4.4 Scale Space and Multi-Scale Techniques

4.4.1 Scale Space for Descriptions

Witkin, A.P.[Andrew P.],
Scale Space Filtering: A New Approach to Multi-Scale Descriptions,
IU84(79-95). BibRef 8400
And: ICASSP84(150-153).
IEEE DOI BibRef
And:
Scale-Space Filtering,
IJCAI83(1019-1022). Reprinted in BibRef RCV87(329-332). Award, IJCAI. (Award winner at IJCAI for the best paper.) Filter at different resolutions and use the data from all the levels. Introduced scale space to vision. BibRef

Saund, E.,
Symbolic Construction of a 2-D Scale-Space Image,
PAMI(12), No. 8, August 1990, pp. 817-830.
IEEE DOI BibRef 9008
Earlier: MIT AI Memo-1028, April 1988, BibRef
Earlier:
Adding Scale to the Primal Sketch,
CVPR89(70-78).
IEEE DOI Generate a set of line segments at different resolutions (Scale-Space). Apply grouping techniques. BibRef

Hummel, R.,
The Scale-Space Formulation of Pyramid Data Structures,
PCV88(107-123). BibRef 8800

Dyer, C.R.,
Multiscale Image Understanding,
PCV88(171-213). BibRef 8800

Unser, M., and Eden, M.,
The L-(2) Polynomial Spline Pyramid,
PAMI(15), No. 4, April 1993, pp. 364-379.
IEEE DOI Representation, Pyramid. BibRef 9304

Dron, L.[Lisa],
The Multiscale Veto Model: A 2-Stage Analog Network for Edge-Detection and Image-Reconstruction,
IJCV(11), No. 1, August 1993, pp. 45-61.
Springer DOI BibRef 9308

Bookstein, F.L., Green, W.D.K.,
A Feature Space for Edgels in Images with Landmarks,
JMIV(3), No. 4, 1993, pp. 231-261. BibRef 9300

Johansen, P.,
On the Classification of Top Points in Scale Space,
JMIV(4), 1994, pp. 57-67. BibRef 9400

Ray, B.K., Ray, K.S.,
A New Approach to Scale-Space Image,
PRL(15), No. 4, April 1994, pp. 365-372. BibRef 9404

Pei, S.C.[Soo-Chang], Chen, F.C.[Fei-Chin],
3-D spatiotemporal subband decompositions for hierarchical compatible video coding by mathematical morphology,
SP:IC(6), No. 1, March 1994, pp. 83-99.
Elsevier DOI 0001
Subband decomposition; Video coding; Morphological filtering BibRef

Pei, S.C.[Soo-Chang], Chen, F.C.[Fei-Chin],
Hierarchical Image Representation by Mathematical Morphology Sub-Band Decomposition,
PRL(16), No. 2, February 1995, pp. 183-192. BibRef 9502

Bijaoui, A., Rue, F.,
A Multiscale Vision Model Adapted to the Astronomical Images,
SP(46), No. 3, October 1995, pp. 345-362. BibRef 9510

Ahuja, N.,
On Detection and Representation of Multiscale Low-Level Image Structure,
Surveys(27), No. 3, September 1995, pp. 304-306. Survey, Scale-Space. BibRef 9509

Soille, P., and Rivest, J.F.,
On the Validity of Fractal Dimension Measurements in Image Analysis,
JVCIR(7), 1996, pp. 217-229. BibRef 9600

Rivest, J.F., Serra, J., Soille, P.,
Dimensionality in Image Analysis,
JVCIR(3), 1992, pp. 137-146. BibRef 9200

Dudek, G.[Gregory],
Shape Description and Classification Using the Interrelationship of Structures at Multiple Scales,
MDSG94(473-492). BibRef 9400
And:
Shape Metrics from Curvature-Scale Space and Curvature-Tuned Smoothing,
SPIE(1570), 1991, pp. 75-85. BibRef

Hong, L., Cao, J., Chen, G.R.,
A Multiresolutional Approach to 3D Object Recognition,
CSSP(16), No. 2, 1997, pp. 217-239. 9703
BibRef

Griffin, L.D.[Lewis D.],
Scale-Imprecision Space,
IVC(15), No. 5, May 1997, pp. 369-398.
Elsevier DOI 9706
BibRef

Cong, G.[Ge], Ma, S.D.[Song De],
Dyadic Scale-Space,
PR(30), No. 2, February 1997, pp. 339-352. 9704
BibRef
Earlier:
Elsevier DOI ICPR96(II: 399-402).
IEEE DOI 9608
(Chinese Academy of Sciences, PRC) BibRef

Cong, G.[Ge], Ma, S.D.[Song De],
Corner Enhancement in Curvature Scale Space,
PR(31), No. 10, October 1998, pp. 1491-1501.
Elsevier DOI 9808
BibRef

Guichard, F.,
A Morphological, Affine, and Galilean Invariant Scale-Space for Movies,
IP(7), No. 3, March 1998, pp. 444-456.
IEEE DOI 9803
BibRef

Solina, F.[Franc], Leonardis, A.[Aleš],
Proper Scale for Modeling Visual Data,
IVC(16), No. 2, February 20 1998, pp. 89-98.
Elsevier DOI 9803
BibRef

Chellappa, R., Girod, B., Munson, D.C., Tekalp, M., Vetterli, M.,
The Past, Present, and Future of Image and Multidimensional Signal-Processing,
SPMag(15), No. 2, March 1998, pp. 21-58. 9804
BibRef

Fdez Valdivia, J., Garcia, J.A., Martinez Baena, J., Fdez Vidal, X.R.[Xose R.],
The Selection of Natural Scales in 2D Images Using Adaptive Gabor Filtering,
PAMI(20), No. 5, May 1998, pp. 458-469.
IEEE DOI 9806
BibRef

Martinez Baena, J., Garcia, J.A., Fdez Valdivia, J., Rodriguez Sanchez, R.,
Scale Selection Using 3 Different Representations For Images,
PRL(18), No. 14, December 1997, pp. 1453-1467. 9806
BibRef
Earlier: A2, A3, A1, Only:
Scale Selection Using Three Different Representations for Images,
ScaleSpace97(xx). 9702
BibRef

Combettes, P.L.[Patrick L.],
Convex Set-Theoretic Image Recovery by Extrapolated Iterations of Parallel Subgradient Projections,
IP(6), No. 4, April 1997, pp. 493-506.
IEEE DOI 9704
BibRef
Earlier:
Generalized convex set theoretic image recovery,
ICIP96(II: 453-456).
IEEE DOI BibRef
Earlier:
Convex set theoretic image recovery via chaotic iterations of approximate projections,
ICIP94(III: 182-186).
IEEE DOI 9411
BibRef

Combettes, P.L.,
Convex Set Theoretic Image Recovery with Inexact Projection Algorithms,
ICIP01(I: 257-260).
IEEE DOI 0108
BibRef

Combettes, P.L.[Patrick L.],
On the Numerical Robustness of the Parallel Projection Method in Signal Synthesis,
SPLetters(8), No. 2, February 2001, pp. 45-47.
IEEE Top Reference. 0101
BibRef
Earlier:
A Surrogate Projection Method for the Constrained Minimization of Residual Energy in Image Restoration,
ICIP99(25PO4). Not in proceedings. BibRef

Combettes, P.L.[Patrick L.], Pesquet, J.C.,
Convex Multiresolution Analysis,
PAMI(20), No. 12, December 1998, pp. 1308-1318.
IEEE DOI BibRef 9812
Earlier:
Nonlinear multiresolution image analysis via convex projections,
ICIP98(II: 762-765).
IEEE DOI 9810
Extension of wavelet analysis. BibRef

Tapamo, J.R.[Jules-Raymond],
Multi-Scale Image Analysis by Pyramidal Algorithms,
JMIV(10), No. 1, January 1999, pp. 87-95.
DOI Link BibRef 9901

García Salinas, M.J.[María José], Gomez Lopera, J.F.[Juan Francisco], Luque Escamilla, P.L.[Pedro Luis], Martinez Aroza, J.[José], Román Roldán, R.[Ramón], Segura Luna, J.C.[J. Carlos],
Empirical multiresolution models applicable to gray-level image processing,
SP:IC(11), No. 2, December 1997, pp. 83-93.
Elsevier DOI BibRef 9712

Chen, C.C.[Chi-Chyang], Wang, C.D.[Chiou-Dong],
A simple edge-preserving filtering technique for constructing multi-resolution systems of images,
PRL(20), No. 5, May 1999, pp. 495-506. 9912
BibRef

Faugeras, O.D.[Olivier D.], Nielsen, M.[Mads], Perona, P.[Pietro], ter Haar Romeny, B.M.[Bart M.], Sapiro, G.[Guillermo],
Special Issue on the Second International Conference on Scale Space Theory in Computer Vision, Guest Editors' Comments,
JVCIR(11), No. 2, June 2000, pp. 95-95. 0008
BibRef

Goutsias, J., Heijmans, H.J.A.M.,
Nonlinear Multiresolution Signal Decomposition Schemes--Part I: Morphological Pyramids,
IP(9), No. 11, November 2000, pp. 1862-1876.
IEEE DOI 0011
See also Nonlinear Multiresolution Signal Decomposition Schemes--Part II: Morphological Wavelets. BibRef

Kadir, T.[Timor], Brady, M.[Michael],
Saliency, Scale and Image Description,
IJCV(45), No. 2, November 2001, pp. 83-105.
DOI Link 0111
A multiscale algorithm for the selection of salient regions of an image, applied to tracking, recognition and retrieval. See also Comparison of Affine Region Detectors, A. BibRef

Kadir, T.[Timor], Brady, M.[Michael],
Estimating statistics in arbitrary regions of interest,
BMVC05(xx-yy).
HTML Version. 0509
BibRef

Joshi, N., Kadir, T., Brady, S.M.,
Simplified Computation for Nonparametric Windows Method of Probability Density Function Estimation,
PAMI(33), No. 8, August 2011, pp. 1673-1680.
IEEE DOI 1107
BibRef

Kadir, T., Brady, M.,
Unsupervised non-parametric region segmentation using level sets,
ICCV03(1267-1274).
IEEE DOI 0311
BibRef

Raducanu, B.[Bogdan], Graña, M.[Manuel], Albizuri, F.X.[F. Xabier],
Morphological Scale Spaces and Associative Morphological Memories: Results on Robustness and Practical Applications,
JMIV(19), No. 2, September 2003, pp. 113-131.
DOI Link 0308
BibRef

Saha, P.K.[Punam Kumar],
Tensor scale: A local morphometric parameter with applications to computer vision and image processing,
CVIU(99), No. 3, September 2005, pp. 384-413.
Elsevier DOI 0508
Localized scale. For matching slices. BibRef

Xu, Z.Y.[Zi-Yue], Saha, P.K.[Punam K.], Dasgupta, S.[Soura],
Tensor scale: An analytic approach with efficient computation and applications,
CVIU(116), No. 10, October 2012, pp. 1060-1075.
Elsevier DOI 1209
Scale-space; Local scale; Morphometry; Tensor scale; Image filtering; Image interpolation BibRef

Saha, P.K.[Punam K.], Xu, Z.Y.[Zi-Yue],
An Analytic Approach to Tensor Scale with an Efficient Algorithm and Applications to Image Filtering,
DICTA10(429-434).
IEEE DOI 1012
BibRef

Ghosh, K.[Kuntal], Sarkar, S.[Sandip], Bhaumik, K.[Kamales],
Understanding image structure from a new multi-scale representation of higher order derivative filters,
IVC(25), No. 8, 1 August 2007, pp. 1228-1238.
Elsevier DOI 0706
Multi-scale filters; Derivative filters; Zero-crossings; Non-classical receptive field BibRef

Markussen, B.[Bo], Steenstrup Pedersen, K.[Kim], Loog, M.[Marco],
Second Order Structure of Scale-Space Measurements,
JMIV(31), No. 2-3, July 2008, pp. 207-220.
WWW Link. 0711
BibRef
Earlier: A2, A3, A1:
Generic Maximum Likely Scale Selection,
SSVM07(362-373).
Springer DOI 0705
BibRef

Wang, Y.Z.[Yi-Zhou], Zhu, S.C.[Song-Chun],
Perceptual Scale-Space and Its Applications,
IJCV(80), No. 1, October 2008, pp. xx-yy.
Springer DOI 0809
BibRef
Earlier: Add A2: Bahrami, S.[Siavosh], ICCV05(I: 58-65).
IEEE DOI 0510
BibRef

Gilboa, G.[Guy],
Nonlinear Scale Space with Spatially Varying Stopping Time,
PAMI(30), No. 12, December 2008, pp. 2175-2187.
IEEE DOI 0811
Applied to denoising signals. See also Variational Denoising of Partly Textured Images by Spatially Varying Constraints. See also Structure-Texture Image Decomposition: Modeling, Algorithms, and Parameter Selection. BibRef

Vanhamel, I.[Iris], Mihai, C.[Cosmin], Sahli, H.[Hichem], Katartzis, A.[Antonis], Pratikakis, I.E.[Ioannis E.],
Scale Selection for Compact Scale-Space Representation of Vector-Valued Images,
IJCV(84), No. 2, August 2009, pp. xx-yy.
Springer DOI 0906
BibRef
Earlier: A2, A1, A3, A4, A5: SSVM07(32-42).
Springer DOI 0705
BibRef

Yang, Z.W.[Zheng-Wu], Fang, T.[Tao],
On the accuracy of image normalization by Zernike moments,
IVC(28), No. 3, March 2010, pp. 403-413.
Elsevier DOI 1001
Image normalization; Zernike moments; Image regularization; Scale space BibRef

Chen, W.R.[Wei-Rong], Henebry, G.M.[Geoffrey M.],
Change of spatial information under rescaling: A case study using multi-resolution image series,
PandRS(64), No. 6, November 2009, pp. 592-597.
Elsevier DOI 1001
High spatial resolution imagery; Semivariogram analysis; Spatial structure; Rescaling BibRef

Tadmor, E.[Eitan], Athavale, P.[Prashant],
Multiscale Image Representation Using Novel Integro-Differential Equations,
IPI(3), 2009, pp. 693-710. BibRef 0900

Athavale, P.[Prashant], Tadmor, E.[Eitan],
Integro-Differential Equations Based On (BV, L^1) Image Decomposition,
SIIMS(4), No. 1, 2011, pp. 300-312.
DOI Link 1106
hierarchical image decompositions; total variation; denoising; deblurring; inverse scale; variational problem; integro-differential equation; energy decomposition BibRef

Bariya, P.[Prabin], Novatnack, J.[John], Schwartz, G.[Gabriel], Nishino, K.[Ko],
3D Geometric Scale Variability in Range Images: Features and Descriptors,
IJCV(99), No. 2, September 2012, pp. 232-255.
Springer DOI 1205
Range image, Scale variability, Scale-space, Geometric feature, Shape descriptor, Range image registration, 3D object recognition. Variations is spatial extents of local structures of 3D data. BibRef

Bariya, P.[Prabin], Nishino, K.[Ko],
Scale-hierarchical 3D object recognition in cluttered scenes,
CVPR10(1657-1664).
IEEE DOI 1006
Scale variability of local structure. BibRef

Novatnack, J.[John], Nishino, K.[Ko],
Scale-Dependent 3D Geometric Features,
ICCV07(1-8).
IEEE DOI 0710
BibRef

Novatnack, J.[John], Nishino, K.[Ko], Shokoufandeh, A.[Ali],
Extracting 3D Shape Features in Discrete Scale-Space,
3DPVT06(946-953).
IEEE DOI 0606
BibRef

Zaharescu, A.[Andrei], Boyer, E.[Edmond], Horaud, R.[Radu],
Keypoints and Local Descriptors of Scalar Functions on 2D Manifolds,
IJCV(100), No. 1, October 2012, pp. 78-98.
WWW Link. 1208
BibRef
Earlier:
Scale-space representation of scalar functions on 2D manifolds,
3DRR11(609-616).
IEEE DOI 1201
BibRef

Elfiky, N.M.[Noha M.], Gonzàlez, J.[Jordi], Roca, F.X.[F. Xavier],
Compact and adaptive spatial pyramids for scene recognition,
IVC(30), No. 8, August 2012, pp. 492-500.
Elsevier DOI 1209
Scene recognition; Spatial pyramids; Texture; Dimensionality reduction; Agglomerative information theory BibRef

Elfiky, N.M.[Noha M.], Khan, F.S.[Fahad Shahbaz], van de Weijer, J.[Joost], Gonzàlez, J.[Jordi],
Discriminative compact pyramids for object and scene recognition,
PR(45), No. 4, 2012, pp. 1627-1636.
Elsevier DOI 1410
Object and scene recognition BibRef

Vondenhoff, E.,
Uniqueness Results for Image Reconstruction from Features on Curves in alpha-Scale Spaces,
JMIV(45), No. 1, January 2013, pp. 1-12.
WWW Link. 1301
2-D image specified by features on 3-D curves BibRef

Theriault, C.[Christian], Thome, N.[Nicolas], Cord, M.[Matthieu],
Extended Coding and Pooling in the HMAX Model,
IP(22), No. 2, February 2013, pp. 764-777.
IEEE DOI 1302
BibRef
Earlier:
HMAX-S: Deep scale representation for biologically inspired image categorization,
ICIP11(1261-1264).
IEEE DOI 1201
BibRef

Theriault, C.[Christian], Thome, N.[Nicolas], Cord, M.[Matthieu],
Dynamic Scene Classification: Learning Motion Descriptors with Slow Features Analysis,
CVPR13(2603-2610)
IEEE DOI 1309
Motion descriptors BibRef

Li, J.[Jian], Levine, M.D.[Martin D.], An, X.J.[Xiang-Jing], Xu, X.[Xin], He, H.[Hangen],
Visual Saliency Based on Scale-Space Analysis in the Frequency Domain,
PAMI(35), No. 4, April 2013, pp. 996-1010.
IEEE DOI 1303
BibRef
Earlier: A1, A2, A3, A5, Only:
Saliency Detection Based on Frequency and Spatial Domain Analyses,
BMVC11(xx-yy).
HTML Version. 1110
Detect large and small, inhibit repetitive. See also Real-Time Long-Range Lane Detection and Tracking for Intelligent Vehicle. BibRef

Mu, N.[Nan], Xu, X.[Xin], Zhang, X.L.[Xiao-Long], Lin, X.L.[Xiao-Li],
Discrete stationary wavelet transform based saliency information fusion from frequency and spatial domain in low contrast images,
PRL(115), 2018, pp. 84-91.
Elsevier DOI 1812
Frequency domain, Spatial domain, Saliency detection, Discrete stationary wavelet transform, Low contrast image BibRef

Mu, N.[Nan], Xu, X.[Xin], Zhang, X.L.[Xiao-Long],
A spatial-frequency-temporal domain based saliency model for low contrast video sequences,
JVCIR(58), 2019, pp. 79-88.
Elsevier DOI 1901
Salient object detection, Low contrast videos, Multimodal fusion, Region covariance, Incremental learning BibRef

Bates, J.[Jonathan], Mio, W.[Washington],
Density Estimators of Gaussian Type on Closed Riemannian Manifolds,
JMIV(50), No. 1-2, September 2014, pp. 53-59.
WWW Link. 1408
BibRef

Bates, J.[Jonathan], Liu, X.W.[Xiu-Wen], Mio, W.[Washington],
Scale-Space Spectral Representation of Shape,
ICPR10(2648-2651).
IEEE DOI 1008
BibRef

Tschirsich, M.[Martin], Kuijper, A.[Arjan],
Notes on Discrete Gaussian Scale Space,
JMIV(51), No. 1, January 2015, pp. 106-123.
WWW Link. 1503
BibRef
Earlier:
Discrete Deep Structure,
SSVM13(343-354).
Springer DOI 1305
BibRef
Earlier:
A Discrete Scale Space Neighborhood for Robust Deep Structure Extraction,
SSSPR12(126-134).
Springer DOI 1211
BibRef
And:
Laplacian Eigenimages in Discrete Scale Space,
SSSPR12(162-170).
Springer DOI 1211
BibRef

Jiang, B.[Bin], Brandt, S.A.[S. Anders],
A Fractal Perspective on Scale in Geography,
IJGI(5), No. 6, 2016, pp. 95.
DOI Link 1608
BibRef

Liang, W.J.[Wei-Jie], Lin, G.X.[Gang-Xuan], Lu, C.S.[Chun-Shien],
Tree Structure Sparsity Pattern Guided Convex Optimization for Compressive Sensing of Large-Scale Images,
IP(26), No. 2, February 2017, pp. 847-859.
IEEE DOI 1702
compressed sensing BibRef

Calatroni, L.[Luca], van Gennip, Y.[Yves], Schönlieb, C.B.[Carola-Bibiane], Rowland, H.M.[Hannah M.], Flenner, A.[Arjuna],
Graph Clustering, Variational Image Segmentation Methods and Hough Transform Scale Detection for Object Measurement in Images,
JMIV(57), No. 2, February 2017, pp. 269-291.
Springer DOI 1702
Scale detection. BibRef

Hu, Z.W.[Zhong-Wen], Li, Q.Q.[Qing-Quan], Zhang, Q.[Qian], Zou, Q.[Qin], Wu, Z.C.[Zhao-Cong],
Unsupervised Simplification of Image Hierarchies via Evolution Analysis in Scale-Sets Framework,
IP(26), No. 5, May 2017, pp. 2394-2407.
IEEE DOI 1704
Clustering algorithms BibRef

Ben Khlifa, A.[Ameni], Ghorbel, F.[Faouzi],
An almost complete curvature scale space representation: Euclidean case,
SP:IC(75), 2019, pp. 32-43.
Elsevier DOI 1906
Curvature Scale Space, Almost-completeness, Curvature's levels, Shape recognition, Retrieval BibRef

Zhou, P.[Peng], Ni, B.B.[Bing-Bing], Geng, C.[Cong], Hu, J.G.[Jian-Guo], Xu, Y.[Yi],
Scale-Transferrable Object Detection,
CVPR18(528-537)
IEEE DOI 1812
Feature extraction, Object detection, Semantics, Detectors, Image resolution, Convolution, Heart BibRef

Ramos, L.A.[Lucas Alexandre], Marana, A.N.[Aparecido Nilceu], de Souza, Jr., L.A.[Luis Antônio],
A Scale-Space Approach for Multiscale Shape Analysis,
CIARP17(542-549).
Springer DOI 1802
BibRef

Rampal, K., Sakurai, K., Imaoka, H.,
Fast and accurate scale estimation method for object tracking,
ICPR16(2712-2715)
IEEE DOI 1705
Decision support systems, Feature extraction, Kernel, Maximum likelihood estimation, Object tracking, object tracking, scale, estimation BibRef

Scalzo-Cornacchia, M.[Maria], Velipasalar, S.[Senem],
Scale estimation with difference of ordered residuals,
ICIP15(3377-3381)
IEEE DOI 1512
BibRef

Liu, X.M.[Xian-Ming], Ji, R.R.[Rong-Rong], Wang, C.H.[Chang-Hu], Liu, W.[Wei], Zhong, B.N.[Bi-Neng], Huang, T.S.[Thomas S.],
Understanding image structure via hierarchical shape parsing,
CVPR15(5042-5050)
IEEE DOI 1510
BibRef

Alves, W.A.L.[Wonder A. L.], Morimitsu, A.[Alexandre], Hashimoto, R.F.[Ronaldo F.],
Scale-Space Representation Based on Levelings Through Hierarchies of Level Sets,
ISMM15(265-276).
Springer DOI 1506
BibRef

Cárdenas, G.M.[Giovanno Marcelo], Weickert, J.[Joachim], Schäffer, S.[Sarah],
A Linear Scale-Space Theory for Continuous Nonlocal Evolutions,
SSVM15(103-114).
Springer DOI 1506
BibRef

Serra, J.[Jean], Kiran, B.R.[Bangalore Ravi],
Energetic lattice for optimizing over hierarchies of partitions,
ICIP14(4857-4861)
IEEE DOI 1502
Convergence BibRef

Rocca, L.[Luigi], Puppo, E.[Enrico],
A Virtually Continuous Representation of the Deep Structure of Scale-Space,
CIAP13(II:522-531).
Springer DOI 1309
BibRef

Fleischmann, O.[Oliver], Sommer, G.[Gerald],
Automatic Scale Selection of Superimposed Signals,
DAGM12(297-306).
Springer DOI 1209
BibRef

Ni, B.B.[Bing-Bing], Xu, M.D.[Meng-Di], Tang, J.H.[Jin-Hui], Yan, S.C.[Shui-Cheng], Moulin, P.[Pierre],
Omni-range spatial contexts for visual classification,
CVPR12(3514-3521).
IEEE DOI 1208
Size changes. BibRef

Chen, C.[Chao], Edelsbrunner, H.[Herbert],
Diffusion runs low on persistence fast,
ICCV11(423-430).
IEEE DOI 1201
BibRef

Hidane, M., Lézoray, O.[Olivier], El Moataz, A.,
A scale-space based hierarchical representation of discrete data,
ICIP11(285-288).
IEEE DOI 1201
BibRef

Yang, J.[Jimei], Safar, S.[Simon], Yang, M.H.[Ming-Hsuan],
Max-Margin Boltzmann Machines for Object Segmentation,
CVPR14(320-327)
IEEE DOI 1409
Boltzmann Machines BibRef

Tsai, Y.H.[Yi-Hsuan], Yang, J.[Jimei], Yang, M.H.[Ming-Hsuan],
Decomposed Learning for Joint Segmentation and Categorization,
BMVC13(xx-yy).
DOI Link 1402
BibRef
And: A2, A3, Only:
Learning Hierarchical Image Representation with Sparsity, Saliency and Locality,
BMVC11(xx-yy).
HTML Version. 1110
See also Learning structured visual dictionary for object tracking. BibRef

Karla, J.W.[Jason W.], Lalibertea, A.S.[Andrea S.], Rangoa, A.[Albert],
Spatial Dependence of Predictions from Image Segmentation: A Methods to Determine Appropriate Scales for Producing Land-Management Information,
GEOBIA10(xx-yy).
PDF File. 1007
BibRef

Paris, S.[Sebastien], Glotin, H.[Herve],
Pyramidal Multi-level Features for the Robot Vision@ICPR 2010 Challenge,
ICPR10(2949-2952).
IEEE DOI 1008
2 hierarchical features. BibRef

Li, H.[Hao], Peng, Y.X.[Yu-Xin],
Effective Multi-level Image Representation for Image Categorization,
ICPR10(1048-1051).
IEEE DOI 1008
BibRef

Dai, D.X.[Deng-Xin], Yang, W.[Wen], Wu, T.F.[Tian-Fu],
Three-layer Spatial Sparse Coding for Image Classification,
ICPR10(613-616).
IEEE DOI 1008
BibRef

Nilufar, S.[Sharmin], Ray, N.[Nilanjan], Zhang, H.[Hong],
Object Detection With DoG Scale-Space: A Multiple Kernel Learning Approach,
IP(21), No. 8, August 2012, pp. 3744-3756.
IEEE DOI 1208
BibRef
And:
Wavelet subband-based steam detection by multiple kernel learning,
ICIP12(1153-1156).
IEEE DOI 1302
BibRef
Earlier:
Optimum kernel function design from scale space features for object detection,
ICIP09(861-864).
IEEE DOI 0911
BibRef

Brill, M.H.,
Visual adaptation of scale and imprecision in a noisy world,
AIPR04(28-32).
IEEE DOI 0410
BibRef

Hirano, Y.[Yuta], Imiya, A.[Atsushi],
Scale-Space Clustering on a Unit Hypersphere,
CAIP15(I:186-197).
Springer DOI 1511
BibRef

Sakai, T.[Tomoya], Nishiguchi, H.[Haruhiko], Itoh, H.[Hayato], Imiya, A.[Atsushi],
Bifurcation of Segment Edge Curves in Scale Space,
SSVM11(302-313).
Springer DOI 1201
BibRef
Earlier: A2, A4, A1, Only:
Scale Space Hierarchy of Segments,
CAIP09(947-954).
Springer DOI 0909
BibRef

Bileschi, S.[Stanley],
Object detection at multiple scales improves accuracy,
ICPR08(1-5).
IEEE DOI 0812
BibRef

Wu, Q.[Qing], Chen, C.[Chun], Yu, Y.Z.[Yi-Zhou],
Wavelet-based hybrid multilinear models for multidimensional image approximation,
ICIP08(2828-2831).
IEEE DOI 0810
BibRef

Wu, Q.[Qing], Xia, T.[Tian], Yu, Y.Z.[Yi-Zhou],
Hierarchical Tensor Approximation of Multidimensional Images,
ICIP07(IV: 49-52).
IEEE DOI 0709
Transform into set of signals to capture multi-scale structures. BibRef

Derpanis, K.G.P.[Konstantinos G.P.], Leung, E.T.H.[Erich T. H.], Sizintsev, M.[Mikhail],
Fast Scale-Space Feature Representations by Generalized Integral Images,
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Chapter on Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar continues in
Scale Space Computation of Features .