7.1.4.1 Implementation of Convolution and Smoothing Techniques

Chapter Contents (Back)
Convolution. Smoothing.
See also Smoothing Techniques, Adaptive Smoothing.
See also Multi-level, Multi-Scale Segmentation and Smoothing Methods.
See also Implementation, Algorithms and Design of Filters.

Sklansky, J.,
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Abramatic, J.F., Faugeras, O.D.,
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Ney, H.,
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Ferrari, L.A.[Leonard A.], Sklansky, J.[Jack],
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Gourlay, A.R.,
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O'Leary, D.P.[Dianne P.],
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Lohar, G., Mukherjee, D.P., Dutta Majumder, D.,
On a Decomposition of 2-D Circular Convolution,
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Glasbey, C.A., Jones, R.,
Fast Computation of Moving Average and Related Filters in Octagonal Windows,
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Cocchia, F., Carrato, S., Ramponi, G.,
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Ramponi, G.,
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Karasik, Y.B.,
A Recursive Formula for Convolutions/Correlations and Its Application in Pattern-Recognition,
PRL(19), No. 1, January 1998, pp. 53-56. 9807
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Karasik, Y.B.,
How To Compute 3-Dimensional Convolution and/or Correlation Optically: A Mathematical Foundation,
ModOpt(45), No. 4, April 1998, pp. 817-823. 9806
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Earlier:
How to Implement N-Dimensional Image Processing Optically,
ICIP97(I: 715-718).
IEEE DOI BibRef

Boykov, Y.Y.[Yuri Y.], Veksler, O.[Olga], Zabih, R.[Ramin],
A Variable Window Approach to Early Vision,
PAMI(20), No. 12, December 1998, pp. 1283-1294.
IEEE DOI BibRef 9812
Earlier:
A Variable Neighborhood Approach to Early Vision,
DARPA97(1453-1458). Change the window at object boundaries to get better results. Apply to restoration, motion, stereo, correspondence. BibRef

Veksler, O.[Olga],
Efficient parallel optimization for potts energy with hierarchical fusion,
CVPR15(887-895)
IEEE DOI 1510
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Boykov, Y.Y.[Yuri Y.], Veksler, O.[Olga], Zabih, R.[Ramin],
Markov Random Fields with Efficient Approximations,
CVPR98(648-655).
IEEE DOI MRF Segmentation BibRef 9800

McCoy, J.S.[J. Scott],
Convolution algorithm for efficient hardware implementation,
US_Patent5,926,580, Jul 20, 1999
WWW Link. BibRef 9907

Sangwine, S.J.[Stephen J.], Ell, T.A.[Todd A.], Karasik, Y.B.,
Evaluation of 3-D Convolution by 2-D Filtering,
AppOpt(36), No. 29, 1997. BibRef 9700

Sangwine, S.J.[Stephen J.], Ell, T.A.[Todd A.],
Colour image filters based on hypercomplex convolution,
VISP(147), No. 2, April 2000, pp. 89. 0005
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Ell, T.A.[Todd A.], Sangwine, S.J.[Stephen J.],
Hypercomplex Fourier Transforms of Color Images,
IP(16), No. 1, January 2007, pp. 22-35.
IEEE DOI 0701
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Earlier: A2, A1: ICIP01(I: 137-140).
IEEE DOI 0108
BibRef

Sangwine, S.J., Ell, T.A.,
Vector zone plates as test patterns for linear vector filters,
ICIP02(II: 361-364).
IEEE DOI 0210
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Sangwine, S.J.[Stephen J.], Ell, T.A.[Todd A.],
Hypercomplex Auto- And Cross-Correlation of Color Images,
ICIP99(IV:319-322).
IEEE DOI BibRef 9900

Ell, T.A.[Todd A.],
Multi-Vector Color-Image Filters,
ICIP07(V: 245-248).
IEEE DOI 0709
BibRef
And:
Hypercomplex Color Affine Filters,
ICIP07(V: 249-252).
IEEE DOI 0709
BibRef

Ell, T.A.[Todd A.],
Hypercomplex Wiener-Khintchine Theorem with Application to Color Image Correlation,
ICIP00(Vol II: 792-795).
IEEE DOI 0008
BibRef

Evans, C., Sangwine, S.J.,
Hypercomplex Color-sensitive Smoothing Filters,
ICIP00(Vol I: 541-544).
IEEE DOI 0008
BibRef

Mount, D.M., Kanungo, T., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.,
Approximating large convolutions in digital images,
IP(10), No. 12, December 2001, pp. 1826-1835.
IEEE DOI 0201
BibRef
Earlier: UMD--TR4017, May 1999.
WWW Link. BibRef

Reichenbach, S.E., Geng, F.,
Two-dimensional cubic convolution,
IP(12), No. 8, August 2003, pp. 857-865.
IEEE DOI 0308
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Toivonen, T.[Tuukka], Heikkilä, J.[Janne],
Video Filtering With Fermat Number Theoretic Transforms Using Residue Number System,
CirSysVideo(16), No. 1, January 2006, pp. 92-101.
IEEE DOI 0601
Implementation of convolution filters. BibRef

Sun, C.M.[Chang-Ming],
Moving average algorithms for diamond, hexagon, and general polygonal shaped window operations,
PRL(27), No. 6, 15 April 2006, pp. 556-566.
Elsevier DOI Moving average algorithm; Diamond shaped windows; Hexagon shaped windows; Polygonal shaped windows; Local statistics 0604
BibRef

Lampert, C.H., Wirjadi, O.,
An Optimal Nonorthogonal Separation of the Anisotropic Gaussian Convolution Filter,
IP(15), No. 11, November 2006, pp. 3501-3513.
IEEE DOI 0610
BibRef

Palomares, J.M., Gonzalez, J., Ros, E., Prieto, A.,
General Logarithmic Image Processing Convolution,
IP(15), No. 11, November 2006, pp. 3602-3608.
IEEE DOI 0610
BibRef

Reju, V.G., Koh, S.N., Soon, I.Y.,
Convolution Using Discrete Sine and Cosine Transforms,
SPLetters(14), No. 7, July 2007, pp. 445-448.
IEEE DOI 0707
BibRef

Suresh, K., Sreenivas, T.V.,
Block Convolution Using Discrete Trigonometric Transforms and Discrete Fourier Transform,
SPLetters(15), No. 1, 2008, pp. 469-472.
IEEE DOI 0806
BibRef

Argyriou, V., Vlachos, T., Piroddi, R.,
Gradient-Adaptive Normalized Convolution,
SPLetters(15), No. 1, 2008, pp. 489-492.
IEEE DOI 0806
BibRef

Munoz-Barrutia, A.[Arrate], Artaechevarria, X.[Xabier], Ortiz-de-Solorzano, C.[Carlos],
Spatially Variant Convolution With Scaled B-Splines,
IP(19), No. 1, January 2010, pp. 11-24.
IEEE DOI 1001
BibRef
Earlier: A2, A1, A3:
Restoration of Biomedical Images using Locally Adaptive B-Spline Smoothing,
ICIP07(II: 425-428).
IEEE DOI 0709
BibRef

Gil, D.[Debora], Hernàndez-Sabaté, A.[Aura], Burnat, M.[Mireia], Jansen, S.[Steven], Martínez-Villalta, J.[Jordi],
Structure-Preserving Smoothing of Biomedical Images,
PR(44), No. 9, September 2011, pp. 1842-1851.
Elsevier DOI 1106
BibRef
Earlier: CAIP09(427-434).
Springer DOI 0909
Non-linear smoothing; Differential geometry; Anatomical structures segmentation; Cardiac magnetic resonance; Computerized tomography BibRef

Martinez, J., Heusdens, R., Hendriks, R.C.,
A Generalized Poisson Summation Formula and its Application to Fast Linear Convolution,
SPLetters(18), No. 9, September 2011, pp. 501-504.
IEEE DOI 1108
BibRef

Simois, F.J., Acha, J.I.,
A New Algorithm for Real Data Convolutions With j -Circulants,
SPLetters(18), No. 11, November 2011, pp. 655-658.
IEEE DOI 1112
BibRef

Milanfar, P.[Peyman],
Symmetrizing Smoothing Filters,
SIIMS(6), No. 1, 2013, pp. 263-284.
DOI Link 1304
BibRef

Wei, J.N.[Jia-Ning], Bouman, C.A., Allebach, J.P.,
Fast Space-Varying Convolution Using Matrix Source Coding With Applications to Camera Stray Light Reduction,
IP(23), No. 5, May 2014, pp. 1965-1979.
IEEE DOI 1405
convolution BibRef

Huo, H.[Haiye],
A new convolution theorem associated with the linear canonical transform,
SIViP(13), No. 1, February 2019, pp. 127-133.
Springer DOI 1901
BibRef

Aguilar-González, A.[Abiel], Arias-Estrada, M.[Miguel], Pérez-Patricio, M.[Madaín], Camas-Anzueto, J.L.,
An FPGA 2D-convolution unit based on the CAPH language,
RealTimeIP(16), No. 2, April 2019, pp. 305-319.
Springer DOI 1904
BibRef

Chun, I.Y.[Il Yong], Hong, D.[David], Adcock, B.[Ben], Fessler, J.A.[Jeffrey A.],
Convolutional Analysis Operator Learning: Dependence on Training Data,
SPLetters(26), No. 8, August 2019, pp. 1137-1141.
IEEE DOI 1908
convolution, probability, unsupervised learning, convolutional analysis operator learning, training data, CAOL, dependence on training sample size BibRef

Ramasinghe, S.[Sameera], Khan, S.H.[Salman H.], Barnes, N.M.[Nick M.], Gould, S.[Stephen],
Representation Learning on Unit Ball with 3D Roto-translational Equivariance,
IJCV(128), No. 6, June 2020, pp. 1612-1634.
Springer DOI 2006
BibRef
And:
Blended Convolution and Synthesis for Efficient Discrimination of 3D Shapes,
WACV20(21-31)
IEEE DOI 2006
Shape, Convolution, Kernel, Feature extraction, Solid modeling Volumetric convolution. BibRef

Rahman, S.[Shafin], Khan, S.H.[Salman H.], Barnes, N.M.[Nick M.], Khan, F.S.[Fahad Shahbaz],
Any-shot Object Detection,
ACCV20(III:89-106).
Springer DOI 2103
BibRef

Roddy, P.J., McEwen, J.D.,
Sifting Convolution on the Sphere,
SPLetters(28), 2021, pp. 304-308.
IEEE DOI 2102
Convolution, Harmonic analysis, Kernel, Standards, Transforms, Hilbert space, Convolution, 2-sphere, spherical harmonics BibRef

Xiao, Y.Q.[You-Qing], Cai, Z.C.[Zhan-Chuan], Yuan, X.X.[Xi-Xi],
YuvConv: Multi-Scale Non-Uniform Convolution Structure Based on YUV Color Model,
MultMed(23), 2021, pp. 2533-2544.
IEEE DOI 2108
Tensile stress, Convolution, Machine learning, Spatial resolution, Computational modeling, Task analysis, Feature extraction, YuvConv, WideResnet BibRef

Huang, D.[Di], Zhang, R.[Rui], Zhang, X.S.[Xi-Shan], Wu, F.[Fan], Wang, X.Z.[Xian-Zhuo], Jin, P.W.[Peng-Wei], Liu, S.L.[Shao-Li], Li, L.[Ling], Chen, Y.J.[Yun-Ji],
A Decomposable Winograd Method for N-D Convolution Acceleration in Video Analysis,
IJCV(129), No. 10, October 2021, pp. 2806-2826.
Springer DOI 2110
BibRef

Jonsson, J.[Joel], Cheeseman, B.L.[Bevan L.], Maddu, S.[Suryanarayana], Gonciarz, K.[Krzysztof], Sbalzarini, I.F.[Ivo F.],
Parallel Discrete Convolutions on Adaptive Particle Representations of Images,
IP(31), 2022, pp. 4197-4212.
IEEE DOI 2206
Image reconstruction, Image resolution, Image processing, Microscopy, Convolution, Data structures, Signal resolution, deconvolution BibRef

Wei, X.[Xuan], Su, S.X.[Shi-Xiang], Wei, Y.[Yun], Lu, X.B.[Xiao-Bo],
Rotational Convolution: Rethinking Convolution for Downside Fisheye Images,
IP(32), 2023, pp. 4355-4364.
IEEE DOI 2308
Convolution, Kernel, Standards, Cameras, Feature extraction, Task analysis, Object detection, Rotational convolution, object detection BibRef


Kim, S.[Sanghyeon], Park, E.[Eunbyung],
SMPConv: Self-Moving Point Representations for Continuous Convolution,
CVPR23(10289-10299)
IEEE DOI 2309
BibRef

Maggioni, M.[Matteo], Tanay, T.[Thomas], Babiloni, F.[Francesca], McDonagh, S.[Steven], Leonardis, A.[Aleš],
Tunable Convolutions with Parametric Multi-Loss Optimization,
CVPR23(20226-20236)
IEEE DOI 2309
BibRef

Wu, H.[Hai], Wen, C.[Chenglu], Shi, S.S.[Shao-Shuai], Li, X.[Xin], Wang, C.[Cheng],
Virtual Sparse Convolution for Multimodal 3D Object Detection,
CVPR23(21653-21662)
IEEE DOI 2309
BibRef

Wu, W.X.[Wen-Xuan], Fuxin, L.[Li], Shan, Q.[Qi],
PointConvFormer: Revenge of the Point-based Convolution,
CVPR23(21802-21813)
IEEE DOI 2309
BibRef

He, S.[Shwai], Jiang, C.[Chenbo], Dong, D.[Daize], Ding, L.[Liang],
SD-Conv: Towards the Parameter-Efficiency of Dynamic Convolution,
WACV23(6443-6452)
IEEE DOI 2302
Costs, Convolution, Computational modeling, Redundancy, Computational efficiency, Task analysis, and algorithms (including transfer) BibRef

Varga, L.A.[Leon Amadeus], Messmer, M.[Martin], Benbarka, N.[Nuri], Zell, A.[Andreas],
Wavelength-aware 2D Convolutions for Hyperspectral Imaging,
WACV23(3777-3786)
IEEE DOI 2302
Training, Deep learning, Convolution, Image color analysis, Inspection, Cameras, Recording, Remote Sensing BibRef

Hamaguchi, R.[Ryuhei], Furukawa, Y.[Yasutaka], Onishi, M.[Masaki], Sakurada, K.[Ken],
Heterogeneous Grid Convolution for Adaptive, Efficient, and Controllable Computation,
CVPR21(13941-13950)
IEEE DOI 2111
Convolutional codes, Location awareness, Image segmentation, Convolution, Roads, Semantics, Computer architecture BibRef

Chen, J.[Jin], Wang, X.J.[Xi-Jun], Guo, Z.C.[Zi-Chao], Zhang, X.Y.[Xiang-Yu], Sun, J.[Jian],
Dynamic Region-Aware Convolution,
CVPR21(8060-8069)
IEEE DOI 2111
Matched filters, Visualization, Convolution, Face recognition, Semantics, Object detection, Information filters BibRef

Arizumi, N.,
Piecewise Polynomial Approximation Method for Convolution With Large Kernel,
ICIP20(3080-3083)
IEEE DOI 2011
Kernel, Convolution, Image processing, Cameras, Real-time systems, Robots, Splines (mathematics), high-resolution image, convolution, faster computation BibRef

Nguyen, A.D., Choi, S., Kim, W., Lee, S., Lin, W.,
Statistical Convolution On Unordered Point Set,
ICIP20(3468-3472)
IEEE DOI 2011
Feature extraction, Convolution, Task analysis, Computer architecture, Neural networks, Shape, Deep Learning BibRef

Carranza, C., Llamocca, D., Pattichis, M.,
Fast and Scalable 2D Convolutions and Cross-correlations for Processing Image Databases and Videos on CPUs,
SSIAI20(70-73)
IEEE DOI 2009
convolutional neural nets, fast Fourier transforms, input-output programs, microprocessor chips, optimisation BibRef

Sekikawa, Y.[Yusuke], Ishikawa, K.[Kohta], Hara, K.[Kosuke], Yoshida, Y.[Yuuichi], Suzuki, K.[Koichiro], Sato, I.[Ikuro], Saito, H.[Hideo],
Constant Velocity 3D Convolution,
3DV18(343-351)
IEEE DOI 1812
approximation theory, cameras, convolution, distance measurement, feature extraction, image representation, image sequences, Fourier transform BibRef

Wang, H.[Hui], Wang, Y.[Yue], Cao, J.J.[Jun-Jie], Liu, X.P.[Xiu-Ping],
Structure-Preserving Texture Smoothing via Adaptive Patches,
PSIVTWS17(311-324).
Springer DOI 1806
BibRef

Szegedy, C.[Christian], Liu, W.[Wei], Jia, Y.Q.[Yang-Qing], Sermanet, P.[Pierre], Reed, S.[Scott], Anguelov, D.[Dragomir], Erhan, D.[Dumitru], Vanhoucke, V.[Vincent], Rabinovich, A.[Andrew],
Going deeper with convolutions,
CVPR15(1-9)
IEEE DOI 1510
BibRef

Yoshizawa, S.[Shin], Yokota, H.[Hideo],
Fast L1 Gaussian convolution via domain splitting,
ICIP14(2908-2912)
IEEE DOI 1502
Approximation algorithms BibRef

Fanello, S.R.[Sean Ryan], Keskin, C.[Cem], Kohli, P.[Pushmeet], Izadi, S.[Shahram], Shotton, J.[Jamie], Criminisi, A.[Antonio], Pattacini, U.[Ugo], Paek, T.[Tim],
Filter Forests for Learning Data-Dependent Convolutional Kernels,
CVPR14(1709-1716)
IEEE DOI 1409
Decision Trees BibRef

Jackett, C.J., Ollington, R., Lovell, J.L.,
Efficient Digital FFT Convolution with Boundary Kernel Renormalisation,
DICTA13(1-6)
IEEE DOI 1402
convolution BibRef

Iandola, F.N.[Forrest N.], Sheffield, D.[David], Anderson, M.J.[Michael J.], Phothilimthana, P.M.[Phitchaya Mangpo], Keutzer, K.[Kurt],
Communication-minimizing 2D convolution in GPU registers,
ICIP13(2116-2120)
IEEE DOI 1402
Convolution;GPU;autotuning;parallel BibRef

Boldyš, J.[Jirí], Flusser, J.[Jan],
Invariants to Symmetrical Convolution with Application to Dihedral Kernel Symmetry,
CIAP13(II:369-378).
Springer DOI 1309
BibRef

Gonzalez, D.[Damien], Malgouyres, R.[Rémy], Esbelin, H.A.[Henri-Alex], Samir, C.[Chafik],
Convergence of Level-Wise Convolution Differential Estimators,
DGCI13(335-346).
Springer DOI 1304
BibRef
Earlier:
Fast Level-Wise Convolution,
IWCIA12(223-233).
Springer DOI 1211
BibRef

Wesierski, D.[Daniel], Mkhinini, M.[Maher], Horain, P.[Patrick], Jezierska, A.[Anna],
Fast recursive ensemble convolution of Haar-like features,
CVPR12(3689-3696).
IEEE DOI 1208
BibRef

Albanese, G.[Giulia], Cipolla, M.[Marco], Valenti, C.[Cesare],
Genetic Normalized Convolution,
CIAP11(I: 670-679).
Springer DOI 1109
BibRef

Svoboda, D.[David],
Efficient Computation of Convolution of Huge Images,
CIAP11(I: 453-462).
Springer DOI 1109
BibRef

Hu, X.[Xin], Peng, H.[Hui], Kesker, J.[Joseph], Cai, X.[Xiang], Wee, W.G.[William G.], Lee, J.H.[Jing-Huei],
An Improved Adaptive Smoothing Method,
CIAP09(757-766).
Springer DOI 0909
BibRef

Belt, H.J.W.,
Word length reduction for the integral image,
ICIP08(805-808).
IEEE DOI 0810
Sums of areas. BibRef

Knutsson, H.[Hans], Westin, C.F.[Carl-Fredrik], Andersson, M.[Mats],
Representing Local Structure Using Tensors II,
SCIA11(545-556).
Springer DOI 1105
BibRef

Brun, A., Westin, C.F., Haker, S., Knutsson, H.,
A Tensor-Like Representation for Averaging, Filtering and Interpolation of 3-D Object Orientation Data,
ICIP05(III: 1092-1095).
IEEE DOI 0512
BibRef

Lumsdaine, A., Wyatt, Jr., J.L., Elfadel, I.M.,
Nonlinear Analog Networks for Image Smoothing and Segmentation,
MIT AI Memo-1280, January 1991. BibRef 9101

Deriche, R., Cocquerez, J.P., Almouzny, G.,
An efficient method to build early image description,
ICPR88(I: 588-590).
IEEE DOI 8811
BibRef

Pan, F.[Feng], Gu, W.K.[Wei-Kang], Jin, R.J.[Ren-Jie], Yao, Q.D.[Qin-Dong],
One-pass preprocessing algorithm for real-time image processing system,
ICPR88(II: 851-853).
IEEE DOI 8811
BibRef

Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Fourier Descriptors, DFT, FFT Computation, Use, Frequency Analysis .


Last update:Mar 16, 2024 at 20:36:19