8.6.1.2 Counting Instances, Counting Objects

Chapter Contents (Back)
Instance Segmentation. Counting. Count Objects.
See also Vehicle Counting.
See also Counting People, Transportation System Monitoring, Queues.
See also Counting People, Crowds, Crowd Counting.
See also Dense Object Detection.

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
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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
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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

Chen, H.[Hao], Lu, Z.M.[Zhe-Ming], Liu, J.[Jie],
A Monkey Swing Counting Algorithm Based on Object Detection,
IEICE(E108-D), No. 4, April 2024, pp. 579-583.
WWW Link. 2404
BibRef

Gao, B.B.[Bin-Bin], Huang, Z.Y.[Zhong-Yi],
CSTrans: Correlation-guided Self-Activation Transformer for Counting Everything,
PR(153), 2024, pp. 110556.
Elsevier DOI Code:
WWW Link. 2405
Few-shot counting, Local dependency, Counting everything, Vision transformer BibRef

Tabernik, D.[Domen], Muhovic, J.[Jon], Skocaj, D.[Danijel],
Dense center-direction regression for object counting and localization with point supervision,
PR(153), 2024, pp. 110540.
Elsevier DOI Code:
WWW Link. 2405
Point-supervision, Object counting, Object localization, Center-point prediction, Center-direction regression, CeDiRNet BibRef

Wang, M.J.[Ming-Jie], Li, Y.[Yande], Zhou, J.[Jun], Taylor, G.W.[Graham W.], Gong, M.L.[Ming-Lun],
GCNet: Probing self-similarity learning for Generalized Counting Network,
PR(153), 2024, pp. 110513.
Elsevier DOI 2405
Class-agnostic counting, Exemplar-free scheme, Zero-shot paradigm, Self-similarity learning BibRef

Shi, Z.L.[Zeng-Lin], Mettes, P.[Pascal], Snoek, C.G.M.[Cees G. M.],
Focus for Free in Density-Based Counting,
IJCV(132), No. 7, July 2024, pp. Pages2600-2617.
Springer DOI 2406
BibRef


d'Alessandro, A.C.[Adriano C.], Mahdavi-Amiri, A.[Ali], Hamarneh, G.[Ghassan],
Learning-to-Count by Learning-to-Rank,
CRV23(105-112)
IEEE DOI 2406
Heating systems, Costs, Codes, Annotations, Estimation, Benchmark testing, Generative adversarial networks, Ranking BibRef

Shi, Z.L.[Zeng-Lin], Sun, Y.[Ying], Zhang, M.[Mengmi],
Training-free Object Counting with Prompts,
WACV24(322-330)
IEEE DOI Code:
WWW Link. 2404
Training, Image segmentation, Codes, Annotations, Training data, Data collection, Algorithms, Image recognition and understanding BibRef

Doubinsky, P.[Perla], Audebert, N.[Nicolas], Crucianu, M.[Michel], Borgne, H.L.[Hervé Le],
Semantic Generative Augmentations for Few-Shot Counting,
WACV24(5431-5440)
IEEE DOI 2404
Training, Measurement, Adaptation models, Semantic segmentation, Semantics, Object detection, Data models, Algorithms, Image recognition and understanding BibRef

Đukic, N.[Nikola], Lukežic, A.[Alan], Zavrtanik, V.[Vitjan], Kristan, M.[Matej],
A Low-Shot Object Counting Network With Iterative Prototype Adaptation,
ICCV23(18826-18835)
IEEE DOI Code:
WWW Link. 2401
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 .


Last update:Jul 13, 2024 at 15:27:21