishard, R.[Randy], Xue, R.D.[Rui-De], Allen, B.[Benjamin], Tressler, M.[Miranda], Qualls, W.[Whitney], Burkett-Cadena, N.D.[Nathan D.],
Spatiotemporal Modeling of Zoonotic Arbovirus Transmission in Northeastern Florida Using Sentinel Chicken Surveillance and Earth Observation Data,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Abuaiadah, D.[Diab], Switzer, A.[Alexander], Bosu, M.[Michael], Liu, Y.[Yun],
Automatic counting of chickens in confined area using the LCFCN algorithm,
ISCV22(1-7)
IEEE DOI 2208
Measurement, Location awareness, Deep learning, Manuals, Prediction algorithms, Pins, deep learning, LCFCN BibRef

Alsubai, S.[Shtwai], Hamdi, M.[Monia], Abdel-Khalek, S.[Sayed], Alqahtani, A.[Abdullah], Binbusayyis, A.[Adel], Mansour, R.F.[Romany F.],
Bald eagle search optimization with deep transfer learning enabled age-invariant face recognition model,
IVC(126), 2022, pp. 104545.
Elsevier DOI 2209
Age invariant face recognition, Facial image analysis, Age progression, Deep transfer learning, Hyperparameter tuning BibRef

Yi, K.P.[Kun-Peng], Zhang, J.J.[Jun-Jian], Batbayar, N.[Nyambayar], Higuchi, H.[Hiroyoshi], Natsagdorj, T.[Tseveenmyadag], Bysykatova, I.P.[Inga P.],
Using Tracking Data to Identify Gaps in Knowledge and Conservation of the Critically Endangered Siberian Crane (Leucogeranus leucogeranus),
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211
BibRef

Merchant, D.[Daniel], Lathrop, R.G.[Richard G.], Santos, C.D.[Carlos David], Paludo, D.[Danielle], Niles, L.[Larry], Smith, J.A.M.[Joseph A. M.], Feigin, S.[Stephanie], Dey, A.[Amanda],
Distribution Modeling and Gap Analysis of Shorebird Conservation in Northern Brazil,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Kobayashi, S.[Shoko], Fujita, M.S.[Motoko S.], Omura, Y.[Yoshiharu], Haryadi, D.S.[Dendy S.], Muhammad, A.[Ahmad], Irham, M.[Mohammad], Shiodera, S.[Satomi],
Evaluating Threatened Bird Occurrence in the Tropics by Using L-Band SAR Remote Sensing Data,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Lawrence, B.[Brett], de Lemmus, E.[Emerson], Cho, H.[Hyuk],
UAS-Based Real-Time Detection of Red-Cockaded Woodpecker Cavities in Heterogeneous Landscapes Using YOLO Object Detection Algorithms,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Wu, J.[Jiahui], Xu, W.[Wen], He, J.F.[Jian-Feng], Lan, M.[Musheng],
YOLO for Penguin Detection and Counting Based on Remote Sensing Images,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306
BibRef

Chalmers, C.[Carl], Fergus, P.[Paul], Wich, S.[Serge], Longmore, S.N.[Steven N.], Walsh, N.D.[Naomi Davies], Stephens, P.A.[Philip A.], Sutherland, C.[Chris], Matthews, N.[Naomi], Mudde, J.[Jens], Nuseibeh, A.[Amira],
Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep Learning,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306
BibRef

Chang, D.L.[Dong-Liang], Pang, K.Y.[Kai-Yue], Du, R.[Ruoyi], Tong, Y.J.[Yu-Jun], Song, Y.Z.[Yi-Zhe], Ma, Z.Y.[Zhan-Yu], Guo, J.[Jun],
Making a Bird AI Expert Work for You and Me,
PAMI(45), No. 10, October 2023, pp. 12068-12084.
IEEE DOI 2310
BibRef

Moreno, E.[Eduardo], Gonzalez, E.[Encarnación], Alvarez, R.[Reinaldo], Menendez, J.[Julio],
Analysis and Quantification of the Distribution of Marabou (Dichrostachys cinerea (L.) Wight and Arn.) in Valle de los Ingenios, Cuba: A Remote Sensing Approach,
RS(16), No. 5, 2024, pp. 752.
DOI Link 2403
BibRef

Merchant, M.A.[Michael Allan], Battaglia, M.J.[Michael J.], French, N.[Nancy], Smith, K.[Kevin], Singer, H.V.[Howard V.], Armstrong, L.[Llwellyn], Harriman, V.B.[Vanessa B.], Slattery, S.[Stuart],
Species Abundance Modelling of Arctic-Boreal Zone Ducks Informed by Satellite Remote Sensing,
RS(16), No. 7, 2024, pp. 1175.
DOI Link 2404
BibRef

Jiang, Q.[Qi], Wang, R.[Rui], Zhang, W.Y.[Wen-Yuan], Jiao, L.X.[Long-Xiang], Li, W.D.[Wei-Dong], Wu, C.F.[Chun-Feng], Hu, C.[Cheng],
Monitoring Dynamically Changing Migratory Flocks Using an Algebraic Graph Theory-Based Clustering Algorithm,
RS(16), No. 7, 2024, pp. 1215.
DOI Link 2404
BibRef

Liu, H.[Hehao], Li, D.[Dong], Zhang, M.[Ming], Wan, J.[Jun], Liu, S.[Shuang], Zhu, H.Y.[Han-Ying], Liu, Q.H.[Qing-Hua],
A Cross-Modal Semantic Alignment and Feature Fusion Method for Bionic Drone and Bird Recognition,
RS(16), No. 17, 2024, pp. 3121.
DOI Link 2409
BibRef

Rodríguez, A.C.[Andrés C.], D'Aronco, S.[Stefano], Daudt, R.C.[Rodrigo Caye], Wegner, J.D.[Jan D.], Schindler, K.[Konrad],
Recognition of Unseen Bird Species by Learning from Field Guides,
WACV24(1731-1740)
IEEE DOI Code:
WWW Link. 2404
Visualization, Image recognition, Image coding, Zero-shot learning, Text recognition, Flowering plants, Algorithms, Animals / Insects BibRef

Kondo, Y.[Yuki], Ukita, N.[Norimichi], Yamaguchi, T.[Takayuki], Hou, H.Y.[Hao-Yu], Shen, M.Y.[Mu-Yi], Hsu, C.C.[Chia-Chi], Huang, E.M.[En-Ming], Huang, Y.C.[Yu-Chen], Xia, Y.C.[Yu-Cheng], Wang, C.Y.[Chien-Yao], Lee, C.Y.[Chun-Yi], Huo, D.[Da], Kastner, M.A.[Marc A.], Liu, T.W.[Ting-Wei], Kawanishi, Y.[Yasutomo], Hirayama, T.[Takatsugu], Komamizu, T.[Takahiro], Ide, I.[Ichiro], Shinya, Y.[Yosuke], Liu, X.Y.[Xin-Yao], Liang, G.[Guang], Yasui, S.[Syusuke],
MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results,
MVA23(1-11)
DOI Link 2403
Machine vision, Process control, Object detection, Birds, Real-time systems, Time factors, Proposals BibRef

Huo, D.[Da], Kastner, M.A.[Marc A.], Liu, T.W.[Ting-Wei], Kawanishi, Y.[Yasutomo], Hirayama, T.[Takatsugu], Komamizu, T.[Takahiro], Ide, I.[Ichiro],
Small Object Detection for Birds with Swin Transformer,
MVA23(1-5)
DOI Link 2403
Training, Pedestrians, Machine vision, Object detection, Transformers, Birds, Magnetic heads BibRef

Yuan, X.X.[Xiao-Xi],
Fine-Grained Image Recognition Method Based on Input Perception Joint Probability Prediction,
CVIDL23(62-69)
IEEE DOI 2403
Image recognition, Uncertainty, Heuristic algorithms, Estimation, Prediction algorithms, Birds, Feature extraction, Probabilistic prediction BibRef

Sun, H.Y.[Hong-Yu], Wang, Y.[Yongcai], Cai, X.D.[Xu-Dong], Wang, P.[Peng], Huang, Z.[Zhe], Li, D.[Deying], Shao, Y.[Yu], Wang, S.[Shuo],
Airbirds: A Large-scale Challenging Dataset for Bird Strike Prevention in Real-world Airports,
ACCV22(V:409-424).
Springer DOI 2307
BibRef

Coluccia, A.[Angelo], Fascista, A.[Alessio], Schumann, A.[Arne], Sommer, L.[Lars], Dimou, A.[Anastasios], Zarpalas, D.[Dimitrios], Sharma, N.[Nabin], Nalamati, M.[Mrunalini], Eryuksel, O.[Ogulcan], Ozfuttu, K.A.[Kamil Anil], Akyon, F.C.[Fatih Cagatay], Sahin, K.[Kadir], Buyukborekci, E.[Efe], Cavusoglu, D.[Devrim], Altinuc, S.[Sinan], Xing, D.[Daitao], Unlu, H.U.[Halil Utku], Evangeliou, N.[Nikolaos], Tzes, A.[Anthony], Nayak, A.[Abhijeet], Bouazizi, M.[Mondher], Ahmad, T.[Tasweer], Gonçalves, A.[Artur], Rigault, B.[Bastien], Jain, R.[Raghvendra], Matsuo, Y.[Yutaka], Prendinger, H.[Helmut], Jajaga, E.[Edmond], Rushiti, V.[Veton], Ramadani, B.[Blerant], Pavleski, D.[Daniel],
Drone-vs-Bird Detection Challenge at ICIAP 2021,
WOSDETC22(410-421).
Springer DOI 2208
BibRef

Lotfian, M., Ingensand, J.,
Using Geo Geo-Tagged Flickr Images to Explore the Correlation Between Land Cover Classes and the Location of Bird Observations,
ISPRS21(B4-2021: 189-194).
DOI Link 2201
BibRef

Ju, S.[Shengtai], Erasmus, M.A.[Marisa A.], Zhu, F.Q.[Feng-Qing], Reibman, A.R.[Amy R.],
Turkey Behavior Identification Using Video Analytics and Object Tracking,
ICIP21(1219-1223)
IEEE DOI 2201
Legged locomotion, Head, Shape, Visual analytics, Production, Object tracking, Object recognition, Video Analytics, Animal Welfare BibRef

Shim, K.[Kyuwon], Barczak, A.[Andre], Reyes, N.[Napoleon], Ahmed, N.[Nasim],
Small mammals and bird detection using IoT devices,
IVCNZ21(1-6)
IEEE DOI 2201
component, formatting, style, styling, insert BibRef

Zhang, Y.L.[Yun-Long], Hotta, S.[Seiji],
Chicken Detection in Occlusion Scenes with Modified Single Shot MultiBox Detector,
ISVC21(I:561-572).
Springer DOI 2112
BibRef

Wang, Y.F.[Yu-Fu], Kolotouros, N.[Nikos], Daniilidis, K.[Kostas], Badger, M.[Marc],
Birds of a Feather: Capturing Avian Shape Models from Images,
CVPR21(14734-14744)
IEEE DOI 2111
Deformable models, Training, Solid modeling, Shape, Birds, Phylogeny BibRef

Brust, C.A.[Clemens-Alexander], Barz, B.[Björn], Denzler, J.[Joachim],
Making Every Label Count: Handling Semantic Imprecision by Integrating Domain Knowledge,
ICPR21(6866-6873)
IEEE DOI 2105
Training, Annotations, Snow, Semantics, Training data, Benchmark testing, Birds BibRef

Belko, A.[Alina], Dobratulin, K.[Konstantin], Kunznetsov, A.[Andrey],
Two-stage Classification Model for Feather Images Identification,
IMTA20(172-181).
Springer DOI 2103
BibRef

Kennelly, S., Green, R.,
Classifying Bird Feeder Photos,
IVCNZ20(1-6)
IEEE DOI 2012
Databases, Training data, Birds, Agriculture, Data models, Convolutional neural networks, Testing BibRef

Chakraborti, T., McCane, B., Mills, S., Pal, U.,
CoCoNet: A Collaborative Convolutional Network applied to fine-grained bird species classification,
IVCNZ20(1-6)
IEEE DOI 2012
Training, Visualization, Image recognition, Collaboration, Birds, Task analysis, deep transfer learniing BibRef

Chakraborti, T., McCane, B., Mills, S., Pal, U.,
PProCRC: Probabilistic Collaboration of Image Patches for Fine-grained Classification,
IVCNZ20(1-5)
IEEE DOI 2012
Visualization, Image recognition, Collaboration, Probabilistic logic, Cost function, Birds, Task analysis, species recognition BibRef

Nawaz, S., Calefati, A., Caraffini, M., Landro, N., Gallo, I.,
Are These Birds Similar: Learning Branched Networks for Fine-grained Representations,
IVCNZ19(1-5)
IEEE DOI 2004
graph theory, image classification, image representation, learning (artificial intelligence), object recognition, Fine-grained image classification BibRef

Ali, A.A., Idris, N.H., Ishak, M.H.I.,
The Influence of Land-use Land-cover Changes On Urban Bird Communities,
GGT19(93-100).
DOI Link 1912
BibRef

Jørgensen, A.[Anders], Dueholm, J.V.[Jacob V.], Fagertun, J.[Jens], Moeslund, T.B.[Thomas B.],
Weight Estimation of Broilers in Images Using 3D Prior Knowledge,
SCIA19(221-232).
Springer DOI 1906
BibRef

Serrano, S.A.[Sergio A.], Benítez-Jimenez, R.[Ricardo], Nuñez-Rosas, L.[Laura], del Coro Arizmendi, M.[Ma], Greeney, H.[Harold], Reyes-Meza, V.[Veronica], Morales, E.[Eduardo], Escalante, H.J.[Hugo Jair],
Automated Detection of Hummingbirds in Images: A Deep Learning Approach,
MCPR18(155-166).
Springer DOI 1807
BibRef

Coluccia, A., Ghenescu, M., Piatrik, T., de Cubber, G., Schumann, A., Sommer, L., Klatte, J., Schuchert, T., Beyerer, J., Farhadi, M., Amandi, R., Aker, C., Kalkan, S., Saqib, M., Sharma, N., Daud, S., Makkah, K., Blumenstein, M.,
Drone-vs-Bird detection challenge at IEEE AVSS2017,
AVSS17(1-6)
IEEE DOI 1806
military aircraft, terrorism, European Commission, Horizon 2020 program, IEEE AVSS2017, SafeShore project, Video sequences BibRef

Bender, M., Yang, X., Chen, H., Kurdila, A., Müller, R.,
Gaussian process dynamic modeling of bat flapping flight,
ICIP17(4542-4546)
IEEE DOI 1803
Cameras, Data models, Dimensionality reduction, Kinematics, Manifolds, Mathematical model, Trajectory, Motion Capture BibRef

Pang, C., Li, H., Cherian, A., Yao, H.,
Part-based fine-grained bird image retrieval respecting species correlation,
ICIP17(2896-2900)
IEEE DOI 1803
Binary codes, Birds, Correlation, Image coding, Image recognition, Image retrieval, Task analysis, part detection BibRef

Srinivas, M., Lin, Y.Y., Liao, H.Y.M.,
Deep dictionary learning for fine-grained image classification,
ICIP17(835-839)
IEEE DOI 1803
Birds, Dictionaries, Feature extraction, Machine learning, Task analysis, Training, Training data, Sparse representation, on-line dictionary learning BibRef

Dash, A.[Amanda], Albu, A.B.[Alexandra Branzan],
Counting Large Flocks of Birds Using Videos Acquired with Hand-Held Devices,
ACIVS17(468-478).
Springer DOI 1712
BibRef

Elhoseiny, M., Zhu, Y., Zhang, H., Elgammal, A.,
Link the Head to the 'Beak': Zero Shot Learning from Noisy Text Description at Part Precision,
CVPR17(6288-6297)
IEEE DOI 1711
Birds, Head, Image recognition, Noise measurement, Training, Visualization BibRef

Wang, X., Zhao, Y.[Yue], Ji, Q.,
Taxonomy augmented object recognition,
ICPR16(1370-1375)
IEEE DOI 1705
Birds, Measurement, Object recognition, Semantics, Support vector machines, Taxonomy BibRef

T'Jampens, R., Hernandez, F., Vandecasteele, F., Verstockt, S.,
Automatic detection, tracking and counting of birds in marine video content,
IPTA16(1-6)
IEEE DOI 1703
feature extraction BibRef

Wang, Q.S.[Qiao-Song], Rasmussen, C.[Christopher], Song, C.B.[Chun-Bo],
Fast, Deep Detection and Tracking of Birds and Nests,
ISVC16(I: 146-155).
Springer DOI 1701
BibRef

Huang, J.B., Caruana, R., Farnsworth, A., Kelling, S., Ahuja, N.,
Detecting Migrating Birds at Night,
CVPR16(2091-2099)
IEEE DOI 1612
BibRef

Mader, S., Grenzdörffer, G.J.,
Automatic Sea Bird Detection From High Resolution Aerial Imagery,
ISPRS16(B7: 299-303).
DOI Link 1610
BibRef

Huang, Y., Zheng, H., Yang, H.,
Improving an object tracker for infrared flying bird tracking,
ICIP16(1699-1703)
IEEE DOI 1610
Decision support systems BibRef

Takeki, A., Trinh, T.T., Yoshihashi, R., Kawakami, R., Iida, M., Naemura, T.,
Detection of small birds in large images by combining a deep detector with semantic segmentation,
ICIP16(3977-3981)
IEEE DOI 1610
Birds BibRef

Kemper, G., Weidauer, A., Coppack, T.,
Monitoring Seabirds And Marine Mammals By Georeferenced Aerial Photography,
ISPRS16(B8: 689-694).
DOI Link 1610
BibRef

Xie, L.X.[Ling-Xi], Wang, J.D.[Jing-Dong], Lin, W.Y.[Wei-Yao], Zhang, B.[Bo], Tian, Q.[Qi],
RIDE: Reversal Invariant Descriptor Enhancement,
ICCV15(100-108)
IEEE DOI 1602
Birds. Description to eliminate need for including reversals in descriptions. BibRef

Wilber, M.J., Kwak, I.S., Kriegman, D., Belongie, S.J.,
Learning Concept Embeddings with Combined Human-Machine Expertise,
ICCV15(981-989)
IEEE DOI 1602
Birds BibRef

Beery, S.[Sara], van Horn, G.[Grant], Perona, P.[Pietro],
Recognition in Terra Incognita,
ECCV18(XVI: 472-489).
Springer DOI 1810
Dataset, Animals.
WWW Link. BibRef

van Horn, G.[Grant], Branson, S.[Steve], Farrell, R.[Ryan], Haber, S.[Scott], Barry, J.[Jessie], Ipeirotis, P.[Panos], Perona, P.[Pietro], Belongie, S.J.[Serge J.],
Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection,
CVPR15(595-604)
IEEE DOI 1510
BibRef

Branson, S.[Steve], van Horn, G.[Grant], Perona, P.[Pietro], Belongie, S.J.[Serge J.],
Improved Bird Species Recognition Using Pose Normalized Deep Convolutional Nets,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Jørgensen, A.[Anders], Jensen, E.M.[Eigil Mølvig], Moeslund, T.B.[Thomas B.],
Detecting Gallbladders in Chicken Livers using Spectral Imaging Anders,
MVAB15(xx-yy).
DOI Link 1601
BibRef

Atanbori, J.[John], Duan, W.T.[Wen-Ting], Murray, J.[John], Appiah, K.[Kofi], Dickinson, P.[Patrick],
A Computer Vision Approach to Classification of Birds in Flight from Video Sequences,
MVAB15(xx-yy).
DOI Link 1601
BibRef

Ge, Z.[Zong_Yuan], McCool, C.[Chris], Sanderson, C.[Conrad], Bewley, A.[Alex], Chen, Z.[Zetao], Corke, P.[Peter],
Fine-grained bird species recognition via hierarchical subset learning,
ICIP15(561-565)
IEEE DOI 1512
fine-grained classification; subset clustering BibRef

Yoshihashi, R.[Ryota], Kawakami, R.[Rei], Iida, M.[Makoto], Naemura, T.[Takeshi],
Construction of a bird image dataset for ecological investigations,
ICIP15(4248-4252)
IEEE DOI 1512
Image recognition BibRef

Tsukioka, H.[Hiroshi], Kudo, M.[Mineichi],
Selection of Features in Accord with Population Drift,
ICPR14(1591-1596)
IEEE DOI 1412
Birds BibRef

Borkar, T.S.[Tejas S.], Karam, L.J.[Lina J.],
Automated Bird Plumage Coloration Quantification in Digital Images,
ISVC14(II: 220-229).
Springer DOI 1501
BibRef

Goering, C.[Christoph], Rodner, E.[Erik], Freytag, A.[Alexander], Denzler, J.[Joachim],
Nonparametric Part Transfer for Fine-Grained Recognition,
CVPR14(2489-2496)
IEEE DOI 1409
bird classification BibRef

Berg, T.[Thomas], Liu, J.X.[Jiong-Xin], Lee, S.W.[Seung Woo], Alexander, M.L.[Michelle L.], Jacobs, D.W.[David W.], Belhumeur, P.N.[Peter N.],
Birdsnap: Large-Scale Fine-Grained Visual Categorization of Birds,
CVPR14(2019-2026)
IEEE DOI 1409
Fine-grained visual categorization BibRef

Angelova, A.[Anelia], Long, P.M.[Philip M.],
Benchmarking large-scale Fine-Grained Categorization,
WACV14(532-539)
IEEE DOI 1406
Birds BibRef

Angelova, A.[Anelia], Zhu, S.H.[Sheng-Huo],
Efficient Object Detection and Segmentation for Fine-Grained Recognition,
CVPR13(811-818)
IEEE DOI 1309
Laplacian propagation; fine-grained categorization; image segmentation. Low level regions into object. Use object for recognition. BibRef

Angelova, A.[Anelia], Niculescu-Mizil, A.[Alexandru],
Feature combination with Multi-Kernel Learning for fine-grained visual classification,
WACV14(241-246)
IEEE DOI 1406
Accuracy; Birds; Dictionaries; Dogs; Feature extraction; Kernel; Manuals BibRef

Berg, T.[Thomas], Belhumeur, P.N.[Peter N.],
How Do You Tell a Blackbird from a Crow?,
ICCV13(9-16)
IEEE DOI 1403
field guide; fine-grained recognition; visual similarity BibRef

Grenzdörffer, G.J.,
UAS-based automatic bird count of a common gull colony,
UAV-g13(169-174).
DOI Link 1311
BibRef

Liu, J.X.[Jiong-Xin], Belhumeur, P.N.[Peter N.],
Bird Part Localization Using Exemplar-Based Models with Enforced Pose and Subcategory Consistency,
ICCV13(2520-2527)
IEEE DOI 1403
Fine-grained classification; Part localization
See also Dog Breed Classification Using Part Localization. BibRef

Yao, B.P.[Bang-Peng], Bradski, G.R.[Gary R.], Fei-Fei, L.[Li],
A codebook-free and annotation-free approach for fine-grained image categorization,
CVPR12(3466-3473).
IEEE DOI 1208
Class is given, detailed classification. (e.g. birds) BibRef

Farrell, R.[Ryan], Oza, O.[Om], Zhang, N.[Ning], Morariu, V.I.[Vlad I.], Darrell, T.J.[Trevor J.], Davis, L.S.[Larry S.],
Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance,
ICCV11(161-168).
IEEE DOI 1201
Differences between part-level characterizations, not just absence of parts. Bird identification. BibRef

Qing, C.M.[Chun-Mei], Dickinson, P.[Patrick], Lawson, S.[Shaun], Freeman, R.[Robin],
Automatic nesting seabird detection based on boosted HOG-LBP descriptors,
ICIP11(3577-3580).
IEEE DOI 1201
BibRef

Zhu, W.X.[Wei-Xing], Lu, C.F.[Chen-Fang], Li, X.C.[Xin-Cheng], Kong, L.W.[Ling-Wu],
Dead Birds Detection in Modern Chicken Farm Based on SVM,
CISP09(1-5).
IEEE DOI 0910
BibRef

Das, M.[Madirakshi], Manmatha, R.,
Automatic Segmentation and Indexing in a Database of Bird Images,
ICCV01(II: 351-358).
IEEE DOI 0106
Segmentation. BibRef

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Bird Sounds, Bird Song, Birds Audio, Identification .