Harwood, D.A.,
Chang, S., and
Davis, L.S.,
Interpreting Aerial Photographs by Segmentation and Search,
DARPA87(507-520).
(
See also Sigma Image Understanding System, The. )
Find segments (homogeneous regions), then find instances which
satisfy definitions of object types, then search for support, then
improve instances, then iterate with new estimates of parameters.
See also Fua and Leclerc Guided Segmentation Papers.
BibRef
8700
Meng, J.,
Yuan, J.,
Yang, J.,
Wang, G.,
Tan, Y.P.,
Object Instance Search in Videos via Spatio-Temporal Trajectory
Discovery,
MultMed(18), No. 1, January 2016, pp. 116-127.
IEEE DOI
1601
Find specific object.
BibRef
Yu, J.G.[Jin-Gang],
Li, Y.S.[Yan-Sheng],
Gao, C.X.[Chang-Xin],
Gao, H.X.[Hong-Xia],
Xia, G.S.[Gui-Song],
Yu, Z.L.[Zhu Liang],
Li, Y.Q.[Yuan-Qing],
Exemplar-Based Recursive Instance Segmentation With Application to
Plant Image Analysis,
IP(29), No. 1, 2020, pp. 389-404.
IEEE DOI
1910
biology computing, computational complexity, computer vision,
image classification, image segmentation, inference mechanisms,
plant phenotyping
BibRef
Zhang, H.,
Tian, Y.,
Wang, K.,
Zhang, W.,
Wang, F.,
Mask SSD: An Effective Single-Stage Approach to Object Instance
Segmentation,
IP(29), 2020, pp. 2078-2093.
IEEE DOI
2001
Object detection, instance segmentation, feedback features, single-shot detector
BibRef
Goldman, E.[Eran],
Goldberger, J.[Jacob],
CRF with deep class embedding for large scale classification,
CVIU(191), 2020, pp. 102865.
Elsevier DOI
2002
CRF, Class embedding, Matrix factorization,
Surrogate likelihood, Batch normalization
BibRef
Goldman, E.[Eran],
Herzig, R.[Roei],
Eisenschtat, A.[Aviv],
Goldberger, J.[Jacob],
Hassner, T.[Tal],
Precise Detection in Densely Packed Scenes,
CVPR19(5222-5231).
IEEE DOI
2002
E.g. man-made scenes with numerous identical objects.
BibRef
Su, H.[Hao],
Wei, S.J.[Shun-Jun],
Liu, S.[Shan],
Liang, J.D.[Jia-Dian],
Wang, C.[Chen],
Shi, J.[Jun],
Zhang, X.L.[Xiao-Ling],
HQ-ISNet: High-Quality Instance Segmentation for Remote Sensing
Imagery,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link
2003
BibRef
Yao, J.[Jian],
Boben, M.[Marko],
Fidler, S.[Sanja],
Urtasun, R.[Raquel],
Real-time coarse-to-fine topologically preserving segmentation,
CVPR15(2947-2955)
IEEE DOI
1510
BibRef
Gupta, A.[Agrim],
Dollar, P.[Piotr],
Girshick, R.[Ross],
LVIS: A Dataset for Large Vocabulary Instance Segmentation,
CVPR19(5351-5359).
IEEE DOI
2002
BibRef
Wang, X.L.[Xin-Long],
Liu, S.[Shu],
Shen, X.Y.[Xiao-Yong],
Shen, C.H.[Chun-Hua],
Jia, J.Y.[Jia-Ya],
Associatively Segmenting Instances and Semantics in Point Clouds,
CVPR19(4091-4100).
IEEE DOI
2002
BibRef
Hsu, K.J.[Kuang-Jui],
Lin, Y.Y.[Yen-Yu],
Chuang, Y.Y.[Yung-Yu],
DeepCO3: Deep Instance Co-Segmentation by Co-Peak Search and
Co-Saliency Detection,
CVPR19(8838-8847).
IEEE DOI
2002
BibRef
Araslanov, N.[Nikita],
Rothkopf, C.A.[Constantin A.],
Roth, S.[Stefan],
Actor-Critic Instance Segmentation,
CVPR19(8229-8238).
IEEE DOI
2002
BibRef
Liu, S.,
Qi, L.,
Qin, H.,
Shi, J.,
Jia, J.,
Path Aggregation Network for Instance Segmentation,
CVPR18(8759-8768)
IEEE DOI
1812
Proposals, Feature extraction, Task analysis, Image segmentation,
Object detection, Training, Semantics
BibRef
Chen, L.,
Hermans, A.,
Papandreou, G.,
Schroff, F.,
Wang, P.,
Adam, H.,
MaskLab: Instance Segmentation by Refining Object Detection with
Semantic and Direction Features,
CVPR18(4013-4022)
IEEE DOI
1812
Semantics, Agriculture, Image segmentation, Object detection,
Convolution, Feature extraction, Encoding
BibRef
Neven, D.[Davy],
De Brabandere, B.[Bert],
Proesmans, M.[Marc],
Van Gool, L.J.[Luc J.],
Instance Segmentation by Jointly Optimizing Spatial Embeddings and
Clustering Bandwidth,
CVPR19(8829-8837).
IEEE DOI
2002
BibRef
Qi, L.[Lu],
Jiang, L.[Li],
Liu, S.[Shu],
Shen, X.Y.[Xiao-Yong],
Jia, J.Y.[Jia-Ya],
Amodal Instance Segmentation With KINS Dataset,
CVPR19(3009-3018).
IEEE DOI
2002
BibRef
Pham, Q.H.[Quang-Hieu],
Nguyen, T.[Thanh],
Hua, B.S.[Binh-Son],
Roig, G.[Gemma],
Yeung, S.K.[Sai-Kit],
JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds With
Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields,
CVPR19(8819-8828).
IEEE DOI
2002
BibRef
Chen, K.[Kai],
Pang, J.M.[Jiang-Miao],
Wang, J.Q.[Jia-Qi],
Xiong, Y.[Yu],
Li, X.X.[Xiao-Xiao],
Sun, S.Y.[Shu-Yang],
Feng, W.[Wansen],
Liu, Z.[Ziwei],
Shi, J.P.[Jian-Ping],
Ouyang, W.L.[Wan-Li],
Loy, C.C.[Chen Change],
Lin, D.[Dahua],
Hybrid Task Cascade for Instance Segmentation,
CVPR19(4969-4978).
IEEE DOI
2002
BibRef
Elich, C.[Cathrin],
Engelmann, F.[Francis],
Kontogianni, T.[Theodora],
Leibe, B.[Bastian],
3D Bird's-Eye-View Instance Segmentation,
GCPR19(48-61).
Springer DOI
1911
BibRef
Shang, C.,
Wu, Q.,
Meng, F.,
Xu, L.,
Instance Segmentation by Learning Deep Feature in Embedding Space,
ICIP19(2444-2448)
IEEE DOI
1910
Instance Segmentation, Instance Discrimination Network,
Embedding Space, Deep Feature
BibRef
Liu, Y.F.[Yan-Feng],
Psota, E.T.[Eric T.],
Pérez, L.C.[Lance C.],
Layered Embeddings for Amodal Instance Segmentation,
ICIAR19(I:102-111).
Springer DOI
1909
Code, Segmentation. Code available:
WWW Link.
BibRef
Couprie, C.[Camille],
Luc, P.[Pauline],
Verbeek, J.[Jakob],
Joint Future Semantic and Instance Segmentation Prediction,
AnticipateBeh18(III:154-168).
Springer DOI
1905
BibRef
Halupka, K.,
Garnavi, R.,
Moore, S.,
Deep Semantic Instance Segmentation of Tree-Like Structures Using
Synthetic Data,
WACV19(1713-1722)
IEEE DOI
1904
data analysis, feature extraction, image segmentation,
learning (artificial intelligence), neural nets,
Periodic structures
BibRef
Follmann, P.[Patrick],
Nig, R.K.[Rebecca Kö],
Rtinger, P.H.[Philipp Hä],
Klostermann, M.[Michael],
Ttger, T.B.[Tobias Bö],
Learning to See the Invisible: End-to-End Trainable Amodal Instance
Segmentation,
WACV19(1328-1336)
IEEE DOI
1904
image segmentation, learning (artificial intelligence),
object detection, COCOA cls, D2S amodal, COCO amodal dataset,
BibRef
Li, K.[Ke],
Malik, J.[Jitendra],
Amodal Instance Segmentation,
ECCV16(II: 677-693).
Springer DOI
1611
predict the region encompassing both visible and occluded parts of each object.
BibRef
Li, K.[Ke],
Hariharan, B.[Bharath],
Malik, J.[Jitendra],
Iterative Instance Segmentation,
CVPR16(3659-3667)
IEEE DOI
1612
BibRef
Li, Z.X.[Zuo-Xin],
Zhou, F.Q.[Fu-Qiang],
Yang, L.[Lu],
Fast Single Shot Instance Segmentation,
ACCV18(IV:257-272).
Springer DOI
1906
BibRef
Manohar, K.V.,
Niitani, Y.[Yusuke],
An End-to-End Tree Based Approach for Instance Segmentation,
POCV18(V:521-527).
Springer DOI
1905
BibRef
Liu, Y.,
Wang, R.,
Shan, S.,
Chen, X.,
Structure Inference Net: Object Detection Using Scene-Level Context
and Instance-Level Relationships,
CVPR18(6985-6994)
IEEE DOI
1812
Object detection, Feature extraction, Logic gates, Visualization,
Detectors, Context modeling, Image edge detection
BibRef
Zhou, Y.Z.[Yan-Zhao],
Zhu, Y.[Yi],
Ye, Q.X.[Qi-Xiang],
Qiu, Q.[Qiang],
Jiao, J.B.[Jian-Bin],
Weakly Supervised Instance Segmentation Using Class Peak Response,
CVPR18(3791-3800)
IEEE DOI
1812
Image segmentation, Visualization, Training, Semantics, Proposals,
Image color analysis, Kernel
BibRef
Liu, Y.D.[Yi-Ding],
Yang, S.[Siyu],
Li, B.[Bin],
Zhou, W.G.[Wen-Gang],
Xu, J.Z.[Ji-Zheng],
Li, H.Q.A.[Hou-Qi-Ang],
Lu, Y.[Yan],
Affinity Derivation and Graph Merge for Instance Segmentation,
ECCV18(III: 708-724).
Springer DOI
1810
BibRef
Novotny, D.[David],
Albanie, S.[Samuel],
Larlus, D.[Diane],
Vedaldi, A.[Andrea],
Semi-convolutional Operators for Instance Segmentation,
ECCV18(I: 89-105).
Springer DOI
1810
BibRef
Margffoy-Tuay, E.[Edgar],
Pérez, J.C.[Juan C.],
Botero, E.[Emilio],
Arbeláez, P.[Pablo],
Dynamic Multimodal Instance Segmentation Guided by Natural Language
Queries,
ECCV18(XI: 656-672).
Springer DOI
1810
BibRef
Xu, W.Q.[Wen-Qiang],
Li, Y.L.[Yong-Lu],
Lu, C.W.[Ce-Wu],
SRDA: Generating Instance Segmentation Annotation via Scanning,
Reasoning and Domain Adaptation,
ECCV18(XII: 124-140).
Springer DOI
1810
BibRef
Pham, T.[Trung],
Kumar, B.G.V.[B. G. Vijay],
Do, T.T.[Thanh-Toan],
Carneiro, G.[Gustavo],
Reid, I.D.[Ian D.],
Bayesian Semantic Instance Segmentation in Open Set World,
ECCV18(X: 3-18).
Springer DOI
1810
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
Li, Y.[Yao],
Liu, L.Q.[Ling-Qiao],
Shen, C.H.[Chun-Hua],
van den Hengel, A.[Anton],
Image Co-Localization by Mimicking a Good Detector's Confidence Score
Distribution,
ECCV16(II: 19-34).
Springer DOI
1611
identify each instance in multiple images.
BibRef
Chen, Y.T.[Yi-Ting],
Liu, X.[Xiaokai],
Yang, M.H.[Ming-Hsuan],
Multi-instance object segmentation with occlusion handling,
CVPR15(3470-3478)
IEEE DOI
1510
BibRef
Liu, B.Y.[Bu-Yu],
He, X.M.[Xu-Ming],
Multiclass semantic video segmentation with object-level active
inference,
CVPR15(4286-4294)
IEEE DOI
1510
BibRef
Liu, B.Y.[Bu-Yu],
He, X.M.[Xu-Ming],
Gould, S.[Stephen],
Multi-class Semantic Video Segmentation with Exemplar-Based Object
Reasoning,
WACV15(1014-1021)
IEEE DOI
1503
Cognition
BibRef
He, X.M.[Xu-Ming],
Gould, S.[Stephen],
An Exemplar-Based CRF for Multi-instance Object Segmentation,
CVPR14(296-303)
IEEE DOI
1409
BibRef
Earlier:
Multi-instance Object Segmentation with Exemplars,
GMSU13(1-4)
IEEE DOI
1403
Markov processes
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
Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Co-Segmentation, Cosegmentation .