Song, G.,
Tang, S.Q.,
Method for Spectral Pattern-Recognition of Color Camouflage,
OptEng(36), No. 6, June 1997, pp. 1779-1781.
9706
BibRef
Camapum Wanderley, J.F.[Juliana F.],
Fisher, M.H.[Mark H.],
Spatial-Feature Parametric Clustering Applied to Motion-Based
Segmentation in Camouflage,
CVIU(85), No. 2, February 2002, pp. 144-157.
DOI Link
0210
BibRef
Earlier:
Segmentation using spatial-feature clustering from image sequences,
ICIP98(III: 799-803).
IEEE DOI
9810
See also Multiscale color invariants based on the human visual system.
BibRef
Hou, D.D.[Dong-Dong],
Zhang, W.M.[Wei-Ming],
Yu, N.H.[Neng-Hai],
Image camouflage by reversible image transformation,
JVCIR(40, Part A), No. 1, 2016, pp. 225-236.
Elsevier DOI
1609
Image camouflage
BibRef
Zhang, X.,
Zhu, C.,
Wang, S.,
Liu, Y.,
Ye, M.,
A Bayesian Approach to Camouflaged Moving Object Detection,
CirSysVideo(27), No. 9, September 2017, pp. 2001-2013.
IEEE DOI
1709
Adaptation models, Bayes methods, Computational modeling,
Feature extraction, Hidden Markov models,
Background subtraction, camouflage problem,
BibRef
Mondal, A.[Ajoy],
Ghosh, S.[Susmita],
Ghosh, A.[Ashish],
Partially Camouflaged Object Tracking using Modified Probabilistic
Neural Network and Fuzzy Energy based Active Contour,
IJCV(122), No. 1, March 2017, pp. 116-148.
Springer DOI
1702
BibRef
Li, S.,
Florencio, D.,
Li, W.,
Zhao, Y.,
Cook, C.,
A Fusion Framework for Camouflaged Moving Foreground Detection in the
Wavelet Domain,
IP(27), No. 8, August 2018, pp. 3918-3930.
IEEE DOI
1806
BibRef
Earlier: A1, A2, A4, A5, A3:
Foreground detection in camouflaged scenes,
ICIP17(4247-4251)
IEEE DOI
1803
image fusion, image segmentation, image sequences,
object detection, wavelet transforms, background models,
wavelet transform.
Correlation, Frequency-domain analysis, Image color analysis,
Wavelet domain, Foreground detection,
BibRef
Hou, D.D.[Dong-Dong],
Qin, C.[Chuan],
Yu, N.H.[Neng-Hai],
Zhang, W.M.[Wei-Ming],
Reversible visual transformation via exploring the correlations
within color images,
JVCIR(53), 2018, pp. 134-145.
Elsevier DOI
1805
Reversible visual transformation, Image camouflage,
Image encryption, Reversible data hiding
BibRef
Zheng, Y.,
Zhang, X.,
Wang, F.,
Cao, T.,
Sun, M.,
Wang, X.,
Detection of People With Camouflage Pattern Via Dense Deconvolution
Network,
SPLetters(26), No. 1, January 2019, pp. 29-33.
IEEE DOI
1901
deconvolution, feature extraction, feedforward neural nets,
image segmentation, natural scenes, object detection,
spatial smoothness
BibRef
Le, T.N.[Trung-Nghia],
Nguyen, T.V.[Tam V.],
Nie, Z.L.[Zhong-Liang],
Tran, M.T.[Minh-Triet],
Sugimoto, A.[Akihiro],
Anabranch network for camouflaged object segmentation,
CVIU(184), 2019, pp. 45-56.
Elsevier DOI
1906
Camouflaged object segmentation, Anabranch network
BibRef
Escudero-Viņolo, M.[Marcos],
Bescos, J.[Jesus],
Squeezing the DCT to Fight Camouflage,
JMIV(62), No. 2, February 2020, pp. 206-222.
Springer DOI
2002
BibRef
Mondal, A.[Ajoy],
Camouflaged Object Detection and Tracking: A Survey,
IJIG(20), No. 4, October 2020, pp. 2050028.
DOI Link
2011
BibRef
Xu, X.Q.[Xiu-Qi],
Zhu, M.Y.[Ming-Yu],
Yu, J.H.[Jin-Hao],
Chen, S.[Shuhan],
Hu, X.L.[Xue-Long],
Yang, Y.Q.[Yue-Quan],
Boundary guidance network for camouflage object detection,
IVC(114), 2021, pp. 104283.
Elsevier DOI
2109
Camouflaged object detection, Boundary guidance,
Hierarchical-Split Convolution, Residual refinement
BibRef
Shen, Y.[Ying],
Li, J.[Jie],
Lin, W.[Wenfu],
Chen, L.Q.[Li-Qiong],
Huang, F.[Feng],
Wang, S.[Shu],
Camouflaged Target Detection Based on Snapshot Multispectral Imaging,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link
2110
BibRef
Ji, G.P.[Ge-Peng],
Zhu, L.[Lei],
Zhuge, M.[Mingchen],
Fu, K.[Keren],
Fast Camouflaged Object Detection via Edge-based Reversible
Re-calibration Network,
PR(123), 2022, pp. 108414.
Elsevier DOI
2112
Camouflaged Object Detection, Reversible Re-calibration Unit,
Selective Edge Aggregation, NGES Priors
BibRef
Le, T.N.[Trung-Nghia],
Cao, Y.[Yubo],
Nguyen, T.C.[Tan-Cong],
Le, M.Q.[Minh-Quan],
Nguyen, K.D.[Khanh-Duy],
Do, T.T.[Thanh-Toan],
Tran, M.T.[Minh-Triet],
Nguyen, T.V.[Tam V.],
Camouflaged Instance Segmentation In-the-Wild:
Dataset, Method, and Benchmark Suite,
IP(31), 2022, pp. 287-300.
IEEE DOI
2112
Image segmentation, Task analysis, Benchmark testing,
Object segmentation, Image color analysis, Urban areas, Semantics,
multimodal learning
BibRef
Mei, H.Y.[Hai-Yang],
Ji, G.P.[Ge-Peng],
Wei, Z.[Ziqi],
Yang, X.[Xin],
Wei, X.P.[Xiao-Peng],
Fan, D.P.[Deng-Ping],
Camouflaged Object Segmentation with Distraction Mining,
CVPR21(8768-8777)
IEEE DOI
2111
Measurement, Frequency modulation, MIMICs, Refining,
Object segmentation, Real-time systems, Pattern recognition
BibRef
Li, A.X.[Ai-Xuan],
Zhang, J.[Jing],
Lv, Y.Q.[Yun-Qiu],
Liu, B.W.[Bo-Wen],
Zhang, T.[Tong],
Dai, Y.C.[Yu-Chao],
Uncertainty-aware Joint Salient Object and Camouflaged Object
Detection,
CVPR21(10066-10076)
IEEE DOI
2111
Visualization, Uncertainty, Computational modeling,
Object detection, Benchmark testing, Predictive models,
Adversarial machine learning
BibRef
Hu, S.N.[Sheng-Nan],
Zhang, Y.[Yang],
Laha, S.[Sumit],
Sharma, A.[Ankit],
Foroosh, H.[Hassan],
CCA: Exploring the Possibility of Contextual Camouflage Attack on
Object Detection,
ICPR21(7647-7654)
IEEE DOI
2105
Training, Adaptation models, Machine learning algorithms,
Neural networks, Lighting, Detectors, Object detection
BibRef
Zhai, Q.[Qiang],
Li, X.[Xin],
Yang, F.[Fan],
Chen, C.Z.[Chengli-Zhao],
Cheng, H.[Hong],
Fan, D.P.[Deng-Ping],
Mutual Graph Learning for Camouflaged Object Detection,
CVPR21(12992-13002)
IEEE DOI
2111
Codes, Image edge detection, Object detection,
Feature extraction, Cognition, Data mining
BibRef
Lv, Y.Q.[Yun-Qiu],
Zhang, J.[Jing],
Dai, Y.[Yuchao],
Li, A.[Aixuan],
Liu, B.[Bowen],
Barnes, N.[Nick],
Fan, D.P.[Deng-Ping],
Simultaneously Localize, Segment and Rank the Camouflaged Objects,
CVPR21(11586-11596)
IEEE DOI
2111
Location awareness, Animals,
Computational modeling, Object detection, Color, Task analysis
BibRef
Huang, L.,
Gao, C.,
Zhou, Y.,
Xie, C.,
Yuille, A.L.,
Zou, C.,
Liu, N.,
Universal Physical Camouflage Attacks on Object Detectors,
CVPR20(717-726)
IEEE DOI
2008
Proposals, Detectors, Semantics, Perturbation methods, Strain,
Optimization
BibRef
Duan, R.,
Ma, X.,
Wang, Y.,
Bailey, J.,
Qin, A.K.,
Yang, Y.,
Adversarial Camouflage: Hiding Physical-World Attacks With Natural
Styles,
CVPR20(997-1005)
IEEE DOI
2008
Perturbation methods, Cameras, Robustness, Feature extraction,
Distortion, Visualization, Measurement
BibRef
Fan, D.,
Ji, G.,
Sun, G.,
Cheng, M.,
Shen, J.,
Shao, L.,
Camouflaged Object Detection,
CVPR20(2774-2784)
IEEE DOI
2008
Task analysis, Object detection, Image segmentation, Measurement, Cats
BibRef
Lamdouar, H.[Hala],
Yang, C.[Charig],
Xie, W.[Weidi],
Zisserman, A.[Andrew],
Betrayed by Motion: Camouflaged Object Discovery via Motion
Segmentation,
ACCV20(II:488-503).
Springer DOI
WWW Link.
2103
See also MoCA: Moving Camouflaged Animals dataset.
BibRef
Miao, C.[Chu],
Shaohui, T.[Tian],
An extraction method for digital camouflage texture based on human
visual perception and isoperimetric theory,
ICIVC17(158-162)
IEEE DOI
1708
Feature extraction, Image color analysis, Image edge detection,
Image segmentation, Sensitivity, Visual perception, Visualization,
digital camouflage, human visual perception,
isoperimetric theory, multilevel, threshold
BibRef
Owens, A.[Andrew],
Barnes, C.[Connelly],
Flint, A.[Alex],
Singh, H.[Hanumant],
Freeman, W.T.[William T.],
Camouflaging an Object from Many Viewpoints,
CVPR14(2782-2789)
IEEE DOI
1409
produce a surface texture that will make the object
difficult for a human to detect.
BibRef
Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Object Localization .