7.1.7.2 Camouflaged Object Detection, Camouflage

Chapter Contents (Back)
Camouflage.
See also Adversarial Attacks.
See also Dense Object Detection.

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-Vińolo, 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

Zhuge, M.[Mingchen], Lu, X.[Xiankai], Guo, Y.[Yiyou], Cai, Z.H.[Zhi-Hua], Chen, S.[Shuhan],
CubeNet: X-shape connection for camouflaged object detection,
PR(127), 2022, pp. 108644.
Elsevier DOI 2205
Camouflaged object detection, Neural network, Edge guidance, Novel feature aggregation BibRef

Liu, Q.[Qiang], Xiang, X.[Xuyu], Qin, J.[Jiaohua], Tan, Y.[Yun], Zhang, Q.[Qin],
A Robust Coverless Steganography Scheme Using Camouflage Image,
CirSysVideo(32), No. 6, June 2022, pp. 4038-4051.
IEEE DOI 2206
Robustness, Convolutional neural networks, Receivers, Forestry, Computer science, Information technology, Elbow, image retrieval BibRef

Zhang, C.[Cong], Wang, K.[Kang], Bi, H.B.[Hong-Bo], Liu, Z.Q.[Zi-Qi], Yang, L.[Lina],
Camouflaged object detection via Neighbor Connection and Hierarchical Information Transfer,
CVIU(221), 2022, pp. 103450.
Elsevier DOI 2206
Deep learning, Camouflaged Object Detection, Salient Object Detection BibRef

Hupel, T.[Tobias], Stütz, P.[Peter],
Adopting Hyperspectral Anomaly Detection for Near Real-Time Camouflage Detection in Multispectral Imagery,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Bi, H.B.[Hong-Bo], Zhang, C.[Cong], Wang, K.[Kang], Tong, J.H.[Jing-Hui], Zheng, F.[Feng],
Rethinking Camouflaged Object Detection: Models and Datasets,
CirSysVideo(32), No. 9, September 2022, pp. 5708-5724.
IEEE DOI 2209
Object detection, Feature extraction, Visualization, Image color analysis, Task analysis, Optical imaging, deep learning BibRef

Chen, G.[Geng], Liu, S.J.[Si-Jie], Sun, Y.J.[Yu-Jia], Ji, G.P.[Ge-Peng], Wu, Y.F.[Ya-Feng], Zhou, T.[Tao],
Camouflaged Object Detection via Context-Aware Cross-Level Fusion,
CirSysVideo(32), No. 10, October 2022, pp. 6981-6993.
IEEE DOI 2210
Deep learning, Object detection, Computational modeling, Image segmentation, Task analysis, Object recognition, polyp segmentation BibRef

Li, P.[Peng], Yan, X.F.[Xue-Feng], Zhu, H.W.[Hong-Wei], Wei, M.Q.[Ming-Qiang], Zhang, X.P.[Xiao-Ping], Qin, J.[Jing],
FindNet: Can You Find Me? Boundary-and-Texture Enhancement Network for Camouflaged Object Detection,
IP(31), 2022, pp. 6396-6411.
IEEE DOI 2211
Codes, Convolution, Shape, Image color analysis, Fuses, Image edge detection, Semantics, FindNet, texture enhancement module BibRef


He, C.Y.[Chi-Yuan], Xu, L.F.[Lin-Feng], Qiu, Z.H.[Zi-Huan],
Eldnet: Establishment and Refinement of Edge Likelihood Distributions for Camouflaged Object Detection,
ICIP22(621-625)
IEEE DOI 2211
Fault tolerance, Image edge detection, Semantics, Fault tolerant systems, Estimation, Object detection, Interference, Deep learning BibRef

Pei, J.L.[Jia-Lun], Cheng, T.Y.[Tian-Yang], Fan, D.P.[Deng-Ping], Tang, H.[He], Chen, C.B.[Chuan-Bo], Van Gool, L.J.[Luc J.],
OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers,
ECCV22(XVIII:19-37).
Springer DOI 2211
BibRef

Zhong, Y.J.[Yi-Jie], Li, B.[Bo], Tang, L.[Lv], Kuang, S.[Senyun], Wu, S.[Shuang], Ding, S.H.[Shou-Hong],
Detecting Camouflaged Object in Frequency Domain,
CVPR22(4494-4503)
IEEE DOI 2210
Visualization, Shape, Fuses, Frequency-domain analysis, Object detection, Pattern recognition, Segmentation, Scene analysis and understanding BibRef

Jia, Q.[Qi], Yao, S.L.[Shui-Lian], Liu, Y.[Yu], Fan, X.[Xin], Liu, R.S.[Ri-Sheng], Luo, Z.X.[Zhong-Xuan],
Segment, Magnify and Reiterate: Detecting Camouflaged Objects the Hard Way,
CVPR22(4703-4712)
IEEE DOI 2210
Measurement, Image segmentation, Codes, Image edge detection, Object detection, Network architecture, Recognition: detection, grouping and shape analysis BibRef

Gao, R.J.[Rui-Jun], Guo, Q.[Qing], Juefei-Xu, F.[Felix], Yu, H.K.[Hong-Kai], Fu, H.Z.[Hua-Zhu], Feng, W.[Wei], Liu, Y.[Yang], Wang, S.[Song],
Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection,
CVPR22(2140-2149)
IEEE DOI 2210
Degradation, Additives, Perturbation methods, Pipelines, Object detection, Robustness, Safety, Low-level vision, Adversarial attack and defense BibRef

Pang, Y.[Youwei], Zhao, X.Q.[Xiao-Qi], Xiang, T.Z.[Tian-Zhu], Zhang, L.[Lihe], Lu, H.C.[Hu-Chuan],
Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection,
CVPR22(2150-2160)
IEEE DOI 2210
Uncertainty, Shape, Semantics, Merging, MIMICs, Object detection, Predictive models, Low-level vision, Segmentation, grouping and shape analysis BibRef

Cheng, X.L.[Xue-Lian], Xiong, H.[Huan], Fan, D.P.[Deng-Ping], Zhong, Y.[Yiran], Harandi, M.[Mehrtash], Drummond, T.[Tom], Ge, Z.Y.[Zong-Yuan],
Implicit Motion Handling for Video Camouflaged Object Detection,
CVPR22(13854-13863)
IEEE DOI 2210
Motion segmentation, Motion estimation, Video sequences, Wildlife, Object detection, Benchmark testing, Transformers, grouping and shape analysis BibRef

Yang, F.[Fan], Zhai, Q.[Qiang], Li, X.[Xin], Huang, R.[Rui], Luo, A.[Ao], Cheng, H.[Hong], Fan, D.P.[Deng-Ping],
Uncertainty-Guided Transformer Reasoning for Camouflaged Object Detection,
ICCV21(4126-4135)
IEEE DOI 2203
Uncertainty, Object detection, Transformer cores, Transformers, Probabilistic logic, Cognition, grouping and shape BibRef

Wang, J.[Jiakai], Liu, A.[Aishan], Yin, Z.[Zixin], Liu, S.[Shunchang], Tang, S.Y.[Shi-Yu], Liu, X.L.[Xiang-Long],
Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World,
CVPR21(8561-8570)
IEEE DOI 2111
Deep learning, Visualization, Shape, Perturbation methods, Semantics, Three-dimensional printing, Robustness 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

Liu, J.W.[Jia-Wei], Zhang, J.[Jing], Barnes, N.[Nick],
Modeling Aleatoric Uncertainty for Camouflaged Object Detection,
WACV22(2613-2622)
IEEE DOI 2202
Training, Uncertainty, Annotations, Estimation, Object detection, Predictive models, Probability distribution, Segmentation, Grouping and Shape 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 .


Last update:Nov 28, 2022 at 16:32:47