7.1.10.2 Object Proposals, Initial Points, Proto-Objects, Candidates

Chapter Contents (Back)
Object Detection. Object Proposals.

Yanulevskaya, V.[Victoria], Uijlings, J.[Jasper], Geusebroek, J.M.[Jan-Mark],
Salient object detection: From pixels to segments,
IVC(31), No. 1, January 2013, pp. 31-42.
Elsevier DOI 1302
Salient object detection; Object-based visual attention theory; Proto-objects BibRef

Yanulevskaya, V.[Victoria], Uijlings, J.[Jasper], Sebe, N.[Nicu],
Learning to Group Objects,
CVPR14(3134-3141)
IEEE DOI 1409
Class independent object proposals BibRef

Jie, Z.Q.[Ze-Qun], Liang, X.D.[Xiao-Dan], Feng, J.S.[Jia-Shi], Lu, W.F.[Wen Feng], Tay, E.H.F.[Eng Hock Francis], Yan, S.C.[Shui-Cheng],
Scale-Aware Pixelwise Object Proposal Networks,
IP(25), No. 10, October 2016, pp. 4525-4539.
IEEE DOI 1610
neural nets BibRef

Hu, P., Wang, W., Zhang, C., Lu, K.,
Detecting Salient Objects via Color and Texture Compactness Hypotheses,
IP(25), No. 10, October 2016, pp. 4653-4664.
IEEE DOI 1610
image classification BibRef

Huo, L.[Lina], Jiao, L.C.[Li-Cheng], Wang, S.[Shuang], Yang, S.Y.[Shu-Yuan],
Object-level saliency detection with color attributes,
PR(49), No. 1, 2016, pp. 162-173.
Elsevier DOI 1511
Candidate objectness BibRef

Kuang, P.J.[Pei-Jiang], Zhou, Z.H.[Zhi-Heng], Wu, D.C.[Dong-Cheng],
Improved Edge Boxes with Object Saliency and Location Awards,
IEICE(E99-D), No. 2, February 2016, pp. 488-495.
WWW Link. 1604
BibRef

Oramas Mogrovejo, J.A.[José Antonio], Tuytelaars, T.[Tinne],
Recovering hard-to-find object instances by sampling context-based object proposals,
CVIU(152), No. 1, 2016, pp. 118-130.
Elsevier DOI 1609
Object detection BibRef

Deng, H.[He], Sun, X.P.[Xian-Ping], Liu, M.[Maili], Ye, C.H.[Chao-Hui], Zhou, X.[Xin],
Entropy-based window selection for detecting dim and small infrared targets,
PR(61), No. 1, 2017, pp. 66-77.
Elsevier DOI 1609
Dim and small target detection BibRef

Lee, D.[Daeha], Kim, J.[Jaehong], Kim, H.H.[Ho-Hee], Kim, S.J.[Soon-Ja],
The Computation Reduction in Object Detection via Composite Structure of Modified Integral Images,
IEICE(E100-D), No. 1, January 2017, pp. 229-233.
WWW Link. 1701
BibRef

Huang, S., Wang, W., He, S.F.[Sheng-Feng], Lau, R.W.H.[Rynson W.H.],
Stereo Object Proposals,
IP(26), No. 2, February 2017, pp. 671-683.
IEEE DOI 1702
object detection BibRef

Huang, S., Wang, W., He, S.F.[Sheng-Feng], Lau, R.W.H.[Rynson W.H.],
Egocentric Temporal Action Proposals,
IP(27), No. 2, February 2018, pp. 764-777.
IEEE DOI 1712
Atom optics, Cameras, Optical computing, Optical imaging, Proposals, Videos, Temporal action proposals, actionness estimation, temporal actionness network BibRef

Ramesh, B., Xiang, C., Lee, T.H.,
Multiple object cues for high performance vector quantization,
PR(67), No. 1, 2017, pp. 380-395.
Elsevier DOI 1704
Log-polar transform BibRef

Li, J.A.[Jian-An], Wei, Y.C.[Yun-Chao], Liang, X.D.[Xiao-Dan], Dong, J.[Jian], Xu, T.F.[Ting-Fa], Feng, J.S.[Jia-Shi], Yan, S.C.[Shui-Cheng],
Attentive Contexts for Object Detection,
MultMed(19), No. 5, May 2017, pp. 944-954.
IEEE DOI 1704
Context for object detection. BibRef

Li, J.A.[Jian-An], Liang, X.D.[Xiao-Dan], Wei, Y.C.[Yun-Chao], Xu, T.F.[Ting-Fa], Feng, J.S.[Jia-Shi], Yan, S.C.[Shui-Cheng],
Perceptual Generative Adversarial Networks for Small Object Detection,
CVPR17(1951-1959)
IEEE DOI 1711
Feature extraction, Gallium nitride, Generators, Image resolution, Object detection, Training BibRef

Wang, J.[Jing], Shen, J.[Jie], Li, P.[Ping],
Object proposal with kernelized partial ranking,
PR(69), No. 1, 2017, pp. 299-309.
Elsevier DOI 1706
Object proposal BibRef

Li, W.[Wei], Li, H.L.[Hong-Liang], Luo, B.[Bing], Shi, H.C.[Heng-Can], Wu, Q.B.[Qing-Bo], Ngan, K.N.[King Ngi],
Improving object proposals with top-down cues,
SP:IC(56), No. 1, 2017, pp. 20-27.
Elsevier DOI 1706
Object, proposals BibRef

Tang, S., Li, Y., Deng, L., Zhang, Y.,
Object Localization Based on Proposal Fusion,
MultMed(19), No. 9, September 2017, pp. 2105-2116.
IEEE DOI 1708
Complexity theory, Feature extraction, Object detection, Proposals, Search problems, Testing, Training, Dense proposal fusion, object detection, object localization, region proposal BibRef


Yu, T., Wu, Y., Yuan, J.,
HOPE: Hierarchical Object Prototype Encoding for Efficient Object Instance Search in Videos,
CVPR17(3195-3204)
IEEE DOI 1711
Computational efficiency, Encoding, Proposals, Prototypes, Search problems, Videos, Visualization BibRef

Hu, H., Lan, S., Jiang, Y., Cao, Z., Sha, F.,
FastMask: Segment Multi-scale Object Candidates in One Shot,
CVPR17(2280-2288)
IEEE DOI 1711
Feature extraction, Head, Image segmentation, Neck, Proposals, Semantics BibRef

Hayder, Z., He, X., Salzmann, M.,
Boundary-Aware Instance Segmentation,
CVPR17(587-595)
IEEE DOI 1711
Image segmentation, Proposals, Robustness, Semantics, Shape, Transforms BibRef

Li, G., Xie, Y., Lin, L., Yu, Y.,
Instance-Level Salient Object Segmentation,
CVPR17(247-256)
IEEE DOI 1711
Image segmentation, Neural networks, Object detection, Proposals, Semantics, Streaming, media BibRef

Deng, Z., Latecki, L.J.,
Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes from 2D Ones in RGB-Depth Images,
CVPR17(398-406)
IEEE DOI 1711
Feature extraction, Object detection, Proposals, Solid modeling, Two, dimensional, displays BibRef

Abbeloos, W., Caccamo, S., Ataer-Cansizoglu, E., Taguchi, Y., Feng, C., Lee, T.Y.,
Detecting and Grouping Identical Objects for Region Proposal and Classification,
DeepLearnRV17(501-502)
IEEE DOI 1709
Clustering algorithms, Computer vision, Object detection, Object recognition, Pipelines, Proposals BibRef

Li, S., Zhang, H., Zhang, J., Ren, Y., Kuo, C.C.J.,
Box Refinement: Object Proposal Enhancement and Pruning,
WACV17(979-988)
IEEE DOI 1609
Detectors, Feature extraction, Image edge detection, Neural networks, Proposals, Search, problems BibRef

Lauri, M.[Mikko], Frintrop, S.[Simone],
Object Proposal Generation Applying the Distance Dependent Chinese Restaurant Process,
SCIA17(I: 260-272).
Springer DOI 1706
BibRef

Waris, M.A., Iosifidis, A., Gabbouj, M.,
Object proposals using CNN-based edge filtering,
ICPR16(627-632)
IEEE DOI 1705
Feature extraction, Image edge detection, Merging, Object detection, Proposals, Semantics, Deep Learning, Object Detection, Object Proposals, Region, Of, Interest BibRef

Zhang, H.Y.[Hao-Yang], He, X.M.[Xu-Ming], Porikli, F.M.[Fatih M.],
Learning Spatial Transforms for Refining Object Segment Proposals,
WACV17(37-46)
IEEE DOI 1609
BibRef
Earlier:
Learning to Generate Object Segment Proposals with Multi-modal Cues,
ACCV16(I: 121-136).
Springer DOI 1704
Feature extraction, Image segmentation, Pipelines, Proposals, Semantics, Transforms, Two, dimensional, displays BibRef

Shi, W., Zhu, H., Yang, L., Luo, Y.,
Shape based co-segmentation repairing by segment evaluation and object proposals,
VCIP16(1-4)
IEEE DOI 1701
Computational modeling BibRef

Zhang, R., Wang, W.,
An advanced local offset matching strategy for object proposal matching,
VCIP16(1-4)
IEEE DOI 1701
Bayes methods BibRef

Knaub, A.[Anton], Narayan, V.[Vikram], Adameck, M.[Markus],
Performance Evaluation of Bottom-Up Saliency Models for Object Proposal Generation,
CRV16(266-272)
IEEE DOI 1612
Object proposal generation BibRef

Ke, W., Zhang, T., Chen, J., Wan, F., Ye, Q., Han, Z.,
Texture Complexity Based Redundant Regions Ranking for Object Proposal,
Robust16(1083-1091)
IEEE DOI 1612
BibRef

Zhang, Y., Jiang, Z., Chen, X., Davis, L.S.,
Generating Discriminative Object Proposals via Submodular Ranking,
Robust16(1168-1176)
IEEE DOI 1612
BibRef

Singh, K.K.[Krishna Kumar], Xiao, F.Y.[Fan-Yi], Lee, Y.J.[Yong Jae],
Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection,
CVPR16(3548-3556)
IEEE DOI 1612
BibRef

Li, D.[Dong], Huang, J.B.[Jia-Bin], Li, Y.[Yali], Wang, S.J.[Sheng-Jin], Yang, M.H.[Ming-Hsuan],
Weakly Supervised Object Localization with Progressive Domain Adaptation,
CVPR16(3512-3520)
IEEE DOI 1612
Image level annotation, not location. BibRef

Sun, C.[Chen], Paluri, M.[Manohar], Collobert, R.[Ronan], Nevatia, R.[Ram], Bourdev, L.[Lubomir],
ProNet: Learning to Propose Object-Specific Boxes for Cascaded Neural Networks,
CVPR16(3485-3493)
IEEE DOI 1612
BibRef

Pham, T.T., Rezatofighi, S.H., Reid, I.D., Chin, T.J.,
Efficient Point Process Inference for Large-Scale Object Detection,
CVPR16(2837-2845)
IEEE DOI 1612
BibRef

Lu, Y.X.[Yong-Xi], Javidi, T.[Tara], Lazebnik, S.[Svetlana],
Adaptive Object Detection Using Adjacency and Zoom Prediction,
CVPR16(2351-2359)
IEEE DOI 1612
BibRef

Tang, Y.X.[Yu-Xing], Wang, J.[Josiah], Gao, B.Y.[Bo-Yang], Dellandréa, E.[Emmanuel], Gaizauskas, R.[Robert], Chen, L.M.[Li-Ming],
Large Scale Semi-Supervised Object Detection Using Visual and Semantic Knowledge Transfer,
CVPR16(2119-2128)
IEEE DOI 1612
BibRef

Kong, T., Yao, A., Chen, Y., Sun, F.,
HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection,
CVPR16(845-853)
IEEE DOI 1612
BibRef

Chavali, N., Agrawal, H., Mahendru, A., Batra, D.,
Object-Proposal Evaluation Protocol is 'Gameable',
CVPR16(835-844)
IEEE DOI 1612
BibRef

Zeng, X.Y.[Xing-Yu], Ouyang, W.L.[Wan-Li], Yang, B.[Bin], Yan, J.J.[Jun-Jie], Wang, X.G.[Xiao-Gang],
Gated Bi-Directional CNN for Object Detection,
ECCV16(VII: 354-369).
Springer DOI 1611
BibRef

Tiwari, L.[Lokender], Anand, S.[Saket], Mittal, S.[Sushil],
Robust Multi-Model Fitting Using Density and Preference Analysis,
ACCV16(IV: 308-323).
Springer DOI 1704
BibRef

Tiwari, L.[Lokender], Anand, S.[Saket],
Fast hypothesis filtering for multi-structure geometric model fitting,
ICIP16(3728-3732)
IEEE DOI 1610
Clustering algorithms BibRef

Bappy, J.H., Roy-Chowdhury, A.K.,
Inter-dependent CNNs for joint scene and object recognition,
ICPR16(3386-3391)
IEEE DOI 1705
BibRef
And:
CNN based region proposals for efficient object detection,
ICIP16(3658-3662)
IEEE DOI 1610
Detectors, Feature extraction, Neural networks, Object detection, Object recognition, Proposals. BibRef

Paul, S., Bappy, J.H., Roy-Chowdhury, A.K.,
Efficient selection of informative and diverse training samples with applications in scene classification,
ICIP16(494-498)
IEEE DOI 1610
Computational modeling BibRef

Horiguchi, S., Aizawa, K., Ogawa, M.,
The log-normal distribution of the size of objects in daily meal images and its application to the efficient reduction of object proposals,
ICIP16(3668-3672)
IEEE DOI 1610
Gaussian distribution BibRef

Zhang, H., He, X., Porikli, F.M., Kneip, L.,
Semantic context and depth-aware object proposal generation,
ICIP16(1-5)
IEEE DOI 1610
Context BibRef

Peng, L., Qi, X.,
Temporal objectness: Model-free learning of object proposals in video,
ICIP16(3663-3667)
IEEE DOI 1610
Detectors BibRef

Werner, T.[Thomas], Martín-García, G.[Germán], Frintrop, S.[Simone],
Saliency-Guided Object Candidates Based on Gestalt Principles,
CVS15(34-44).
Springer DOI 1507
BibRef

Klein, D.A.[Dominik Alexander], Frintrop, S.[Simone],
Salient Pattern Detection Using W2 on Multivariate Normal Distributions,
DAGM12(246-255).
Springer DOI 1209
BibRef

Klein, D.A.[Dominik Alexander], Schulz, D.[Dirk], Frintrop, S.[Simone],
Boosting with a Joint Feature Pool from Different Sensors,
CVS09(63-72).
Springer DOI 0910
BibRef

Frintrop, S.,
The high repeatability of salient regions,
ViA08(xx-yy). 0810
BibRef

Lee, T., Fidler, S., Dickinson, S.,
Learning to Combine Mid-Level Cues for Object Proposal Generation,
ICCV15(1680-1688)
IEEE DOI 1602
Adaptation models BibRef

Zhu, H.Y.[Hong-Yuan], Lu, S.J.[Shi-Jian], Cai, J.F.[Jian-Fei], Lee, G.Q.[Guang-Qing],
Diagnosing state-of-the-art object proposal methods,
BMVC15(xx-yy).
DOI Link 1601
See also How good are detection proposals, really?. BibRef

Chen, X.Z.[Xiao-Zhi], Ma, H.M.[Hui-Min], Wang, X.[Xiang], Zhao, Z.C.[Zhi-Chen],
Improving object proposals with multi-thresholding straddling expansion,
CVPR15(2587-2595)
IEEE DOI 1510
BibRef

Liu, S.[Shu], Lu, C.[Cewu], Jia, J.[Jiaya],
Box Aggregation for Proposal Decimation: Last Mile of Object Detection,
ICCV15(2569-2577)
IEEE DOI 1602
Computational modeling BibRef

Pont-Tuset, J.[Jordi], van Gool, L.J.[Luc J.],
Boosting Object Proposals: From Pascal to COCO,
ICCV15(1546-1554)
IEEE DOI 1602
Survey of techniques and impact of changing standard benchmark datasets. BibRef

Manen, S.[Santiago], Guillaumin, M.[Matthieu], Van Gool, L.J.[Luc J.],
Prime Object Proposals with Randomized Prim's Algorithm,
ICCV13(2536-2543)
IEEE DOI 1403
Object Detection; Object Proposal BibRef

Ristin, M.[Marko], Gall, J.[Juergen], Van Gool, L.J.[Luc J.],
Local Context Priors for Object Proposal Generation,
ACCV12(I:57-70).
Springer DOI 1304
Selective search to get hypotheses BibRef

He, S.F.[Sheng-Feng], Lau, R.W.H.[Rynson W. H.],
Oriented Object Proposals,
ICCV15(280-288)
IEEE DOI 1602
Detectors BibRef

Kwak, S.[Suha], Cho, M.[Minsu], Laptev, I., Ponce, J.[Jean], Schmid, C.[Cordelia],
Unsupervised Object Discovery and Tracking in Video Collections,
ICCV15(3173-3181)
IEEE DOI 1602
BibRef
And: A2, A1, A5, A4, Only:
Unsupervised object discovery and localization in the wild: Part-based matching with bottom-up region proposals,
CVPR15(1201-1210)
IEEE DOI 1510
Coherence. dominant objects from a noisy image collection with multiple object classes. BibRef

Zitnick, C.L.[C. Lawrence], Dollár, P.[Piotr],
Edge Boxes: Locating Object Proposals from Edges,
ECCV14(V: 391-405).
Springer DOI 1408
BibRef

Krähenbühl, P.[Philipp], Koltun, V.[Vladlen],
Geodesic Object Proposals,
ECCV14(V: 725-739).
Springer DOI 1408
BibRef

Rantalankila, P.[Pekka], Kannala, J.H.[Ju-Ho], Rahtu, E.[Esa],
Generating Object Segmentation Proposals Using Global and Local Search,
CVPR14(2417-2424)
IEEE DOI 1409
Object detection BibRef

Bonev, B.[Boyan], Yuille, A.L.[Alan L.],
A Fast and Simple Algorithm for Producing Candidate Regions,
ECCV14(III: 535-549).
Springer DOI 1408
e.g. initial bounding box? BibRef

Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Maximally Stable Extremal Regions, MSER Descriptions .


Last update:Dec 28, 2017 at 17:11:31