4.8.2 Perceptual Grouping, Saliency, General Systems

Chapter Contents (Back)
Human Vision. Grouping, Perceptual. Perceptual Grouping. Saliency.

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Earlier: ICPR84(512-514). BibRef

Lowe, D.G.,
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Boston: KluwerAcademic Publishers, June 1985. BibRef 8506 Ph.D.Thesis (CS). ISBN 0-89838-172-X. Grouping, Perceptual. Grouping, Models. Recognition, Model Based. See also Recovery of Three-Dimensional Structure from Image Curves, The.
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And:
Perceptual Organization as a Basis for Visual Recognition,
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Earlier:
Segmentation and Aggregation: An Approach to Figure-Ground Phenomena,
DARPA82(168-178), BibRef RCV87(282-292). Figure-Ground separation. Bottom up grouping is a prerequisite for recognition. This breaks into 3 types of grouping: segmentation, 3D interpretation, descriptions of objects. BibRef

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Perceptual Organization for Scene Segmentation and Description,
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IEEE DOI BibRef 9206 USC Computer Vision BibRef
Earlier:
Segmentation and Description Based on Perceptual Organization,
CVPR89(333-341).
IEEE DOI BibRef
And:
Perceptual Organization for Segmentation and Description,
DARPA89(415-424). Segmentation, Grouping. Groupings of line features are located by co-curvilinearity and symmetry to find curves, symmetries and ribbons. These combine to give 2-D shapes and object surfaces. Combination uses a Hopfield network. See also Using Perceptual Organization to Extract 3-D Structures. BibRef

Mohan, R.,
Perceptual Organization for Computer Vision,
USC_IRISTR-254, August 1989, BibRef 8908 Ph.D.Thesis (CS). Thesis with perceptual organization for segmentation and matching applications. BibRef

Ahuja, N., and Tuceryan, M.,
Extraction of Early Perceptual Structure in Dot Patterns: Integrating Region, Boundary, and Component Gestalt,
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Earlier: A2, A1:
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And:
Optimization in Model Matching and Perceptual Organization: A First Look,
YaleCS, YaleU/DCS/RR-634, June 1988. Hopfield network. BibRef

Sarkar, S., Boyer, K.L.,
Perceptual Organization in Computer Vision: A Review and a Proposal for a Classificatory Structure,
SMC(23), No. 2, 1993, pp. 382-399. BibRef 9300

Sarkar, S., and Boyer, K.L.,
Integration, Inference, and Management of Spatial Information Using Bayesian Networks: Perceptual Organization,
PAMI(15), No. 3, March 1993, pp. 256-274.
IEEE DOI Bayes Nets. BibRef 9303
Earlier:
Perceptual Organization Using Bayesian Networks,
CVPR92(251-256).
IEEE DOI Integrate a number of different systems. BibRef

Sarkar, S., Boyer, K.L.,
Using Perceptual Inference Networks To Manage Vision Processes,
CVIU(62), No. 1, July 1995, pp. 27-46.
DOI Link BibRef 9507
Earlier: ICPR94(A:808-810).
IEEE DOI BibRef

Sarkar, S., and Boyer, K.L.,
Computing Perceptual Organization in Computer Vision,
World Scientific1994. (ISBN: 981-02-1832-X). 232pp. BibRef 9400 Book Code, Perceptual Grouping. Code:
HTML Version. Based on Sarkar's thesis. Derive a framework for perceptual organization at various levels. lower levels feed higher levels. Does not get to the recognition level. BibRef

Sarkar, S., Boyer, K.L.,
Automated Design of Bayesian Perceptual Inference Networks,
CVPR94(98-103).
IEEE DOI BibRef 9400

Sarkar, S., Boyer, K.L.,
A Computational Structure for Preattentive Perceptual Organization: Graphical Enumeration and Voting Methods,
SMC(24), 1994, pp. 246-267. BibRef 9400

Sarkar, S., Boyer, K.L.,
Computing Perceptual Organization Using Voting Methods and Graphical Enumeration,
ICPR92(I:263-267).
IEEE DOI BibRef 9200

Pun, T.[Thierry],
Electromagnetic Models for Perceptual Grouping,
AMV Strategies921992, pp. 129-149. BibRef 9200

Saund, E.[Eric],
Putting Knowledge into a Visual Shape Representation,
AI(54), No. 1-2, March 1992, pp. 71-119.
WWW Link. BibRef 9203
Earlier:
Representation and the Dimensions of Shape Deformation,
ICCV90(684-689).
IEEE DOI BibRef
And:
The Role of Knowledge in Visual Shape Representation,
MIT AI-TR-1092, October 1988.
WWW Link. BibRef

Chen, L.H.,
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von der Malsburg, C.[Christoph], and Buhmann, J.M.,
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Sompolinsky, H., Golomb, D., and Kleinfeld, D.,
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Lebegue, X., Aggarwal, J.K.,
Significant Line Segments for an Indoor Mobile Robot,
RA(9), 1993, pp. 801-815. BibRef 9300
And:
Detecting 3D Parallel Lines for Perceptual Organization,
ECCV92(720-724).
Springer DOI BibRef

Denasi, S., Quaglia, G., and Rinaudi, D.,
The Use of Perceptual Organization in the Prediction of Geometric Structures,
PRL(13), No. 7, 1991, pp. 529-539. BibRef 9100

Leclerc, Y.G.,
Region Grouping Using the Minimum-Description-Length Principle,
DARPA90(473-481). Group transparent surface regions together. (Some of the theory on human perception seems to say this only works one way, not the other?) BibRef 9000

Shashua, A., and Ullman, S.,
Structural Saliency: The Detection of Globally Salient Structures Using a Locally Connected Network,
ICCV88(321-327).
IEEE DOI BibRef 8800
And: A2, A1: MIT AI Memo-1061, July 1988. BibRef

Shashua, A., and Ullman, S.,
Grouping Contours by Iterated Pairing Network,
Neural Info(3), 1991, pp. 335-341, BibRef 9100

Borra, S.[Sudhir], Sarkar, S.[Sudeep],
A Framework for Performance Characterization of Intermediate Level Grouping Modules,
PAMI(19), No. 11, November 1997, pp. 1306-1312.
IEEE DOI Code and images available:
HTML Version. 9712
Compare (in order of ranking): Jacobs: See also Robust and Efficient Detection of Salient Convex Groups. Sarkar-Boyer: See also Integration, Inference, and Management of Spatial Information Using Bayesian Networks: Perceptual Organization. Etemadi: See also Low-Level Grouping of Straight Line Segments. BibRef

Feldman, J.[Jacob],
Perceptual Grouping by Selection of a Logically Minimal Model,
IJCV(55), No. 1, September 2003, pp. 5-25.
DOI Link 0307
BibRef

Feldman, J.[Jacob],
Regularity-Based Perceptual Grouping,
CompIntel(13), No. 4, November 1997, pp. 582-623. 9801
BibRef
Earlier:
Efficient Regularity-Based Grouping,
CVPR97(288-294).
IEEE DOI 9704
Grouping, general. BibRef

Feldman, J.,
Constructing perceptual categories,
CVPR92(244-250).
IEEE DOI 0403
BibRef

Amir, A., Lindenbaum, M.,
A Generic Grouping Algorithm and Its Quantitative Analysis,
PAMI(20), No. 2, February 1998, pp. 168-185.
IEEE DOI 9803
Grouping by graph clustering. Find lines and curves in noisy images. BibRef

Amir, A., Lindenbaum, M.,
Quantitative Analysis of Grouping Processes,
ECCV96(I:369-384).
Springer DOI BibRef 9600

Amir, A., Lindenbaum, M.,
Grouping-Based Nonadditive Verification,
PAMI(20), No. 2, February 1998, pp. 186-192.
IEEE DOI 9803
BibRef

Boyer, K.L.[Kim L.], Sarkar, S.[Sudeep],
Perceptual Organization in Computer Vision: Status, Challenges, and Potential,
CVIU(76), No. 1, October 1999, pp. 1-6.
DOI Link Guest Editors' Introduction. Perceptual Grouping BibRef 9910

Boyer, K.L.[Kim L.], Sarkar, S.[Sudeep],
Perceptual Organization for Artificial Vision Systems,
KluwerMarch 2000, ISBN 0-7923-7799-0
WWW Link. BibRef 0003

Foresti, G.L., Regazzoni, C.S.,
A Hierarchical Approach to Feature Extraction and Grouping,
IP(9), No. 6, June 2000, pp. 1056-1074.
IEEE DOI 0006
BibRef

Luo, J.B.[Jie-Bo], Singhal, A.[Amit],
On Measuring Low-Level Self and Relative Saliency in Photographic Images,
PRL(22), No. 2, February 2001, pp. 157-169.
Elsevier DOI 0101
BibRef
Earlier:
On Measuring Low-Level Saliency in Photographic Images,
CVPR00(I: 84-89).
IEEE DOI 0005
Seg. by Saliency BibRef

Mordohai, P.[Philippos], Medioni, G.[Gérard],
Tensor Voting: A Perceptual Organization Approach to Computer Vision and Machine Learning,
Morgan Claypool2006. Synthesis Lectures on Image, Video, and Multimedia Processing
WWW Link. Survey, Tensor Voting. Tensor Voting. BibRef 0600

Mordohai, P.[Philippos], Medioni, G.[Gérard],
The Tensor Voting Framework,
ETCV04(Chapter 5). BibRef 0400

Medioni, G., Lee, M.S.[Mi-Suen], Tang, C.K.[Chi-Keung],
A Computational Framework for Segmentation and Grouping,
Elsevier2000. ISBN: 0-444-50353-6 BibRef 0001 USC Computer Vision Tensor Voting. Conceptual framework that solves a wide variety of problems -- Tensor Voting.
WWW Link. BibRef

Wu, T.P.[Tai-Pang], Yeung, S.K.[Sai-Kit], Jia, J.Y.[Jia-Ya], Tang, C.K.[Chi-Keung], Medioni, G.[Gerard],
A Closed-Form Solution to Tensor Voting: Theory and Applications,
PAMI(34), No. 8, August 2012, pp. 1482-1495.
IEEE DOI 1206
Tensor Voting. Closed form solution. Exact, continuous, efficient algorithm to compute tensor for structure detection and outlier attenuation. See also Comments on 'A Closed-Form Solution to Tensor Voting: Theory and Applications'. BibRef

Johansen, P.[Peter], Ersbøll, B.K.[Bjarne K.],
Guest Editors' Introduction,
IJCV(42), No. 1-2, April-May 2001, pp. 5-5.
DOI Link 0106
BibRef
And: JMIV(15), No. 1/2, July 2001, pp. 5-5. 0106
Perceptual grouping. Papers in both journals. BibRef

Pauli, J.[Josef], Sommer, G.[Gerald],
Perceptual organization with image formation compatibilities,
PRL(23), No. 7, May 2002, pp. 803-817.
Elsevier DOI 0203
BibRef

Zweck, J.[John], Williams, L.R.[Lance R.],
Euclidean Group Invariant Computation of Stochastic Completion Fields Using Shiftable-Twistable Functions,
JMIV(21), No. 2, September 2004, pp. 135-154.
DOI Link 0409
BibRef
Earlier: ECCV00(II: 100).
Springer DOI 0003
BibRef

Maeder, A.J.[Anthony J.],
The image importance approach to human vision based image quality characterization,
PRL(26), No. 3, February 2005, pp. 347-354.
WWW Link. 0501
BibRef

Maeder, A.J.[Anthony J.], Osberger, W.[Wilfried],
Automatic Identification of Perceptually Important Regions in an Image Using a Model of the Human Visual System,
ICPR98(Vol I: 701-704).
IEEE DOI Features used to find salient regions. BibRef 9800

Chen, H.T.[Hwann-Tzong], Liu, T.L.[Tyng-Luh], Fuh, C.S.[Chiou-Shann],
Tone Reproduction: A Perspective from Luminance-Driven Perceptual Grouping,
IJCV(65), No. 1-2, November 2005, pp. 73-96.
Springer DOI 0604
BibRef
Earlier: A1 and A2 only, Add A3: Chang, T.L.[Tien-Lung], CVPR05(II: 369-376).
IEEE DOI 0507
BibRef

Feldman, T.[Thomas], Younes, L.[Laurent],
Homeostatic image perception: An artificial system,
CVIU(102), No. 1, April 2006, pp. 70-80.
WWW Link. Image model; Visual system; Gibbs distribution; Saliency detection 0604
Complements PCA by analyzing interactions. BibRef

Parvin, B., Yang, Q.[Qing], Han, J., Chang, H., Rydberg, B., Barcellos-Hoff, M.H.,
Iterative Voting for Inference of Structural Saliency and Characterization of Subcellular Events,
IP(16), No. 3, March 2007, pp. 615-623.
IEEE DOI 0703
See also Tool for the Quantitative Spatial Analysis of Complex Cellular Systems, A. BibRef

Yang, Q.[Qing], Parvin, B., Barcellos-Hoff, M.H.,
Localization of saliency through iterative voting,
ICPR04(I: 63-66).
IEEE DOI 0409
BibRef

Hu, J.Y.[Jian-Ying], Mojsilovic, A.[Aleksandra],
High-utility pattern mining: A method for discovery of high-utility item sets,
PR(40), No. 11, November 2007, pp. 3317-3324.
WWW Link. 0707
High-utility item sets; Pattern mining; Partition tree BibRef

Loss, L.A.[Leandro A.], Bebis, G.N.[George N.], Nicolescu, M.[Mircea], Skurikhin, A.N.[Alexei N.],
An iterative multi-scale tensor voting scheme for perceptual grouping of natural shapes in cluttered backgrounds,
CVIU(113), No. 1, January 2009, pp. 126-149.
Elsevier DOI 0812
BibRef
Earlier:
An Automatic Framework for Figure-Ground Segmentation in Cluttered Backgrounds,
BMVC07(xx-yy).
PDF File. 0709
BibRef
Earlier:
Perceptual Grouping Based on Iterative Multi-scale Tensor Voting,
ISVC06(II: 870-881).
Springer DOI 0611
Segmentation; Boundary detection; Grouping; Object detection; Tensor voting BibRef

Loss, L.A.[Leandro A.], Bebis, G.N.[George N.], Parvin, B.[Bahram],
Iterative Tensor Voting for Perceptual Grouping of Ill-Defined Curvilinear Structures,
MedImg(30), No. 8, August 2011, pp. 1503-1513.
IEEE DOI 1108
BibRef
Earlier:
Tunable tensor voting improves grouping of membrane-bound macromolecules,
MMBIA09(72-78).
IEEE DOI 0906
BibRef

Guo, C.L.[Chen-Lei], Zhang, L.M.[Li-Ming],
A Novel Multiresolution Spatiotemporal Saliency Detection Model and Its Applications in Image and Video Compression,
IP(19), No. 1, January 2010, pp. 185-198.
IEEE DOI 1001
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Guo, C.L.[Chen-Lei], Ma, Q.[Qi], Zhang, L.M.[Li-Ming],
Spatio-temporal Saliency detection using phase spectrum of quaternion fourier transform,
CVPR08(1-8).
IEEE DOI 0806
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Liu, Z.[Zhi], Xue, Y.[Yinzhu], Yan, H., Zhang, Z.Y.[Zhao-Yang],
Efficient saliency detection based on gaussian models,
IET-IPR(5), No. 2, April 2011, pp. 122-131.
DOI Link 1103
BibRef

Li, Y., Fu, K., Liu, Z., Yang, J.,
Efficient Saliency-Model-Guided Visual Co-Saliency Detection,
SPLetters(22), No. 5, May 2015, pp. 588-592.
IEEE DOI 1411
Computational modeling BibRef

Liu, Z.[Zhi], Xue, Y.[Yinzhu], Shen, L.Q.[Li-Quan], Zhang, Z.Y.[Zhao-Yang],
Nonparametric saliency detection using kernel density estimation,
ICIP10(253-256).
IEEE DOI 1009
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Song, Y.Z.[Yi-Zhe], Xiao, B.[Bai], Hall, P.M.[Peter M.], Wang, L.,
In Search of Perceptually Salient Groupings,
IP(20), No. 4, April 2011, pp. 935-947.
IEEE DOI 1103
BibRef

Xiao, B.[Bai], Song, Y.Z.[Yi-Zhe], Balika, A.[Anupriya], Hall, P.M.[Peter M.],
Structure Is a Visual Class Invariant,
SSPR08(329-338).
Springer DOI 0812
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Song, Y.Z.[Yi-Zhe], Hall, P.M.[Peter M.],
Stable Image Descriptions Using Gestalt Principles,
ISVC08(I: 318-327).
Springer DOI 0812
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Toet, A.,
Computational versus Psychophysical Bottom-Up Image Saliency: A Comparative Evaluation Study,
PAMI(33), No. 11, November 2011, pp. 2131-2146.
IEEE DOI 1110
Compare 13 models, plus Multiscale Contrast Conspicuity (MCC) metric, with Human experiments. Simple multiscale contrast model and the MCC metric both yield the largest correlation with human results. BibRef

Moreno, R.[Rodrigo], Garcia, M.A.[Miguel Angel], Puig, D.[Domenec], Pizarro, L., Burgeth, B., Weickert, J.,
On Improving the Efficiency of Tensor Voting,
PAMI(33), No. 11, November 2011, pp. 2215-2228.
IEEE DOI 1110
Introduce alternate computational formulations to reduce high computational cost. See also Tensor Voting: A Perceptual Organization Approach to Computer Vision and Machine Learning. See also Edge-preserving color image denoising through tensor voting. BibRef

Li, H.L.[Hong-Liang], Ngan, K.N.[King Ngi],
A Co-Saliency Model of Image Pairs,
IP(20), No. 12, December 2011, pp. 3365-3375.
IEEE DOI 1112
combine single image saliency and multi-image saliency maps. BibRef

Luo, W.[Wang], Li, H.L.[Hong-Liang], Liu, G.H.[Guang-Hui], Ngan, K.N.[King Ngi],
Global salient information maximization for saliency detection,
SP:IC(27), No. 3, March 2012, pp. 238-248.
Elsevier DOI 1203
PCA; Information maximization; Saliency detection BibRef

Hou, X.D.[Xiao-Di], Harel, J.[Jonathan], Koch, C.[Christof],
Image Signature: Highlighting Sparse Salient Regions,
PAMI(34), No. 1, January 2012, pp. 194-201.
IEEE DOI 1112
Compute signature approximates the foreground of an image. Predict human fixation points. BibRef

Hou, X.D.[Xiao-Di], Zhang, L.Q.[Li-Qing],
Saliency Detection: A Spectral Residual Approach,
CVPR07(1-8).
IEEE DOI 0706
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Fang, Y.M.[Yu-Ming], Lin, W.S.[Wei-Si], Lee, B.S., Lau, C.T., Chen, Z.Z.[Zhen-Zhong], Lin, C.W.,
Bottom-Up Saliency Detection Model Based on Human Visual Sensitivity and Amplitude Spectrum,
MultMed(14), No. 1, January 2012, pp. 187-198.
IEEE DOI 1201
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Fang, Y.M.[Yu-Ming], Chen, Z.Z.[Zhen-Zhong], Lin, W.S.[Wei-Si], Lin, C.W.[Chia-Wen],
Saliency Detection in the Compressed Domain for Adaptive Image Retargeting,
IP(21), No. 9, September 2012, pp. 3888-3901.
IEEE DOI 1208
BibRef

Fang, Y.M.[Yu-Ming], Lin, W.S.[Wei-Si], Chen, Z.Z.[Zhen-Zhong], Tsai, C.M.[Chia-Ming], Lin, C.W.[Chia-Wen],
A Video Saliency Detection Model in Compressed Domain,
CirSysVideo(24), No. 1, January 2014, pp. 27-38.
IEEE DOI 1402
data compression BibRef

Fang, Y.M.[Yu-Ming], Wang, Z.[Zhou], Lin, W.S.[Wei-Si], Fang, Z.J.[Zhi-Jun],
Video Saliency Incorporating Spatiotemporal Cues and Uncertainty Weighting,
IP(23), No. 9, September 2014, pp. 3910-3921.
IEEE DOI 1410
statistical analysis BibRef

Dong, L.[Lu], Lin, W.S.[Wei-Si], Fang, Y.M.[Yu-Ming], Wu, S.Q.[Shi-Qian], Seah, H.S.[Hock Soon],
Saliency detection in computer rendered images based on object-level contrast,
JVCIR(25), No. 3, 2014, pp. 525-533.
Elsevier DOI 1403
BibRef
Earlier:
Detection of salient objects in computer synthesized images based on object-level contrast,
VCIP13(1-6)
IEEE DOI 1402
Graphic saliency detection. feature extraction BibRef

Fang, Y.M.[Yu-Ming], Wang, J.[Junle], Narwaria, M., Le Callet, P., Lin, W.S.[Wei-Si],
Saliency Detection for Stereoscopic Images,
IP(23), No. 6, June 2014, pp. 2625-2636.
IEEE DOI 1406
BibRef
Earlier:
Saliency detection for stereoscopic images,
VCIP13(1-6)
IEEE DOI 1402
Computational modeling. Gaussian processes BibRef

Lang, C., Liu, G., Yu, J., Yan, S.,
Saliency Detection by Multitask Sparsity Pursuit,
IP(21), No. 3, March 2012, pp. 1327-1338.
IEEE DOI 1203
BibRef

Victor, J.D.[Jonathan D.], Conte, M.M.[Mary M.],
Local image statistics: Maximum-entropy constructions and perceptual salience,
JOSA-A(29), No. 7, July 2012, pp. 1313-1345.
WWW Link. 1208
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Emami, M.[Mohsen], Hoberock, L.L.[Lawrence L.],
Selection of a best metric and evaluation of bottom-up visual saliency models,
IVC(31), No. 10, 2013, pp. 796-808.
Elsevier DOI 1310
Bottom-up saliency mechanism. Which metric for particular model. BibRef

Rudi, A.[Alessandro], Odone, F.[Francesca], De Vito, E.[Ernesto],
Geometrical and computational aspects of Spectral Support Estimation for novelty detection,
PRL(36), No. 1, 2014, pp. 107-116.
Elsevier DOI 1312
Support estimation BibRef

Chuang, Y.L.[Yue-Long], Chen, L.[Ling], Chen, G.C.[Gen-Cai], Woodward, J.[John],
Isophote Based Center-Surround Contrast Computation for Image Saliency Detection,
IEICE(E97-D), No. 1, January 2013, pp. 160-163.
WWW Link. 1402
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Alsam, A.[Ali], Sharma, P.[Puneet],
Robust metric for the evaluation of visual saliency algorithms,
JOSA-A(31), No. 3, March 2014, pp. 532-540.
DOI Link 1403
Fourier optics and signal processing BibRef

Cheng, M.M.[Ming-Ming], Mitra, N.J.[Niloy J.], Huang, X.L.[Xiao-Lei], Hu, S.M.[Shi-Min],
SalientShape: group saliency in image collections,
VC(30), No. 4, April 2014, pp. 443-453.
WWW Link. 1404
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Tong, N., Lu, H.C.[Hu-Chuan], Zhang, L.[Lihe], Ruan, X.[Xiang],
Saliency Detection with Multi-Scale Superpixels,
SPLetters(21), No. 9, Sept 2014, pp. 1035-1039.
IEEE DOI 1406
Bayes methods BibRef

Tong, N.[Na], Lu, H.C.[Hu-Chuan], Zhang, Y.[Ying], Ruan, X.[Xiang],
Salient object detection via global and local cues,
PR(48), No. 10, 2015, pp. 3258-3267.
Elsevier DOI 1507
Visual saliency BibRef

Zhang, Y.[Ying], Lu, H.C.[Hu-Chuan], Zhang, L.[Lihe], Ruan, X.[Xiang],
Combining motion and appearance cues for anomaly detection,
PR(51), No. 1, 2016, pp. 443-452.
Elsevier DOI 1601
Anomaly detection BibRef

Zhang, Y.[Ying], Lu, H.C.[Hu-Chuan], Zhang, L.[Lihe], Ruan, X.[Xiang], Sakai, S.[Shun],
Video anomaly detection based on locality sensitive hashing filters,
PR(59), No. 1, 2016, pp. 302-311.
Elsevier DOI 1609
Anomaly detection BibRef

You, J.[Jia], Zhang, L.[Lihe], Qi, J.Q.[Jin-Qing], Lu, H.C.[Hu-Chuan],
Salient object detection via point-to-set metric learning,
PRL(84), No. 1, 2016, pp. 85-90.
Elsevier DOI 1612
Salient object detection BibRef

Tong, N.[Na], Lu, H.C.[Hu-Chuan], Ruan, X.[Xiang], Yang, M.H.[Ming-Hsuan],
Salient object detection via bootstrap learning,
CVPR15(1884-1892)
IEEE DOI 1510
BibRef

Huang, F., Qi, J., Lu, H., Zhang, L., Ruan, X.,
Salient Object Detection via Multiple Instance Learning,
IP(26), No. 4, April 2017, pp. 1911-1922.
IEEE DOI 1704
Computational modeling BibRef

Lu, H.C.[Hu-Chuan], Zhang, X., Qi, J., Tong, N., Ruan, X.[Xiang], Yang, M.H.[Ming-Hsuan],
Co-Bootstrapping Saliency,
IP(26), No. 1, January 2017, pp. 414-425.
IEEE DOI 1612
feature extraction BibRef

Lu, H.C.[Hu-Chuan], Li, X.H.[Xiao-Hui], Zhang, L.[Lihe], Ruan, X.[Xiang], Yang, M.H.[Ming-Hsuan],
Dense and Sparse Reconstruction Error Based Saliency Descriptor,
IP(25), No. 4, April 2016, pp. 1592-1603.
IEEE DOI 1604
BibRef
Earlier: A2, A1, A3, A4, A5:
Saliency Detection via Dense and Sparse Reconstruction,
ICCV13(2976-2983)
IEEE DOI 1403
Bayes methods BibRef

Zhang, L.[Lihe], Yang, C., Lu, H.C.[Hu-Chuan], Ruan, X.[Xiang], Yang, M.H.[Ming-Hsuan],
Ranking Saliency,
PAMI(39), No. 9, September 2017, pp. 1892-1904.
IEEE DOI 1708
Computational modeling, Electronic mail, Image color analysis, Image segmentation, Labeling, Manifolds, Visualization, Saliency detection, manifold ranking, multi-scale, graph BibRef

Jiang, B.[Bowen], Zhang, L.[Lihe], Lu, H.C.[Hu-Chuan], Yang, C.[Chuan], Yang, M.H.[Ming-Hsuan],
Saliency Detection via Absorbing Markov Chain,
ICCV13(1665-1672)
IEEE DOI 1403
absorbing Markov chain; object detection; saliency detection BibRef

Yang, C.[Chuan], Zhang, L.[Lihe], Lu, H.C.[Hu-Chuan], Ruan, X.[Xiang], Yang, M.H.[Ming-Hsuan],
Saliency Detection via Graph-Based Manifold Ranking,
CVPR13(3166-3173)
IEEE DOI 1309
BibRef

Zhang, C.[Chao], Li, X.[Xiong], Ruan, X.[Xiang], Zhao, Y.M.[Yu-Ming], Yang, M.H.[Ming-Hsuan],
Discriminative Generative Contour Detection,
BMVC13(xx-yy).
DOI Link 1402
BibRef
And: A1, A3, A4, A5, Only:
Contour detection via random forest,
ICPR12(2772-2775).
WWW Link. 1302
BibRef

Manipoonchelvi, P., Muneeswaran, K.,
Region-based saliency detection,
IET-IPR(8), No. 9, September 2014, pp. 519-527.
DOI Link 1410
image resolution BibRef

Maggiori, E., Lotito, P., Manterola, H.L., del Fresno, M.,
Comments on 'A Closed-Form Solution to Tensor Voting: Theory and Applications',
PAMI(36), No. 12, December 2014, pp. 2567-2568.
IEEE DOI 1411
Closed-form solutions See also Closed-Form Solution to Tensor Voting: Theory and Applications, A. BibRef

Xia, C.[Chen], Qi, F.[Fei], Shi, G.M.[Guang-Ming], Wang, P.J.[Peng-Jin],
Nonlocal Center-Surround Reconstruction-Based Bottom-Up Saliency Estimation,
PR(48), No. 4, 2015, pp. 1337-1348.
Elsevier DOI 1502
BibRef
Earlier: A1, A4, A2, A3: ICIP13(206-210)
IEEE DOI 1402
Saliency Compressed sensing BibRef

Zhang, X.J.[Xiu-Jun], Xu, C.[Chen], Li, M.[Min], Teng, R.K.F.[Robert K.F.],
Study of visual saliency detection via nonlocal anisotropic diffusion equation,
PR(48), No. 4, 2015, pp. 1315-1327.
Elsevier DOI 1502
Saliency detection BibRef

Filipe, S., Itti, L., Alexandre, L.A.,
BIK-BUS: Biologically Motivated 3D Keypoint Based on Bottom-Up Saliency,
IP(24), No. 1, January 2015, pp. 163-175.
IEEE DOI 1502
computational complexity BibRef

Luo, Y.K.[Yong-Kang], Wang, P.[Peng], Zhu, W.J.[Wen-Jun], Qiao, H.[Hong],
Sparse-Distinctive Saliency Detection,
SPLetters(22), No. 9, September 2015, pp. 1378-1382.
IEEE DOI 1503
feature extraction BibRef

Sen, D.[Debashis], Kankanhalli, M.[Mohan],
Salience computation in images based on perceptual distinctness,
SP:IC(32), No. 1, 2015, pp. 129-147.
Elsevier DOI 1503
Perceptual distinctness BibRef

Sen, D.[Debashis], Kankanhalli, M.[Mohan],
A bio-inspired center-surround model for salience computation in images,
JVCIR(30), No. 1, 2015, pp. 277-288.
Elsevier DOI 1507
Visual salience BibRef

Xu, M.[Min], Zhang, H.[Hanling],
Saliency detection with color contrast based on boundary information and neighbors,
VC(31), No. 3, March 2015, pp. 355-364.
WWW Link. 1503
BibRef

Rigas, I.[Ioannis], Economou, G.[George], Fotopoulos, S.[Spiros],
Efficient modeling of visual saliency based on local sparse representation and the use of hamming distance,
CVIU(134), No. 1, 2015, pp. 33-45.
Elsevier DOI 1504
Visual saliency BibRef

Maggiori, E., Manterola, H.L., del Fresno, M.,
Perceptual grouping by tensor voting: A comparative survey of recent approaches,
IET-CV(9), No. 2, 2015, pp. 259-277.
DOI Link 1506
computer vision BibRef

Guo, W.Z.[Wen-Zhong], Sun, X.[Xiaolong], Niu, Y.[Yuzhen],
Multi-scale saliency detection via inter-regional shortest colour path,
IET-CV(9), No. 2, 2015, pp. 290-299.
DOI Link 1506
image colour analysis BibRef

Wang, K.[Keze], Lin, L.[Liang], Lu, J.B.[Jiang-Bo], Li, C., Shi, K.[Keyang],
PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures With Edge-Preserving Coherence,
IP(24), No. 10, October 2015, pp. 3019-3033.
IEEE DOI 1507
BibRef
Earlier: A5, A1, A3, A2, Only:
PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures with Spatial Priors,
CVPR13(2115-2122)
IEEE DOI 1309
image processing. Coherence BibRef

Xu, L.F.[Lin-Feng], Zeng, L.Y.[Liao-Yuan], Duan, H.P.[Hui-Ping],
An effective vector model for global-contrast-based saliency detection,
JVCIR(30), No. 1, 2015, pp. 64-74.
Elsevier DOI 1507
Visual attention BibRef

Ma, X.L.[Xiao-Long], Xie, X.D.[Xu-Dong], Lam, K.M.[Kin-Man], Zhong, Y.S.[Yi-Sheng],
Efficient saliency analysis based on wavelet transform and entropy theory,
JVCIR(30), No. 1, 2015, pp. 201-207.
Elsevier DOI 1507
Saliency detection BibRef

Zhang, C.[Changqing], Tao, Z.Q.[Zhi-Qiang], Wei, X.[Xingxing], Cao, X.C.[Xiao-Chun],
A flexible framework of adaptive method selection for image saliency detection,
PRL(63), No. 1, 2015, pp. 66-70.
Elsevier DOI 1508
Saliency detection BibRef

Li, S.[Shuang], Lu, H.C.[Hu-Chuan], Lin, Z.[Zhe], Shen, X.H.[Xiao-Hui], Price, B.,
Adaptive Metric Learning for Saliency Detection,
IP(24), No. 11, November 2015, pp. 3321-3331.
IEEE DOI 1509
image coding BibRef

Ju, R.[Ran], Liu, Y.[Yang], Ren, T.[Tongwei], Ge, L.[Ling], Wu, G.S.[Gang-Shan],
Depth-aware salient object detection using anisotropic center-surround difference,
SP:IC(38), No. 1, 2015, pp. 115-126.
Elsevier DOI 1512
Salient object detection BibRef

Ju, R.[Ran], Ge, L.[Ling], Geng, W.[Wenjing], Ren, T.[Tongwei], Wu, G.S.[Gang-Shan],
Depth saliency based on anisotropic center-surround difference,
ICIP14(1115-1119)
IEEE DOI 1502
Color BibRef

Vilaplana, V.[Verónica],
Saliency maps on image hierarchies,
SP:IC(38), No. 1, 2015, pp. 84-99.
Elsevier DOI 1512
Region-based saliency map BibRef

Warnell, G.[Garrett], David, P.[Philip], Chellappa, R.[Rama],
Ray Saliency: Bottom-Up Visual Saliency for a Rotating and Zooming Camera,
IJCV(116), No. 2, January 2016, pp. 174-189.
Springer DOI 1602
Saliency with multiple cameras, requires consistency across views. Not just merging single view saliency. BibRef

Zhou, X., Liu, Z., Sun, G., Ye, L., Wang, X.,
Improving Saliency Detection Via Multiple Kernel Boosting and Adaptive Fusion,
SPLetters(23), No. 4, April 2016, pp. 517-521.
IEEE DOI 1604
Adaptation models BibRef

Dong, Y., Pourazad, M.T., Nasiopoulos, P.,
Human Visual System-Based Saliency Detection for High Dynamic Range Content,
MultMed(18), No. 4, April 2016, pp. 549-562.
IEEE DOI 1604
Computational modeling BibRef

Souly, N.[Nasim], Shah, M.[Mubarak],
Visual Saliency Detection Using Group Lasso Regularization in Videos of Natural Scenes,
IJCV(117), No. 1, March 2016, pp. 93-110.
Springer DOI 1604
BibRef

Zhao, T.[Tong], Li, L.[Lin], Ding, X.H.[Xing-Hao], Huang, Y.[Yue], Zeng, D.[Delu],
Saliency Detection With Spaces of Background-Based Distribution,
SPLetters(23), No. 5, May 2016, pp. 683-687.
IEEE DOI 1604
Bayes methods BibRef

Ge, C.J.[Chen-Jie], Fu, K.[Keren], Liu, F.H.[Fang-Hui], Bai, L.[Li], Yang, J.[Jie],
Co-saliency detection via inter and intra saliency propagation,
SP:IC(44), No. 1, 2016, pp. 69-83.
Elsevier DOI 1605
Co-saliency detection BibRef

Ge, C.J.[Chen-Jie], Fu, K.[Keren], Li, Y.J.[Yi-Jun], Yang, J.[Jie], Shi, P.F.[Peng-Fei], Bai, L.[Li],
Co-saliency detection via similarity-based saliency propagation,
ICIP15(1845-1849)
IEEE DOI 1512
Co-saliency detection BibRef

Chen, D.Y.[Dong-Yue], Jia, T.[Tong], Wu, C.D.[Cheng-Dong],
Visual saliency detection: From space to frequency,
SP:IC(44), No. 1, 2016, pp. 57-68.
Elsevier DOI 1605
Saliency detection BibRef

Li, G.B.[Guan-Bin], Yu, Y.Z.[Yi-Zhou],
Visual Saliency Detection Based on Multiscale Deep CNN Features,
IP(25), No. 11, November 2016, pp. 5012-5024.
IEEE DOI 1610
BibRef
And:
Deep Contrast Learning for Salient Object Detection,
CVPR16(478-487)
IEEE DOI 1612
BibRef
Earlier:
Visual saliency based on multiscale deep features,
CVPR15(5455-5463)
IEEE DOI 1510
feature extraction BibRef

Shafiee, M.J., Siva, P., Scharfenberger, C., Fieguth, P., Wong, A.,
NeRD: A Neural Response Divergence Approach to Visual Saliency Detection,
SPLetters(23), No. 10, October 2016, pp. 1404-1408.
IEEE DOI 1610
computer vision BibRef

Shafiee, M.J., Siva, P., Fieguth, P., Wong, A.,
Embedded Motion Detection via Neural Response Mixture Background Modeling,
ECVW16(837-844)
IEEE DOI 1612
BibRef

Qi, W.[Wei], Han, J.[Jing], Zhang, Y.[Yi], Bai, L.F.[Lian-Fa],
Graph-Boolean Map for salient object detection,
SP:IC(49), No. 1, 2016, pp. 9-16.
Elsevier DOI 1609
Saliency detection BibRef

Tang, H., Chen, C., Pei, X.,
Visual Saliency Detection via Sparse Residual and Outlier Detection,
SPLetters(23), No. 12, December 2016, pp. 1736-1740.
IEEE DOI 1612
image filtering BibRef

Deutsch, S.[Shay], Medioni, G.[Gérard],
Learning the Geometric Structure of Manifolds with Singularities Using the Tensor Voting Graph,
JMIV(57), No. 3, March 2017, pp. 402-422.
WWW Link. 1702
BibRef
Earlier:
Unsupervised Learning Using the Tensor Voting Graph,
SSVM15(282-293).
Springer DOI 1506
BibRef

Qi, W.[Wei], Han, J.[Jing], Zhang, Y.[Yi], Bai, L.[Lianfa],
Saliency detection via Boolean and foreground in a dynamic Bayesian framework,
VC(33), No. 2, February 2017, pp. 209-220.
WWW Link. 1702
BibRef

Huang, R., Feng, W., Sun, J.,
Color Feature Reinforcement for Cosaliency Detection Without Single Saliency Residuals,
SPLetters(24), No. 5, May 2017, pp. 569-573.
IEEE DOI 1704
feature extraction BibRef

Xiao, Y.[Yun], Wang, L.M.[Liang-Min], Jiang, B.[Bo], Tu, Z.Z.[Zheng-Zheng], Tang, J.[Jin],
A global and local consistent ranking model for image saliency computation,
JVCIR(46), No. 1, 2017, pp. 199-207.
Elsevier DOI 1706
Saliency, detection BibRef

Fu, K.[Keren], Gu, I.Y.H.[Irene Yu-Hua], Yang, J.[Jie],
Saliency Detection by Fully Learning a Continuous Conditional Random Field,
MultMed(19), No. 7, July 2017, pp. 1531-1544.
IEEE DOI 1706
Boosting, Estimation, Feature extraction, Image segmentation, Labeling, Object detection, Continuous conditional random field (C-CRF), feature integration, learning, saliency map, salient object detection, spatial, ranges BibRef

Zhang, L.B.[Li-Bao], Lv, X.R.[Xin-Ran], Liang, X.[Xu],
Saliency Analysis via Hyperparameter Sparse Representation and Energy Distribution Optimization for Remote Sensing Images,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Li, N.Y.[Nian-Yi], Ye, J.W.[Jin-Wei], Ji, Y.[Yu], Ling, H.B.[Hai-Bin], Yu, J.Y.[Jing-Yi],
Saliency Detection on Light Field,
PAMI(39), No. 8, August 2017, pp. 1605-1616.
IEEE DOI 1707
Cluttered backgrounds, similar foreground/background. Cameras, Databases, Image color analysis, Object detection, Robustness, Spatial resolution, Lytro, Saliency detection, focus stack, light field. BibRef

Li, N.Y.[Nian-Yi], Sun, B.[Bilin], Yu, J.Y.[Jing-Yi],
A weighted sparse coding framework for saliency detection,
CVPR15(5216-5223)
IEEE DOI 1510
BibRef

Dou, H., Ming, D., Yang, Z., Pan, Z., Li, Y., Tian, J.,
Object-Based Visual Saliency via Laplacian Regularized Kernel Regression,
MultMed(19), No. 8, August 2017, pp. 1718-1729.
IEEE DOI 1708
Biological system modeling, Computational modeling, Kernel, Laplace equations, Object detection, Visualization, Kernel regression, Laplacian regularized kernel regression (LKR), salient object detection, visual saliency BibRef

Chen, J.Z.[Jia-Zhong], Chen, J.[Jie], Cao, H.[Hua], Li, R.[Rong], Xia, T.[Tao], Ling, H.[Hefei], Chen, Y.[Yang],
Saliency detection using suitable variant of local and global consistency,
IET-CV(11), No. 6, September 2017, pp. 479-487.
DOI Link 1709
BibRef

Wang, Y.Y.[Yi-Yang], Liu, R.S.[Ri-Sheng], Song, X.L.[Xiao-Liang], Su, Z.X.[Zhi-Xun],
A nonlocal L0 model with regression predictor for saliency detection and extension,
VC(33), No. 11, November 2017, pp. 1467-1482.
WWW Link. 1710
BibRef

Aytekin, C.[Caglar], Iosifidis, A.[Alexandros], Gabbouj, M.[Moncef],
Probabilistic saliency estimation,
PR(74), No. 1, 2018, pp. 359-372.
Elsevier DOI 1711
Saliency BibRef

Aytekin, C.[Caglar], Ozan, E.C.[Ezgi Can], Kiranyaz, S.[Serkan], Gabbouj, M.[Moncef],
Visual saliency by extended quantum cuts,
ICIP15(1692-1696)
IEEE DOI 1512
Salience map generation. BibRef

Rabbani, N.[Navid], Nazari, B.[Behzad], Sadri, S.[Saeid], Rikhtehgaran, R.[Reyhaneh],
Efficient Bayesian approach to saliency detection based on Dirichlet process mixture,
IET-IPR(11), No. 11, November 2017, pp. 1103-1113.
DOI Link 1711
BibRef


Zhu, C.B.[Chun-Biao], Li, G.[Ge], Guo, X.Q.[Xiao-Qiang], Wang, W.M.[Wen-Min], Wang, R.G.[Rong-Gang],
A Multilayer Backpropagation Saliency Detection Algorithm Based on Depth Mining,
CAIP17(II: 14-23).
Springer DOI 1708
BibRef

Li, M.[Meng], Liu, X.[Xing], Tang, L.M.[Li-Ming],
A Phase Field Variational Model with Arctangent Regularization for Saliency Detection,
SoftBio17(29-35)
IEEE DOI 1609
feature extraction, variational techniques, visual perception, arctangent regularization, classical Cahn-Hilliard model, complex image domain, dynamical competition, energy functional minimization, highly anisotropic interfacial energy, human visual perception, phase field variational model, saliency detection, visual attention feature extraction, Computational modeling, Conferences, Feature extraction, Mathematical model, Visual systems, Visualization BibRef

Cornia, M., Baraldi, L., Serra, G., Cucchiara, R.,
A deep multi-level network for saliency prediction,
ICPR16(3488-3493)
IEEE DOI 1705
Benchmark testing, Computer architecture, Convolutional codes, Encoding, Feature extraction, Measurement, Observers BibRef

Jetley, S.[Saumya], Murray, N.[Naila], Vig, E.[Eleonora],
End-to-End Saliency Mapping via Probability Distribution Prediction,
CVPR16(5753-5761)
IEEE DOI 1612
BibRef

Pan, J., Sayrol, E., Giró-i-Nieto, X.[Xavier], McGuinness, K.[Kevin], O'Connor, N.E.,
Shallow and Deep Convolutional Networks for Saliency Prediction,
CVPR16(598-606)
IEEE DOI 1612
BibRef

Salvador, A., Giró-i-Nieto, X.[Xavier], Marqués, F.[Ferran],
Faster R-CNN Features for Instance Search,
DeepLearn-C16(394-401)
IEEE DOI 1612
BibRef

Cornia, M.[Marcella], Baraldi, L.[Lorenzo], Serra, G.[Giuseppe], Cucchiara, R.[Rita],
Multi-level Net: A Visual Saliency Prediction Model,
ACVR16(II: 302-315).
Springer DOI 1611
BibRef

Liu, H., Tao, S., Li, Z.,
Saliency detection via global-object-seed-guided cellular automata,
ICIP16(2772-2776)
IEEE DOI 1610
Automata BibRef

Marighetto, P., Abdelkader, I.H., Duzelier, S., Décombas, M., Riche, N., Jakubowicz, J., Mancas, M., Gosselin, B., Laganière, R.,
FUNNRAR: Hybrid rarity/learning visual saliency,
ICIP16(2782-2786)
IEEE DOI 1610
Artificial neural networks BibRef

Zhang, L., Sun, Q., Chen, J.,
Multi-image saliency analysis via histogram and spectral feature clustering for satellite images,
ICIP16(2802-2806)
IEEE DOI 1610
Histograms;Image processing;clustering;saliency BibRef

Martinez-Rodriguez, D.E.[Diana E.], Ayala-Ramirez, V.[Victor], Hernandez-Belmonte, U.H.[Uriel H.],
Saliency Detection Based on Heuristic Rules,
MCPR16(94-103).
Springer DOI 1608
BibRef

Xu, F.[Fei], Xian, M.[Min], Cheng, H.D., Ding, J.R.[Jian-Rui], Zhang, Y.T.[Ying-Tao],
Unsupervised saliency estimation based on robust hypotheses,
WACV16(1-6)
IEEE DOI 1606
Adaptation models BibRef

Tasse, F.P., Kosinka, J., Dodgson, N.,
Cluster-Based Point Set Saliency,
ICCV15(163-171)
IEEE DOI 1602
Computational modeling BibRef

Li, J., Xia, C., Song, Y., Fang, S., Chen, X.,
A Data-Driven Metric for Comprehensive Evaluation of Saliency Models,
ICCV15(190-198)
IEEE DOI 1602
Benchmark testing BibRef

Huang, X., Shen, C., Boix, X., Zhao, Q.,
SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks,
ICCV15(262-270)
IEEE DOI 1602
Computational modeling BibRef

Zeng, Y., Xu, Y.,
Saliency Detection Using Quaternion Sparse Reconstruction,
ACVR15(469-476)
IEEE DOI 1602
Color BibRef

Schauerte, B.[Boris], Wortwein, T.[Torsten], Stiefelhagen, R.[Rainer],
Color decorrelation helps visual saliency detection,
ICIP15(1965-1969)
IEEE DOI 1512
BibRef

Greenberg, S., Chung, A.G., Chwyl, B., Wong, A.,
TIGGER: A Texture-Illumination Guided Global Energy Response Model for Illumination Robust Object Saliency,
CRV16(296-302)
IEEE DOI 1612
Bayesian estimation BibRef

Chwyl, B., Chung, A.G., Li, F.Y., Wong, A., Clausi, D.A.,
TIGER: A texture-illumination guided energy response model for illumination robust local saliency,
ICIP15(1970-1974)
IEEE DOI 1512
Bayesian estimation BibRef

Zhang, H.[Hui], Zhang, J.F.[Jin-Fang], Xu, F.J.[Fan-Jiang],
Land use and land cover classification base on image saliency map cooperated coding,
ICIP15(2616-2620)
IEEE DOI 1512
Bag-of-Words Model BibRef

Liu, Y.[Yaqi], Cai, Q.A.[Qi-Ang], Zhu, X.B.[Xiao-Bin], Cao, J.[Jian], Li, H.S.[Hai-Sheng],
Saliency detection using two-stage scoring,
ICIP15(4062-4066)
IEEE DOI 1512
Saliency detection; manifold ranking; random walk; two-stage scoring BibRef

Zhang, D.W.[Ding-Wen], Han, J.W.[Jun-Wei], Li, C.[Chao], Wang, J.D.[Jing-Dong],
Co-saliency detection via looking deep and wide,
CVPR15(2994-3002)
IEEE DOI 1510
BibRef

Wang, L.J.[Li-Jun], Lu, H.C.[Hu-Chuan], Ruan, X.[Xiang], Yang, M.H.[Ming-Hsuan],
Deep networks for saliency detection via local estimation and global search,
CVPR15(3183-3192)
IEEE DOI 1510
BibRef

Park, H.S.[Hyun Soo], Shi, J.B.[Jian-Bo],
Social saliency prediction,
CVPR15(4777-4785)
IEEE DOI 1510
BibRef

Luo, Y.[Yan], Wong, Y.[Yongkang], Zhao, Q.[Qi],
Label Consistent Quadratic Surrogate model for visual saliency prediction,
CVPR15(5060-5069)
IEEE DOI 1510
BibRef

Qin, Y.[Yao], Lu, H.C.[Hu-Chuan], Xu, Y.Q.[Yi-Qun], Wang, H.[He],
Saliency detection via Cellular Automata,
CVPR15(110-119)
IEEE DOI 1510
BibRef

Le Meur, O.[Olivier], Liu, Z.[Zhi],
Saliency Aggregation: Does Unity Make Strength?,
ACCV14(IV: 18-32).
Springer DOI 1504
Does aggregation do better then good saliency maps. BibRef

Zhao, B.[Bin], Delp, E.J.[Edward J.],
Visual Saliency Models Based on Spectrum Processing,
WACV15(976-981)
IEEE DOI 1503
Computational modeling. Frequency domain analysis. BibRef

Qi, S.X.[Sheng-Xiang], Yu, J.G.[Jin-Gang], Zhao, J.[Ji], Ma, J.[Jie], Tian, J.W.[Jin-Wen],
Visual saliency detection using feature activity weighted decorrelation cues,
ICIP14(1140-1144)
IEEE DOI 1502
Decorrelation BibRef

Stefic, D.[Daria], Patras, I.[Ioannis],
Action recognition using saliency learned from recorded human gaze,
IVC(52), No. 1, 2016, pp. 195-205.
Elsevier DOI 1609
BibRef
Earlier:
Learning visual saliency using topographic independent component analysis,
ICIP14(1130-1134)
IEEE DOI 1502
Action recognition. Face BibRef

Khatoonabadi, S.H.[Sayed Hossein], Bajic, I.V.[Ivan V.], Shan, Y.F.[Yu-Feng],
Comparison of visual saliency models for compressed video,
ICIP14(1081-1085)
IEEE DOI 1502
Computational modeling BibRef

Altamirano-Gómez, G.E.[Gerardo E.], Bayro-Corrochano, E.[Eduardo],
Conformal Geometric Algebra method for detection of geometric primitives,
ICPR16(4190-4195)
IEEE DOI 1705
BibRef
Earlier:
Conformal Geometric Method for Voting,
CIARP14(802-809).
Springer DOI 1411
Extension of Hough or tensor voting. Algebra, Clustering algorithms, Data mining, Feature extraction, Organizations, Silicon, Tensile stress. BibRef

Fathalla, R.[Radwa], Vogiatzis, G.[George],
Detection of multiple meaningful primitive geometric models,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Kocak, A.[Aysun], Cizmeciler, K.[Kemal], Erdem, A.[Aykut], Erdem, E.[Erkut],
Top down saliency estimation via superpixel-based discriminative dictionaries,
BMVC14(xx-yy).
HTML Version. 1410
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Liu, R.S.[Ri-Sheng], Cao, J.J.[Jun-Jie], Lin, Z.C.[Zhou-Chen], Shan, S.G.[Shi-Guang],
Adaptive Partial Differential Equation Learning for Visual Saliency Detection,
CVPR14(3866-3873)
IEEE DOI 1409
Learning-Based PDEs; Saliency Detection; Submodular Optimization BibRef

Vig, E.[Eleonora], Dorr, M.[Michael], Cox, D.[David],
Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images,
CVPR14(2798-2805)
IEEE DOI 1409
deep learning; hyperparameter optimization; saliency BibRef

Yan, X.Y.[Xiao-Yun], Wang, Y.H.[Yue-Huan], Song, M.M.[Meng-Meng], Jiang, M.[Man],
Saliency Detection Using Color Spatial Variance Weighted Graph Model,
ACPR13(410-414)
IEEE DOI 1408
computer vision BibRef

Gupta, S., Agrawal, R., Layek, R., Mukhopadhyay, J.,
Psychovisual saliency in color images,
NCVPRIPG13(1-4)
IEEE DOI 1408
computer vision BibRef

Wu, J.[Jie], Zhang, L.Q.[Li-Qing],
Gestalt saliency: Salient region detection based on Gestalt principles,
ICIP13(181-185)
IEEE DOI 1402
Computer vision BibRef

Imamoglu, N.[Nevrez], Fang, Y.M.[Yu-Ming], Yu, W.[Wenwei], Lin, W.S.[Wei-Si],
2D mel-cepstrum based saliency detection,
ICIP13(236-239)
IEEE DOI 1402
Biological system modeling BibRef

Pan, J.[Jinshan], Su, Z.[Zhixun], Bian, M.[Maoran], Liu, R.[Risheng],
Saliency detection based on an edge-preserving filter,
ICIP13(1757-1761)
IEEE DOI 1402
Bayesian framework;Saliency map;edge-preserving filter;image matting BibRef

Zhou, Q.[Quan], Chen, J.[Ji], Ren, S.[Shiwei], Zhou, Y.[Yu], Chen, J.[Jun], Liu, W.Y.[Wen-Yu],
On contrast combinations for visual saliency detection,
ICIP13(2665-2669)
IEEE DOI 1402
Saliency detection BibRef

Zhang, L.[Lin], Gu, Z.Y.[Zhong-Yi], Li, H.Y.[Hong-Yu],
SDSP: A novel saliency detection method by combining simple priors,
ICIP13(171-175)
IEEE DOI 1402
Accuracy BibRef

Shtrom, E.[Elizabeth], Leifman, G.[George], Tal, A.[Ayellet],
Saliency Detection in Large Point Sets,
ICCV13(3591-3598)
IEEE DOI 1403
Point sets; Saliency; Visual saliency BibRef

Margolin, R.[Ran], Tal, A.[Ayellet], Zelnik-Manor, L.[Lihi],
What Makes a Patch Distinct?,
CVPR13(1139-1146)
IEEE DOI 1309
distinctness; saliency; salient object BibRef

Mai, L.[Long], Niu, Y.Z.[Yu-Zhen], Liu, F.[Feng],
Saliency Aggregation: A Data-Driven Approach,
CVPR13(1131-1138)
IEEE DOI 1309
BibRef

Zhou, Z.[Zhen], Huang, Y.Z.[Yong-Zhen], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
Depth-embedded multiple pooling for image classification,
ICIP13(4335-4339)
IEEE DOI 1402
Depth Estimation; Image Classification; Multiple Pooling BibRef

Wu, Z.F.[Zi-Feng], Huang, Y.Z.[Yong-Zhen], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
Group encoding of local features in image classification,
ICPR12(1505-1508).
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Zhou, Q.[Quan], Li, N.[Nianyi], Yang, Y.[Yi], Chen, P.[Pan], Liu, W.Y.[Wen-Yu],
Corner-surround Contrast for saliency detection,
ICPR12(1423-1426).
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Zhou, Y.[Yue], Shi, K.[Kun],
Spatiotemporal saliency based on distributed opponent oriented energy,
ICPR12(2021-2024).
WWW Link. 1302
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Zhang, C.[Chi], Wang, W.Q.[Wei-Qiang],
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ICIP13(2475-2479)
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saliency detection BibRef

Zhang, H.[Hui], Wang, W.Q.[Wei-Qiang], Su, G.P.[Gui-Ping], Duan, L.J.[Li-Juan],
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Song, R.[Ran], Liu, Y.H.[Yong-Huai], Zhao, Y.[Yitian], Martin, R.R.[Ralph R.], Rosin, P.L.[Paul L.],
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ICIP12(637-640).
IEEE DOI 1302
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Yeh, H.H.[Hsin-Ho], Chen, C.S.[Chu-Song],
From rareness to compactness: Contrast-aware image saliency detection,
ICIP12(1077-1080).
IEEE DOI 1302
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Yang, W.B.[Wei-Bin], Fang, B.[Bin], Tang, Y.Y.[Yuan Yan], Shang, Z.W.[Zhao-Wei], Zhao, H.J.[Heng-Jun],
Visual saliency estimation using support value transform,
ICIP12(1069-1072).
IEEE DOI 1302
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Narayanan, M.[Maruthi], Kimia, B.B.[Benjamin B.],
Bottom-Up Perceptual Organization of Images into Object Part Hypotheses,
ECCV12(I: 257-271).
Springer DOI 1210
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Sharma, G.[Gaurav], Jurie, F.[Frederic], Schmid, C.[Cordelia],
Discriminative spatial saliency for image classification,
CVPR12(3506-3513).
IEEE DOI 1208
Where is the object. BibRef

Gong, D.[Dian], Medioni, G.[Gerard],
Probabilistic tensor voting for robust perceptual grouping,
POCV12(1-8).
IEEE DOI 1207
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Attention-driven segmentation of cluttered 3D scenes,
ICPR12(3610-3613).
WWW Link. 1302
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Earlier:
Learning What Matters: Combining Probabilistic Models of 2D and 3D Saliency Cues,
CVS11(132-142).
Springer DOI 1109
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Schiffner, D.[Daniel], Kromker, D.[Detlef],
Three Dimensional Saliency Calculation Using Splatting,
ICIG11(835-840).
IEEE DOI 1109
BibRef

Wang, M.[Meng], Konrad, J.[Janusz], Ishwar, P.[Prakash], Jing, K.[Kevin], Rowley, H.[Henry],
Image saliency: From intrinsic to extrinsic context,
CVPR11(417-424).
IEEE DOI 1106
BibRef

Murray, N.[Naila], Vanrell, M.[Maria], Otazu, X.[Xavier], Parraga, C.A.[C. Alejandro],
Low-Level Spatiochromatic Grouping for Saliency Estimation,
PAMI(35), No. 11, 2013, pp. 2810-2816.
IEEE DOI 1309
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CVPR11(433-440).
IEEE DOI 1106
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Aziz, M.Z.[M. Zaheer], Knopf, M.[Michael], Mertsching, B.[Bärbel],
Knowledge-Driven Saliency: Attention to the Unseen,
ACIVS11(34-45).
Springer DOI 1108
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Vikram, T.N.[Tadmeri Narayan], Tscherepanow, M.[Marko], Wrede, B.[Britta],
A Visual Saliency Map Based on Random Sub-window Means,
IbPRIA11(33-40).
Springer DOI 1106
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Li, X.[Xue], Yao, H.X.[Hong-Xun], Sun, X.S.[Xiao-Shuai], Ji, R.R.[Rong-Rong], Liu, X.M.[Xian-Ming], Xu, P.F.[Peng-Fei],
Sparse representation based visual element analysis,
ICIP11(657-660).
IEEE DOI 1201
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Sun, X.S.[Xiao-Shuai], Yao, H.X.[Hong-Xun], Ji, R.R.[Rong-Rong], Xu, P.F.[Peng-Fei], Liu, X.M.[Xian-Ming], Liu, S.H.[Shao-Hui],
Saliency detection based on short-term sparse representation,
ICIP10(1101-1104).
IEEE DOI 1009
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Zhao, C.R.[Cai-Rong], Liu, C.C.[Chuan-Cai], Lai, Z.H.[Zhi-Hui], Yang, J.Y.[Jing-Yu],
Sparse Embedding Visual Attention Systems Combined with Edge Information,
ICPR10(3432-3435).
IEEE DOI 1008
BibRef

Huang, R.[Rui], Sang, N.[Nong], Liu, L.Y.[Le-Yuan], Tang, Q.L.[Qi-Ling],
Saliency Based on Multi-scale Ratio of Dissimilarity,
ICPR10(13-16).
IEEE DOI 1008
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Hong, Y.[Yi], Jiang, J.Y.[Jia-Yan], Tu, Z.W.[Zhuo-Wen],
Sparse semi-supervised learning for perceptual grouping,
POCV10(1-8).
IEEE DOI 1006
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Ngau, C.W.H.[Christopher Wing Hong], Ang, L.M.[Li-Minn], Seng, K.P.[Kah Phooi],
Low Memory Implementation of Saliency Map Using Strip-Based Method,
IVIC09(715-726).
Springer DOI 0911
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Valenti, R.[Roberto], Sebe, N.[Nicu], Gevers, T.[Theo],
Image Saliency by Isocentric Curvedness and Color,
ICCV09(2185-2192).
IEEE DOI
PDF File. 0909
BibRef
Earlier:
Isocentric color saliency in images,
ICIP09(993-996).
IEEE DOI 0911
BibRef

Michaelsen, E.[Eckart], Middelmann, W.[Wolfgang], Sörgel, U.[Uwe],
Cognitive Vision and Perceptual Grouping by Production Systems with Blackboard Control: An Example for High-Resolution SAR-Images,
VISAPP06(293-304).
Springer DOI 0711
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Lombardi, G.[Gabriele], Casiraghi, E.[Elena], Campadelli, P.[Paola],
Curvature Estimation and Curve Inference with Tensor Voting: A New Approach,
ACIVS08(xx-yy).
Springer DOI 0810
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Campadelli, P.[Paola], Lombardi, G.[Gabriele],
Tensor Voting Fields: Direct Votes Computation and New Saliency Functions,
CIAP07(677-684).
IEEE DOI 0709
BibRef

Syeda-Mahmood, T.[Tanveer], Wang, F.[Fei],
Unsupervised Clustering using Multi-Resolution Perceptual Grouping,
CVPR07(1-8).
IEEE DOI 0706
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Liu, Y.[Yang], Bouganis, C.S., Cheung, P.Y.K.,
A Spatiotemporal Saliency Framework,
ICIP06(437-440).
IEEE DOI 0610
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Orabona, F.[Francesco], Metta, G.[Giorgio], Sandini, G.[Giulio],
Learning Association Fields from Natural Images,
PercOrg06(174).
IEEE DOI 0609
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Govindu, V.M.[Venu Madhav], Layout, S.[Simhapuri],
A Tensor Decomposition for Geometric Grouping and Segmentation,
CVPR05(I: 1150-1157).
IEEE DOI 0507
Apply method to salient feature grouping and motion segmentation. BibRef

Driancourt, R.[Remi],
Learning Perceptual Organization with a Developmental Robot,
PercOrg04(60).
IEEE DOI 0502
BibRef

Arsenio, A.M.[Artur M.],
An Embodied Approach to Perceptual Grouping,
PercOrg04(51).
IEEE DOI 0502
BibRef

Engbers, E.A.[Erik A.], Lindenbaum, M.[Michael], Smeulders, A.W.M.[Arnold W.M.],
An Information-Based Measure for Grouping Quality,
ECCV04(Vol III: 392-404).
Springer DOI 0405
BibRef

Massad, A.,
A Perceptual Grouping Approach for Visual Interpolation between Good Continuation and Minimal Path using Tensor Voting,
BMVC06(II:639).
PDF File. 0609
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Aziz, M.Z.[Muhammad Zaheer], Mertsching, B.[Bärbel],
An Attentional Approach for Perceptual Grouping of Spatially Distributed Patterns,
DAGM07(345-354).
Springer DOI 0709
BibRef

Massad, A., Babós, M., Mertsching, B.[Bärbel],
Application of the Tensor Voting Technique for Perceptual Grouping to Grey-Level Images,
DAGM02(306 ff.).
Springer DOI 0303
BibRef

Malik, J.,
Visual grouping and object recognition,
CIAP01(612-621).
WWW Link. 0210
BibRef

Yu, S.X.[Stella X.], Zhang, H.[Hao], Malik, J.[Jitendra],
Inferring spatial layout from a single image via depth-ordered grouping,
Tensor08(1-7).
IEEE DOI 0806
BibRef

Yu, S.X.[Stella X.],
Segmentation Induced by Scale Invariance,
CVPR05(I: 444-451).
IEEE DOI 0507
BibRef
Earlier:
Segmentation using multiscale cues,
CVPR04(I: 247-254).
IEEE DOI 0408
handle texture and contours through scales. BibRef

Yu, S.X.[Stella X.], and Shi, J.B.[Jian-Bo],
Understanding Popout through Repulsion,
CVPR01(II:752-757).
IEEE DOI 0110
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And:
Understanding Popout: Pre-attentive Segmentation through Nondirectional Repulsion,
CMU-RI-TR-01-20, July, 2001.
PDF File. 0205
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And:
Perceiving Shapes through Region and Boundary Interaction,
CMU-RI-TR-01-21, July, 2001.
PDF File. 0205
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Mahoney, J.V.[James V.], Fromherz, M.P.J.[Markus P.J.],
Perceptual organization as graph rectification in a constraint-based scheme for interpreting sloppy stick figures,
PercOrg01(xx-yy). 0106
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Marques, J.S.[Jorge S.], Abrantes, A.J.[Arnaldo J.],
A Constrained Clustering Algorithm for Shape Analysis with Multiple Features,
ICPR00(Vol I: 916-919).
IEEE DOI 0009
BibRef

Ambrosio, G.[Gregorio], González, J.[Javier],
Extracting and Matching Perceptual Groups for Hierarchical Stereo Vision,
ICPR00(Vol I: 542-545).
IEEE DOI 0009
BibRef

Marichal, X.[Xavier], Delmot, T., de Vleeschouwer, C., Warscotte, V., Macq, B.,
Automatic Detection of Interest Areas of an Image or of a Sequence of Images,
ICIP96(III: 371-374).
IEEE DOI Saliency. Find salient regions in video. BibRef 9600

Sara, R.[Radim], and Bajcsy, R.[Ruzena],
Fish-Scales: Representing Fuzzy Manifolds,
ICCV98(811-817).
IEEE DOI BibRef 9800

Borra, S., Sarkar, S.,
Experimental Performance Evaluation of Feature Grouping Modules,
CVPR97(891-896).
IEEE DOI 9704
BibRef

Serra, J.R., Subirana-Vilanova, J.B.,
Perceptual grouping on texture images using non-cartesian networks,
ICPR96(II: 462-466).
IEEE DOI 9608
(Univ. Autonoma Barcelona, E) BibRef

Subirana, B.[Brian],
Perceptual Organization, Figure Ground, Attention And Saliency,
MIT AI Memo-1218, August 1991. BibRef 9108

Lawton, D.T., McConnell, C.C.,
Perceptual Organization Using Interestingness,
SRMSF87(405-419). BibRef 8700

Dabis, H.S., Palmer, P.L., Kittler, J.V.,
An Interest Operator Based on Perceptual Grouping,
SCIA95(315-322). BibRef 9500

Wang, C.L., Prasanna, V.K., Chung, Y.,
Parallel Implementations of Perceptual Grouping Tasks on Distributed Memory Machines,
ARPA96(905-912). BibRef 9600

Fellenz, W.A., Hartmann, G.,
Preattentive Grouping and Attentive Selection for Early Visual Computation,
ICPR96(IV: 340-345).
IEEE DOI 9608
(Univ. of Paderborn, D) BibRef

Kang, H.B., Walker, E.L.,
Multilevel Grouping: Combining Bottom-Up and Top-Down Reasoning for Object Recognition,
ICPR94(A:559-562).
IEEE DOI BibRef 9400

Derou, D.[Dominique], Herault, L.[Laurent],
Pulsed neural networks and perceptive grouping,
ECCV94(A:521-526).
Springer DOI 9405
BibRef

Horaud, R., Veilon, F., and Skordas, T.,
Finding Geometric and Relational Structures in an Image,
ECCV90(374-384).
Springer DOI Group simple features into more comples structures. BibRef 9000

Subirana-Vilanova, J.B., and Sung, K.K.[Kah Kay],
Multi-Scale Vector-Ridge-Detection for Perceptual Organization Without Edges,
ICCV93(57-64).
IEEE DOI BibRef 9300
And: MIT AI Memo-1318, December 1992.
WWW Link. BibRef
Earlier:
Perceptual Organization without Edges,
DARPA92(289-298). Grouping using regions and using color for grouping. BibRef

Subirana-Vilanova, J.B.,
The Skeleton Sketch: Finding Salient Frames of Reference,
DARPA90(614-622). BibRef 9000

Subirana-Vilanova, J.B.,
Curved Inertia Frames and the Skeleton Sketch: Finding Salient Frames of Reference,
ICCV90(702-708).
IEEE DOI BibRef 9000

Abella, A.,
Extracting Geometric Shapes from a Set of Points,
DARPA92(573-583). Grouping applied to points. BibRef 9200

Ahmad, S.,
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ICSITR-91-049, Berkeley, CA, 1991, BibRef 9100 Ph.D.Thesis (UofIll). BibRef

Chapter on Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar continues in
Grouping, Lines and Curves .


Last update:Dec 7, 2017 at 17:23:10