See also Vehicle Counting.

See also Counting People, Transportation System Monitoring, Queues.

See also Counting People, Crowds, Crowd Counting.

*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**

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.[Mengyi]*,
*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

IEEE DOI

Training, Semantics, Estimation, Object detection, Detectors, Predictive models, Efficient training and inference methods, grouping and shape 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

*Deng, Z.L.[Zhao-Li]*,
*Yang, C.[Chenhui]*,

**Multiple-step Sampling for Dense Object Detection and Counting**,

*ICPR21*(1036-1042)

IEEE DOI
**2105**

Training, Detectors, Object detection, Benchmark testing,
Sampling methods, Feature extraction, Pattern recognition,
object counting
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

*Xiong, H.*,
*Lu, H.*,
*Liu, C.*,
*Liu, L.*,
*Cao, Z.*,
*Shen, C.*,

**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

*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:Aug 14, 2022 at 21:20:19