Wolf, C.[Christian],
Jolion, J.M.[Jean-Michel],
Object count/area graphs for the evaluation of object detection and
segmentation algorithms,
IJDAR(8), No. 4, September 2006, pp. 280-296.
Springer DOI
0609
BibRef
Wang, Y.,
Zou, Y.,
Wang, W.,
Manifold-Based Visual Object Counting,
IP(27), No. 7, July 2018, pp. 3248-3263.
IEEE DOI
1805
image classification, image reconstruction,
image representation, image resolution,
object density map estimation
BibRef
Stahl, T.,
Pintea, S.L.,
van Gemert, J.C.,
Divide and Count: Generic Object Counting by Image Divisions,
IP(28), No. 2, February 2019, pp. 1035-1044.
IEEE DOI
1811
Proposals, Computer architecture, Task analysis, Automobiles,
Animals, Object detection, Generic-class object counting,
counting with region proposals
BibRef
Zhang, S.H.[Shi-Hui],
Li, H.[He],
Kong, W.H.[Wei-Hang],
Object counting method based on dual attention network,
IET-IPR(14), No. 8, 19 June 2020, pp. 1621-1627.
DOI Link
2005
BibRef
Li, H.[He],
Zhang, S.H.[Shi-Hui],
Kong, W.H.[Wei-Hang],
Bilateral counting network for single-image object counting,
VC(36), No. 8, August 2020, pp. 1693-1704.
WWW Link.
2007
BibRef
Liu, L.,
Lu, H.,
Xiong, H.,
Xian, K.,
Cao, Z.,
Shen, C.,
Counting Objects by Blockwise Classification,
CirSysVideo(30), No. 10, October 2020, pp. 3513-3527.
IEEE DOI
2010
Kernel, Nonhomogeneous media, Task analysis, Feature extraction,
Quantization (signal), Convolutional neural networks,
count-level classification
BibRef
Xu, C.[Can],
Yuen, P.[Peter],
Lang, W.X.[Wen-Xi],
Xin, R.[Rui],
Mao, K.[Kaichen],
Jiang, H.Y.[Hai-Yan],
Generative detect for occlusion object based on occlusion generation
and feature completing,
JVCIR(78), 2021, pp. 103189.
Elsevier DOI
2107
BibRef
And: A1, A3, A4, A5, A6, Only:
Corrigendum:
JVCIR(93), 2023, pp. 103809.
Elsevier DOI
2305
Apply it to the in-filed Rice Panicles Counting.
Occlusion, Object detection, Feature completing, Generative adversarial networks
BibRef
Xu, W.[Wei],
Liang, D.K.[Ding-Kang],
Zheng, Y.X.[Yi-Xiao],
Xie, J.H.[Jia-Hao],
Ma, Z.Y.[Zhan-Yu],
Dilated-Scale-Aware Category-Attention ConvNet for Multi-Class Object
Counting,
SPLetters(28), 2021, pp. 1570-1574.
IEEE DOI
2108
Annotations, Feature extraction, Task analysis, Convolution,
Automobiles, Training, Visualization, Multi-class object counting,
category-attention module
BibRef
Cholakkal, H.[Hisham],
Sun, G.[Guolei],
Khan, S.[Salman],
Khan, F.S.[Fahad Shahbaz],
Shao, L.[Ling],
Van Gool, L.J.[Luc J.],
Towards Partial Supervision for Generic Object Counting in Natural
Scenes,
PAMI(44), No. 3, March 2022, pp. 1604-1622.
IEEE DOI
2202
Visualization, Genomics, Bioinformatics, Image segmentation,
Modulation, Sun, Graphical models, Generic object counting,
weakly supervised instance segmentation
BibRef
Wan, J.[Jia],
Wang, Q.Z.[Qing-Zhong],
Chan, A.B.[Antoni B.],
Kernel-Based Density Map Generation for Dense Object Counting,
PAMI(44), No. 3, March 2022, pp. 1357-1370.
IEEE DOI
2202
Kernel, Estimation, Feature extraction, Generators, Task analysis,
Prediction algorithms, Bandwidth, Crowd counting, vehicle counting,
deep learning
BibRef
Tang, M.Y.[Meng-Yi],
Yashtini, M.[Maryam],
Kang, S.H.[Sung Ha],
Counting Objects by Diffused Index: Geometry-free and training-free
approach,
JVCIR(86), 2022, pp. 103527.
Elsevier DOI
2206
Object counting, Variational analysis,
Alternating minimization, Fast methods, Clustering, Convergence analysis
BibRef
Moon, J.[Jiwon],
Lim, S.[Sangkyu],
Lee, H.[Hakjun],
Yu, S.[Seungbum],
Lee, K.B.[Ki-Baek],
Smart Count System Based on Object Detection Using Deep Learning,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Gao, J.Y.[Jun-Yu],
Gong, M.[Maoguo],
Li, X.L.[Xue-Long],
Global Multi-Scale Information Fusion for Multi-Class Object Counting
in Remote Sensing Images,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Guo, X.Y.[Xiang-Yu],
Anisetti, M.[Marco],
Gao, M.L.[Ming-Liang],
Jeon, G.G.[Gwang-Gil],
Object Counting in Remote Sensing via Triple Attention and
Scale-Aware Network,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link
2212
BibRef
McCarthy, T.[Tadhg],
Virtusio, J.J.[John Jethro],
Ople, J.J.M.[Jose Jaena Mari],
Tan, D.S.[Daniel Stanley],
Amalin, D.[Divina],
Hua, K.L.[Kai-Lung],
MACnet: Mask augmented counting network for class-agnostic counting,
PRL(169), 2023, pp. 75-80.
Elsevier DOI
2305
Class-agnostic counting, Extreme points, Segmentation masks
BibRef
Soliven, A.F.O.[Adrienne Francesca O.],
Virtusio, J.J.[John Jethro],
Ople, J.J.M.[Jose Jaena Mari],
Tan, D.S.[Daniel Stanley],
Amalin, D.[Divina],
Hua, K.L.[Kai-Lung],
ConCoNet: Class-agnostic counting with positive and negative
exemplars,
PRL(171), 2023, pp. 148-154.
Elsevier DOI
2306
Object counting, Class-agnostic, Few-shot learning
BibRef
Xiong, H.P.[Hai-Peng],
Lu, H.[Hao],
Liu, C.X.[Cheng-Xin],
Liu, L.[Liang],
Shen, C.H.[Chun-Hua],
Cao, Z.G.[Zhi-Guo],
From Open Set to Closed Set:
Supervised Spatial Divide-and-Conquer for Object Counting,
IJCV(131), No. 7, July 2023, pp. 1722-1740.
Springer DOI
2307
BibRef
Earlier: A1, A2, A3, A4, A6, A5:
From Open Set to Closed Set:
Counting Objects by Spatial Divide-and-Conquer,
ICCV19(8361-8370)
IEEE DOI
2004
Code, Counting.
WWW Link. divide and conquer methods, image processing,
learning (artificial intelligence), neural nets, Estimation
BibRef
Gillert, A.[Alexander],
Resente, G.[Giulia],
Anadon-Rosell, A.[Alba],
Wilmking, M.[Martin],
von Lukas, U.F.[Uwe Freiherr],
Iterative Next Boundary Detection for Instance Segmentation of Tree
Rings in Microscopy Images of Shrub Cross Sections,
CVPR23(14540-14548)
IEEE DOI
2309
BibRef
Chen, Y.H.[Yue-Hai],
Yang, J.[Jing],
Chen, B.D.[Ba-Dong],
Du, S.Y.[Shao-Yi],
Hua, G.[Gang],
Tolerating Annotation Displacement in Dense Object Counting via Point
Annotation Probability Map,
IP(32), 2023, pp. 6359-6372.
IEEE DOI
2311
BibRef
Guo, X.Y.[Xiang-Yu],
Gao, M.L.[Ming-Liang],
Zhai, W.Z.[Wen-Zhe],
Li, Q.[Qilei],
Jeon, G.G.[Gwang-Gil],
Scale Region Recognition Network for Object Counting in Intelligent
Transportation System,
ITS(24), No. 12, December 2023, pp. 15920-15929.
IEEE DOI
2312
BibRef
Jiang, S.Q.[Sheng-Qin],
Wang, Q.[Qing],
Cheng, F.[Fengna],
Qi, Y.[Yuankai],
Liu, Q.S.[Qing-Shan],
A Unified Object Counting Network with Object Occupation Prior,
CirSysVideo(34), No. 2, February 2024, pp. 1147-1158.
IEEE DOI Code:
WWW Link.
2402
Task analysis, Training, Knowledge engineering, Feature extraction,
Deformable models, Convolutional neural networks, Annotations,
convolution neural network
BibRef
Zhang, Z.R.[Zhen-Rong],
Hu, P.F.[Peng-Fei],
Ma, J.F.[Jie-Feng],
Du, J.[Jun],
Zhang, J.S.[Jian-Shu],
Yin, B.C.[Bao-Cai],
Yin, B.[Bing],
Liu, C.[Cong],
SEMv2: Table separation line detection based on instance segmentation,
PR(149), 2024, pp. 110279.
Elsevier DOI Code:
WWW Link.
2403
Table structure recognition, Table separation line detection,
Instance segmentation, Conditional convolution, Table structure dataset
BibRef
Han, T.[Tao],
Bai, L.[Lei],
Liu, L.B.[Ling-Bo],
Ouyang, W.L.[Wan-Li],
STEERER: Resolving Scale Variations for Counting and Localization via
Selective Inheritance Learning,
ICCV23(21791-21802)
IEEE DOI Code:
WWW Link.
2401
BibRef
Huang, Y.F.[Yi-Feng],
Ranjan, V.[Viresh],
Hoai, M.[Minh],
Interactive Class-Agnostic Object Counting,
ICCV23(22255-22265)
IEEE DOI Code:
WWW Link.
2401
BibRef
Araújo, F.[Felipe],
Gadelha, I.[Igor],
Tsukahara, R.[Rodrigo],
Pita, L.[Luiz],
Costa, F.[Filipe],
Vaz, I.[Igor],
Santos, A.[Andreza],
Folego, G.[Guilherme],
Hinting Pipeline and Multivariate Regression CNN for Maize Kernel
Counting on the Ear,
ICIP23(1110-1114)
IEEE DOI
2312
BibRef
Sun, G.[Guolei],
An, Z.[Zhaochong],
Liu, Y.[Yun],
Liu, C.[Ce],
Sakaridis, C.[Christos],
Fan, D.P.[Deng-Ping],
Van Gool, L.J.[Luc J.],
Indiscernible Object Counting in Underwater Scenes,
CVPR23(13791-13801)
IEEE DOI
2309
BibRef
Xu, J.Y.[Jing-Yi],
Le, H.[Hieu],
Nguyen, V.[Vu],
Ranjan, V.[Viresh],
Samaras, D.[Dimitris],
Zero-Shot Object Counting,
CVPR23(15548-15557)
IEEE DOI
2309
BibRef
Zgaren, A.[Ahmed],
Bouachir, W.[Wassim],
Bouguila, N.[Nizar],
Hammoud, R.I.[Riad I.],
MoundCount: A detection-based approach for automatic counting of
planting microsites on UAV images,
PBVS23(497-506)
IEEE DOI
2309
BibRef
Wu, C.S.[Chen-Shen],
van de Weijer, J.[Joost],
Density Map Distillation for Incremental Object Counting,
CLVision23(2506-2515)
IEEE DOI
2309
BibRef
Ranjan, V.[Viresh],
Nguyen, M.H.[Minh Hoai],
Exemplar Free Class Agnostic Counting,
ACCV22(IV:71-87).
Springer DOI
2307
BibRef
Jenkins, P.[Porter],
Armstrong, K.[Kyle],
Nelson, S.[Stephen],
Gotad, S.[Siddhesh],
Jenkins, J.S.[J. Stockton],
Wilkey, W.[Wade],
Watts, T.[Tanner],
CountNet3D: A 3D Computer Vision Approach to Infer Counts of Occluded
Objects,
WACV23(3007-3016)
IEEE DOI
2302
Point cloud compression, Location awareness, Detectors,
Object detection, Inventory management, visual reasoning
BibRef
You, Z.Y.[Zhi-Yuan],
Yang, K.[Kai],
Luo, W.H.[Wen-Han],
Lu, X.[Xin],
Cui, L.[Lei],
Le, X.[Xinyi],
Few-shot Object Counting with Similarity-Aware Feature Enhancement,
WACV23(6304-6313)
IEEE DOI
2302
Training, Image recognition, Target recognition, Focusing,
Benchmark testing, Finite element analysis
BibRef
Li, L.M.[Li-Ming],
Song, S.[Sanming],
Wang, L.[Li],
Ye, L.[Lei],
Jing, Y.[Yan],
Pang, G.[Guofu],
Feature Evaluation for Underwater Acoustic Object Counting and F0
Estimation,
ICRVC22(180-185)
IEEE DOI
2301
Shafts, Time-frequency analysis, Source separation,
Time series analysis, Estimation, Object detection, Lakes,
F0 estimation
BibRef
Xiong, H.P.[Hai-Peng],
Yao, A.[Angela],
Discrete-Constrained Regression for Local Counting Models,
ECCV22(XXIV:621-636).
Springer DOI
2211
BibRef
Nguyen, T.[Thanh],
Pham, C.[Chau],
Nguyen, K.[Khoi],
Hoai, M.[Minh],
Few-Shot Object Counting and Detection,
ECCV22(XX:348-365).
Springer DOI
2211
BibRef
Gong, S.J.[Shen-Jian],
Zhang, S.S.[Shan-Shan],
Yang, J.[Jian],
Dai, D.X.[Deng-Xin],
Schiele, B.[Bernt],
Class-Agnostic Object Counting Robust to Intraclass Diversity,
ECCV22(XXXIII:388-403).
Springer DOI
2211
BibRef
Ranjan, V.[Viresh],
Hoai, M.[Minh],
Vicinal Counting Networks,
L3D-IVU22(4220-4229)
IEEE DOI
2210
Training, Visualization, Buildings, Training data, Generators
BibRef
Han, T.[Tao],
Bai, L.[Lei],
Gao, J.Y.[Jun-Yu],
Wang, Q.[Qi],
Ouyang, W.L.[Wan-Li],
DR.VIC: Decomposition and Reasoning for Video Individual Counting,
CVPR22(3073-3082)
IEEE DOI
2210
Codes, Annotations, Estimation, Manuals, Cognition,
Pattern recognition, Video analysis and understanding,
Scene analysis and understanding
BibRef
Shi, M.[Min],
Lu, H.[Hao],
Feng, C.[Chen],
Liu, C.X.[Cheng-Xin],
Cao, Z.G.[Zhi-Guo],
Represent, Compare, and Learn:
A Similarity-Aware Framework for Class-Agnostic Counting,
CVPR22(9519-9528)
IEEE DOI
2210
Visualization, Computational modeling, Pipelines,
Feature extraction, Robustness, Power capacitors, retrieval
BibRef
Cheng, Z.Q.[Zhi-Qi],
Dai, Q.[Qi],
Li, H.[Hong],
Song, J.K.[Jing-Kuan],
Wu, X.[Xiao],
Hauptmann, A.G.[Alexander G.],
Rethinking Spatial Invariance of Convolutional Networks for Object
Counting,
CVPR22(19606-19616)
IEEE DOI
2210
Convolution, Annotations, Benchmark testing, Feature extraction,
Pattern recognition, Kernel, Scene analysis and understanding,
Vision applications and systems
BibRef
Michel, A.[Andreas],
Gross, W.[Wolfgang],
Schenkel, F.[Fabian],
Middelmann, W.[Wolfgang],
Class-aware Object Counting,
RWSurvil22(469-478)
IEEE DOI
2202
Conferences, Estimation, Object detection, Detectors
BibRef
Huberman-Spiegelglas, I.[Inbar],
Fattal, R.[Raanan],
Single Image Object Counting and Localizing using Active-Learning,
WACV22(3717-3726)
IEEE DOI
2202
Training, Manifolds, Location awareness, Visualization, Surveillance,
Microscopy, Lighting,
Semi- and Un- supervised Learning
BibRef
Yang, S.D.[Shuo-Diao],
Su, H.T.[Hung-Ting],
Hsu, W.H.[Winston H.],
Chen, W.C.[Wen-Chin],
Class-agnostic Few-shot Object Counting,
WACV21(869-877)
IEEE DOI
2106
Training, Computational modeling,
Force, Data collection, Data models
BibRef
Chen, F.[Feng],
Pound, M.P.[Michael P.],
French, A.P.[Andrew P.],
Learning to Localise and Count with Incomplete Dot-Annotations,
ILDAV21(1612-1620)
IEEE DOI
2112
Training, Heating systems, Head, Annotations, Training data,
Semisupervised learning, Fatigue
BibRef
Godi, M.[Marco],
Joppi, C.[Christian],
Giachetti, A.[Andrea],
Cristani, M.[Marco],
SIMCO: SIMilarity-based object COunting,
ICPR21(47-52)
IEEE DOI
2105
Training, Head, Shape, Image color analysis, Annotations,
Benchmark testing, Pattern recognition
BibRef
Laradji, I.H.,
Pardinas, R.,
Rodriguez, P.,
Vazquez, D.,
LOOC: Localize Overlapping Objects with Count Supervision,
ICIP20(2316-2320)
IEEE DOI
2011
Proposals, Training, Games, Task analysis, Object recognition,
Generators, Videos, localization, counting, weakly supervised
BibRef
Yang, Y.,
Li, G.,
Wu, Z.,
Su, L.,
Huang, Q.,
Sebe, N.,
Reverse Perspective Network for Perspective-Aware Object Counting,
CVPR20(4373-4382)
IEEE DOI
2008
Distortion, Training, Feature extraction, Estimation, Convolution,
Kernel, Adaptation models
BibRef
Shi, Z.L.[Zeng-Lin],
Mettes, P.S.[Pascal S.],
Snoek, C.G.M.[Cees G. M.],
Counting With Focus for Free,
ICCV19(4199-4208)
IEEE DOI
2004
Code, Counting.
WWW Link. convolutional neural nets, image segmentation,
network theory (graphs), object detection, supervised learning,
Convolution
BibRef
Zhao, M.M.[Mu-Ming],
Zhang, J.[Jian],
Zhang, C.Y.[Chong-Yang],
Zhang, W.J.[Wen-Jun],
Towards Locally Consistent Object Counting with Constrained Multi-stage
Convolutional Neural Networks,
ACCV18(VI:247-261).
Springer DOI
1906
BibRef
Ren, M.Y.[Meng-Ye],
Zemel, R.S.[Richard S.],
End-to-End Instance Segmentation with Recurrent Attention,
CVPR17(293-301)
IEEE DOI
1711
Computational modeling, Convolution,
Image segmentation, Indexes, Training. Counting.
BibRef
Chattopadhyay, P.,
Vedantam, R.,
Selvaraju, R.R.,
Batra, D.,
Parikh, D.,
Counting Everyday Objects in Everyday Scenes,
CVPR17(4428-4437)
IEEE DOI
1711
Detectors, Feature extraction, Knowledge discovery,
Object detection, Surveillance, Visualization
BibRef
Fiaschi, L.[Luca],
Koethe, U.[Ullrich],
Nair, R.[Rahul],
Hamprecht, F.A.[Fred A.],
Learning to count with regression forest and structured labels,
ICPR12(2685-2688).
WWW Link.
1302
count instances
BibRef
Yu, L.[Li],
Hoover, A.[Adam],
Threshold Selection as a Function of Region Count Stability,
PercOrg04(59).
IEEE DOI
0502
BibRef
Ancin, H.,
Dufresne, T.E.,
Ridder, G.M.,
Turner, J.N.,
Roysam, B.,
An improved watershed algorithm for counting objects in noisy,
anisotropic 3-D biological images,
ICIP95(III: 172-175).
IEEE DOI
9510
BibRef
Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Panoptic Segmentation .