14.5.10.5 Neural Networks for Shapes and Complex Features

Chapter Contents (Back)
Feature Description. Neural Networks.
See also Recurrent Neural Networks for Shapes and Complex Features, RNN.

Morris, R.J.T., Rubin, L.D., Tirri, H.,
Neural network techniques for object orientation detection. Solution by optimal feedforward network and learning vector quantization approaches,
PAMI(12), No. 11, November 1990, pp. 1107-1115.
IEEE DOI 0401
BibRef

Anand, R.[Rangachari], Mehrotra, K.[Kishan], Mohan, C.K.[Chilukuri K.], Ranka, S.[Sanjay],
Analyzing images containing multiple sparse patterns with neural networks,
PR(26), No. 11, November 1993, pp. 1717-1724.
Elsevier DOI 0401
BibRef

Mitzias, D.A., Mertzios, B.G.,
Shape recognition with a neural classifier based on a fast polygon approximation technique,
PR(27), No. 5, May 1994, pp. 627-636.
Elsevier DOI 0401
BibRef

Gupta, L.[Lalit], Wang, J.S.[Jie-Sheng], Charles, A.[Alain], Kisatsky, P.[Paul],
Three-Layer Perceptron Based Classifiers for the Partial Shape Classification Problem,
PR(27), No. 1, January 1994, pp. 91-97.
Elsevier DOI BibRef 9401

Hirakura, Y., Yamaguchi, Y., Shimizu, H., Nagai, S.,
Dynamic Linking Among Neural Oscillators Leads to Flexible Pattern-Recognition with Figure-Ground Separation,
NeurNet(9), No. 2, March 1996, pp. 189-209. BibRef 9603
And: Addition: NeurNet(9), No. 7, October 1996, pp. 1255. BibRef

Tang, H.W., Srinivasan, V., Ong, S.H.,
Invariant Object Recognition Using a Neural Template Classifier,
IVC(14), No. 7, July 1996, pp. 473-483.
Elsevier DOI 9607
Invariants. BibRef

Ulgen, P., Flavell, A., Akamatsu, N.,
Online Shape-Recognition with Incremental Training Using Binary Synaptic Weights Algorithm,
AppIntel(6), No. 3, July 1996, pp. 225-240. Feature Extraction. Neural Networks. BibRef 9607

Jang, J.S., Shin, D.H.,
Parallel Optical-Feature Extraction by Use of Rotationally Multiplexed Holograms,
Optics Letters(21), No. 19, October 1 1996, pp. 1612-1614. Recognition. BibRef 9610

Gupta, L.[Lalit], Upadhye, A.M.[Anand M.],
Non-Linear Alignment of Neural Net Outputs for Partial Shape Classification,
PR(24), No. 10, 1991, pp. 943-948.
Elsevier DOI BibRef 9100

Nezis, K., Vosniakos, G.,
Recognizing 2-1/2D Shape-Features Using a Neural-Network and Heuristics,
CAD(29), No. 7, July 1997, pp. 523-539. 9705
BibRef

Zha, H.B.[Hong-Bin], Nanamegi, H.[Hideki], Nagata, T.[Tadashi],
Recognizing 3-D Objects by Using a Hopfield-Style Optimization Algorithm for Matching Patch Based Descriptions,
PR(31), No. 6, June 1998, pp. 727-741.
Elsevier DOI 9806
BibRef
Earlier:
3-D Object Recognition from Range Images by Using a Model-Based Hopfield-Style Matching Algorithm,
ICPR96(IV: 111-116).
IEEE DOI 9608
(Kyushu Univ., J) BibRef

Hara, K.[Kenji], Zha, H.B.[Hong-Bin], Hasegawa, T.[Tsutomu],
Regularization-Based 3-D Object Modeling from Multiple Range Images,
ICPR98(Vol II: 964-968).
IEEE DOI 9808
BibRef

Tadeusz, D., Ewa, D.D.,
Application of Neural Networks in 3D Object Recognition System,
PRAI(12), No. 4, June 1998, pp. 491-504. 9808
BibRef

Sanchiz, J.M., Pla, F., Inesta, J.M.,
Using Neural Networks To Detect Dominant Points In Chain-Coded Contours,
PRAI(12), No. 5, August 1998, pp. 661-675. 9809
BibRef
Earlier: A1, A3, A2:
A Neural Network-Based Algorithm to Detect Dominant Points from the Chain-Code of a Contour,
ICPR96(IV: 325-329).
IEEE DOI 9608
(Univ. Jaume I, E) BibRef

Foresti, G.L.[Gian Luca],
Outdoor Scene Classification by a Neural Tree-Based Approach,
PAA(2), No. 2, 1999, pp. 129-142. BibRef 9900
Earlier:
A Probabilistic Approach to Object Classification by Neural Trees,
ICIP99(I:510-514).
IEEE DOI BibRef

Foresti, G.L., Vanzella, W.,
Generalized Neural Trees for Outdoor Scene Understanding,
ICIP00(Vol III: 336-339).
IEEE DOI 0008
BibRef

Foresti, G.L., and Pieroni, G.,
3D Object Recognition by Neural Trees,
ICIP97(III: 408-411).
IEEE DOI BibRef 9700

Sim, H.C., Damper, R.I.,
A Neural Network Approach to Planar-Object Recognition in 3D Space,
PAA(2), No. 2, 1999, pp. 143-163. BibRef 9900

Bors, A.G., Pitas, I.,
Object Classification in 3-D Images Using Alpha-Trimmed Mean Radial Basis Function Network,
IP(8), No. 12, December 1999, pp. 1744-1756.
IEEE DOI 9912
BibRef

Spence, C.D.[Clay Douglas], Pearson, J.C.[John Carr], Sajda, P.[Paul],
Method and apparatus for training a neural network to learn hierarchical representations of objects and to detect and classify objects with uncertain training data,
US_Patent6,018,728, Jan 25, 2000
WWW Link. BibRef 0001

Spence, C.D.[Clay Douglas], Sajda, P.[Paul],
Method and apparatus for training a neural network to detect objects in an image,
US_Patent6,324,532, Nov 27, 2001
WWW Link. BibRef 0111

López Rubio, E.[Ezequiel], Muńoz Pérez, J.[José], Gómez Ruiz, J.A.[José Antonio],
Invariant pattern identification by self-organising networks,
PRL(22), No. 9, July 2001, pp. 983-990.
Elsevier DOI 0106
BibRef

Yuan, C., Niemann, H.,
Neural networks for the recognition and pose estimation of 3D objects from a single 2D perspective view,
IVC(19), No. 9-10, August 2001, pp. 585-592.
Elsevier DOI 0108
BibRef

Lee, Y.L.[Yang-Lyul], Park, R.H.[Rae-Hong],
A surface-based approach to 3-D object recognition using a mean field annealing neural network,
PR(35), No. 2, February 2002, pp. 299-316.
Elsevier DOI 0201
BibRef

Lee, R.S.T.[Raymond S.T.], Liu, J.N.K.[James N.K.],
Scene analysis using an integrated composite neural oscillatory elastic graph matching model,
PR(35), No. 9, September 2002, pp. 1835-1846.
Elsevier DOI 0206
BibRef

Bayro-Corrochano, E.[Eduardo], Vallejo, R.[Refugio],
Geometric preprocessing and neurocomputing for pattern recognition and pose estimation,
PR(36), No. 12, December 2003, pp. 2909-2926.
Elsevier DOI 0310
BibRef
Earlier:
Geometric neurocomputing for pattern recognition and pose estimation,
ICPR02(I: 41-44).
IEEE DOI 0211
Pose Estimation. BibRef

Chen, Y.H.[Yuan-Hao], Zhu, L.L.[Long Leo], Yuille, A.L.[Alan L.], Zhang, H.J.[Hong-Jiang],
Unsupervised Learning of Probabilistic Object Models (POMs) for Object Classification, Segmentation, and Recognition Using Knowledge Propagation,
PAMI(31), No. 10, October 2009, pp. 1747-1761.
IEEE DOI 0909
BibRef
Earlier:
Unsupervised learning of probabilistic object models (POMs) for object classification, segmentation and recognition,
CVPR08(1-8).
IEEE DOI 0806

See also Learning a Hierarchical Deformable Template for Rapid Deformable Object Parsing. BibRef

Zhu, L.L.[Long Leo], Chen, Y.H.[Yuan-Hao], Yuille, A.L.[Alan L.], Freeman, W.[William],
Latent hierarchical structural learning for object detection,
CVPR10(1062-1069).
IEEE DOI 1006
BibRef

Zhu, L.L.[Long Leo], Chen, Y.H.[Yuan-Hao], Ye, X.Y.[Xing-Yao], Yuille, A.L.[Alan L.],
Structure-perceptron learning of a hierarchical log-linear model,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Tang, X.[Xikai], Habashy, K.[Karim], Huang, F.Z.[Fang-Zheng], Li, C.[Chao], Ban, D.[Dayan],
SCA-Net: Spatial and channel attention-based network for 3D point clouds,
CVIU(232), 2023, pp. 103690.
Elsevier DOI 2305
Point clouds, 3D point processing, Attention network, Computer vision BibRef

Rivas-Manzaneque, F.[Fernando], Ribeiro, A.[Angela], Avila-García, O.[Orlando],
ICE: Implicit Coordinate Encoder for Multiple Image Neural Representation,
IP(32), 2023, pp. 5209-5219.
IEEE DOI 2310
learning a common feature space between images. BibRef


Gandikota, K.V.[Kanchana Vaishnavi], Geiping, J.[Jonas], Lähner, Z.[Zorah], Czaplinski, A.[Adam], Möller, M.[Michael],
A Simple Strategy to Provable Invariance via Orbit Mapping,
ACCV22(V:387-405).
Springer DOI 2307
Invariance of networkto data transformations. Rotation and scaling. BibRef

Saltori, C.[Cristiano], Roy, S.[Subhankar], Sebe, N.[Nicu], Iacca, G.[Giovanni],
Regularized Evolutionary Algorithm for Dynamic Neural Topology Search,
CIAP19(I:219-230).
Springer DOI 1909
BibRef

Chang, Y., Wu, X., Zhang, S.,
Piecewise Linear Units for Fast Self-Normalizing Neural Networks,
ICPR18(429-434)
IEEE DOI 1812
Biological neural networks, Random variables, Jacobian matrices, Force, Neurons, Pattern recognition, Piecewise linear units, self-normalizing property BibRef

Fan, L.,
Deep Epitome for Unravelling Generalized Hamming Network,
ICPR18(409-416)
IEEE DOI 1812
approximation theory, convolution, data visualisation, neural nets, optimisation, deep epitomes, Feature extraction BibRef

Wang, X.L.[Xiao-Long], Girshick, R.[Ross], Gupta, A.[Abhinav], He, K.M.[Kai-Ming],
Non-local Neural Networks,
CVPR18(7794-7803)
IEEE DOI 1812
Computational modeling, Neural networks, Convolution, Feedforward systems, Convolutional codes BibRef

Meng, Z.H.[Zi-Hang], Adluru, N.[Nagesh], Kim, H.W.J.[Hyun-Woo J.], Fung, G.[Glenn], Singh, V.[Vikas],
Efficient Relative Attribute Learning Using Graph Neural Networks,
ECCV18(XIV: 575-590).
Springer DOI 1810
BibRef

Haeusser, P., Mordvintsev, A., Cremers, D.,
Learning by Association: A Versatile Semi-Supervised Training Method for Neural Networks,
CVPR17(626-635)
IEEE DOI 1711
Data models, Dogs, Neural networks, Training, Training, data BibRef

Knöbelreiter, P., Reinbacher, C., Shekhovtsov, A., Pock, T.,
End-to-End Training of Hybrid CNN-CRF Models for Stereo,
CVPR17(1456-1465)
IEEE DOI 1711
Computational modeling, Correlation, Feature extraction, Neural networks, Robustness, Support vector machines, Training BibRef

Jiang, Z., Wang, Y., Davis, L., Andrews, W., Rozgic, V.,
Learning Discriminative Features via Label Consistent Neural Network,
WACV17(207-216)
IEEE DOI 1609
Biological neural networks, Convergence, Feature extraction, Linear programming, Neurons, Training, Videos BibRef

Hafiz, A.R., Al-Marzouqi, H.,
Efficient neural network training using curvelet features,
IVMSP16(1-5)
IEEE DOI 1608
Biological neural networks BibRef

Movshovitz-Attias, Y.[Yair], Yu, Q.[Qian], Stumpe, M.C.[Martin C.], Shet, V.[Vinay], Arnoud, S.[Sacha], Yatziv, L.[Liron],
Ontological supervision for fine grained classification of Street View storefronts,
CVPR15(1693-1702)
IEEE DOI 1510
BibRef

Kim, E.[Edward], Hannan, D.[Darryl], Kenyon, G.T.[Garrett T.],
Deep Sparse Coding for Invariant Multimodal Halle Berry Neurons,
CVPR18(1111-1120)
IEEE DOI 1812
Neurons, Encoding, Brain modeling, Biological system modeling, Biological neural networks, Visualization, Standards BibRef

Schultz, P.F.[Peter F.], Bettencourt, L.M.[Luis M.], Kenyon, G.T.[Garrett T.],
A symmetry-breaking generative model of a simple-cell/complex-cell hierarchy,
Southwest12(89-92).
IEEE DOI 1205
3 layer neural net BibRef

Hörnlein, T.[Thomas], Jähne, B.[Bernd],
Boosting Shift-Invariant Features,
DAGM09(121-130).
Springer DOI 0909
local convolutions followed by subsampling, then learning. BibRef

Szilágyi, S.M.[Sándor M.], Szilágyi, L.[László], Frigy, A.[Attila], Görög, L.K.[Levente K.], Benyó, Z.[Zoltán],
Unified Neural Network Based Pathologic Event Reconstruction Using Spatial Heart Model,
CIARP07(851-860).
Springer DOI 0711
BibRef

Wersing, H.[Heiko], Kirstein, S.[Stephan], Schneiders, B.[Bernd], Bauer-Wersing, U.[Ute], Körner, E.[Edgar],
Online Learning for Bootstrapping of Object Recognition and Localization in a Biologically Motivated Architecture,
CVS08(xx-yy).
Springer DOI 0805
BibRef

Kirstein, S.[Stephan], Wersing, H.[Heiko], Körner, E.[Edgar],
Rapid Online Learning of Objects in a Biologically Motivated Recognition Architecture,
DAGM05(301).
Springer DOI 0509
BibRef

Doulamis, N.D., Doulamis, A.D.,
Non-linear 3D rendering workload prediction based on a combined fuzzy-neural network architecture for grid computing applications,
ICIP03(III: 1069-1072).
IEEE DOI 0312
BibRef

Loh, A.W.K., Robey, M.C., West, G.A.W.,
IFOSART: a noise resistant neural network capable of incremental learning,
ICPR00(Vol II: 985-988).
IEEE DOI 0403
BibRef

Loh, A.W.K., Robey, M.C., West, G.A.W.,
Refining 3d models using a two-stage neural network-based iterative process,
ICPR02(I: 172-175).
IEEE DOI 0211
BibRef

Villela, P.R.[Patricia Rayon], Sossa Azuela, J.H.[J. Humberto],
Object Recognition by Indexing Using Neural Networks,
ICPR00(Vol II: 1001-1004).
IEEE DOI 0009
BibRef

Papamarkos, N.[Nikos],
Using Local Features in a Neural Network Based Gray-level Reduction Technique,
ICPR00(Vol III: 1025-1028).
IEEE DOI BibRef 0001 ICPR00(Vol III: 1037-1040).
IEEE DOI 0009
BibRef

Webb, A.R., Shannon, S.,
Adaptive Radial Basis Functions,
ICPR96(IV: 630-634).
IEEE DOI 9608
(Defence Res. Agency, UK) BibRef

Webb, A.R.,
Nonlinear Feature Extraction with Radial Basis Functions Using a Weighted Multidimensional Scaling Stress Measure,
ICPR96(IV: 635-639).
IEEE DOI 9608
(Defence Res. Agency, UK) BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Vision Transformers, ViT .


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