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

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

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


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:Sep 18, 2017 at 11:34:11