Deep learning on sparse manifolds for faster object segmentation
Abstract
We propose a new combination of deep belief networks and sparse manifold learning strategies for the 2D segmentation of non-rigid visual objects. With this novel combination, we aim to reduce the training and inference complexities while maintaining the accuracy of machine learning-based non-rigid segmentation methodologies. Typical non-rigid object segmentation methodologies divide the problem into a rigid detection followed by a non-rigid segmentation, where the low dimensionality of the rigid detection allows for a robust training (i.e., a training that does not require a vast amount of annotated images to estimate robust appearance and shape models) and a fast search process during inference. Therefore, it is desirable that the dimensionality of this rigid transformation space is as small as possible in order to enhance the advantages brought by the aforementioned division of the problem. In this paper, we propose the use of sparse manifolds to reduce the dimensionality of the rigid detection space. Furthermore, we propose the use of deep belief networks to allow for a training process that can produce robust appearance models without the need of large annotated training sets. We test our approach in the segmentation of the left ventricle of the heart from ultrasound images and lips from frontal face images. Our experiments show that the use of sparse manifolds and deep belief networks for the rigid detection stage leads to segmentation results that are as accurate as the current state of the art, but with lower search complexity and training processes that require a small amount of annotated training data.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The combination of sparse manifold learning and DBN in the rigid detection stage yielded performance as accurate as the state of the art but with lower training and search complexity.
“Their experiments demonstrated that the combination of sparse manifold learning and DBN in the rigid detection stage yielded a performance as accurate as the state of the art, but with lower training and search complexity.”
From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [135]
Cites
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601 · see citing works
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works
Kass, M., Witkin, A., & Terzopoulos, D. (1988) · International Journal of Computer Vision · doi:10.1007/bf00133570 · Cited by 17,165 · see citing works
Robust Real-Time Face Detection
Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097 · see citing works
Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004) · The Annals of Statistics · doi:10.1214/009053604000000067 · Cited by 9,357 · see citing works
Active Shape Models-Their Training and Application
Cootes, T. F., Taylor, C. J., Cooper, D. H., & Graham, J. (1995) · Computer Vision and Image Understanding · doi:10.1006/cviu.1995.1004 · Cited by 7,190 · see citing works
Atomic Decomposition by Basis Pursuit
Chen, S. S., Donoho, D. L., & Saunders, M. A. (1998) · SIAM Journal on Scientific Computing · doi:10.1137/s1064827596304010 · Cited by 6,734 · see citing works
Comparing images using the Hausdorff distance
Huttenlocher, D. P., Klanderman, G. A., & Rucklidge, W. J. (1993) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.232073 · Cited by 4,545 · see citing works
Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops · doi:10.1109/cvprw.2010.5543262 · Cited by 4,303 · see citing works
Shape modeling with front propagation: a level set approach
Malladi, R., Sethian, J. A., & Vemuri, B. C. (1995) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.368173 · Cited by 3,166 · see citing works
Sparse Approximate Solutions to Linear Systems
Natarajan, B. K. (1995) · SIAM Journal on Computing · doi:10.1137/s0097539792240406 · Cited by 2,866 · see citing works
The Geometry of Algorithms with Orthogonality Constraints
Edelman, A. S., Arias, T. A., & Smith, S. T. (1998) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/s0895479895290954 · Cited by 2,773 · see citing works
Large Margin Methods for Structured and Interdependent Output Variables
Tsochantaridis, I., Joachims, T., Hofmann, T., & Altün, Y. (2005) · MPG.PuRe (Max Planck Society) · Cited by 1,879 · see citing works
Sparse solutions to linear inverse problems with multiple measurement vectors
Cotter, S. F., Rao, B. D., Engan, K., & Kreutz-Delgado, K. (2005) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2005.849172 · Cited by 1,394 · see citing works
Robust Recovery of Signals From a Structured Union of Subspaces
Eldar, Y. C., & Mishali, M. (2009) · IEEE Transactions on Information Theory · doi:10.1109/tit.2009.2030471 · Cited by 1,062 · see citing works
Numerical Methods for Computing Angles Between Linear Subspaces
Björck, Å., & Golub, G. H. (1973) · Mathematics of Computation · doi:10.1090/s0025-5718-1973-0348991-3 · Cited by 870 · see citing works
Fast Global Minimization of the Active Contour/Snake Model
Bresson, X., Esedoḡlu, S., Vandergheynst, P., Thiran, J., & Osher, S. (2007) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-007-0002-0 · Cited by 857 · see citing works
Theoretical Results on Sparse Representations of Multiple-Measurement Vectors
Chen, J., & Huo, X. (2006) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2006.881263 · Cited by 833 · see citing works
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
Paragios, N., & Deriche, R. (2002) · International Journal of Computer Vision · doi:10.1023/a:1014080923068 · Cited by 754 · see citing works
Algorithms for simultaneous sparse approximation. Part II: Convex relaxation
Tropp, J. A. (2005) · Signal Processing · doi:10.1016/j.sigpro.2005.05.031 · Cited by 712 · see citing works
Beyond sliding windows: Object localization by efficient subwindow search
Lampert, C. H., Blaschko, M. B., & Hofmann, T. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587586 · Cited by 693 · see citing works
Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithm
Gorodnitsky, I. F., George, J. S., & Rao, B. D. (1995) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(95)00107-a · Cited by 567 · see citing works
Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., & Comaniciu, D. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.2004421 · Cited by 546 · see citing works
Semantic Segmentation with Second-Order Pooling
Carreira, J., Caseiro, R., Batista, J. P., & Sminchisescu, C. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33786-4_32 · Cited by 461 · see citing works
Learning structured prediction models
Taskar, B., Chatalbashev, V., Koller, D., & Guestrin, C. (2005) · International Conference on Machine Learning · doi:10.1145/1102351.1102464 · Cited by 444 · see citing works
Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure
Salakhutdinov, R., & Hinton, G. E. (2007) · Cited by 437 · see citing works
The Mathematics of Learning: Dealing With Data
Smale, S. (2005) · International Conference on Neural Networks and Brain · doi:10.1109/icnnb.2005.1614860 · Cited by 399 · see citing works
Riemannian Geometry of Grassmann Manifolds with a View on Algorithmic Computation
Absil, P.-A., Mahony, R. E., & Sepulchre, R. J. (2004) · Acta Applicandae Mathematicae · doi:10.1023/b:acap.0000013855.14971.91 · Cited by 393 · see citing works
Yao, J., Fidler, S., & Urtasun, R. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247739 · Cited by 373 · see citing works
Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI
Andreopoulos, A., & Tsotsos, J. K. (2008) · Medical Image Analysis · doi:10.1016/j.media.2007.12.003 · Cited by 362 · see citing works
Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation
Cremers, D., Osher, S., & Soatto, S. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-7533-5 · Cited by 352 · see citing works
Efficient Subwindow Search: A Branch and Bound Framework for Object Localization
Lampert, C. H., Blaschko, M. B., & Hofmann, T. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.144 · Cited by 342 · see citing works
A level set approach for shape-driven segmentation and tracking of the left ventricle
Paragios, N. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.814785 · Cited by 336 · see citing works
Mitchell, S., Lelieveldt, B. P. F., van der Geest, R. J., Bosch, H. G., Reiver, J. H. C., & Milan Sonka, A. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.925294 · Cited by 330 · see citing works
Subset selection in noise based on diversity measure minimization
Rao, B. D., Engan, K., Cotter, S. F., Palmer, J. A., & Kreutz-Delgado, K. (2003) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2002.808076 · Cited by 324 · see citing works
What, Where and How Many? Combining Object Detectors and CRFs
Ladický, Ľ., Sturgess, P., Alahari, K., Russell, C., & Torr, P. H. S. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15561-1_31 · Cited by 312 · see citing works
Graph Cut Based Inference with Co-occurrence Statistics
Ladický, Ľ., Russell, C., Kohli, P., & Torr, P. H. S. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15555-0_18 · Cited by 307 · see citing works
Automatic segmentation of echocardiographic sequences by active appearance motion models
Bosch, J. G., Mitchell, S., Lelieveldt, B. P. F., Nijland, F., Otto Kamp, B., Milan Sonka, A., & Reiber, J. H. C. (2002) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2002.806427 · Cited by 296 · see citing works
Semantic segmentation using regions and parts
Arbeláez, P., Hariharan, B., Gu, C., Gupta, S., Bourdev, L. D., & Malik, J. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6248077 · Cited by 274 · see citing works
Towards robust and effective shape modeling: Sparse shape composition
Zhang, S., Zhan, Y., Dewan, M., Huang, J., Metaxas, D. N., & Zhou, X. S. (2012) · Medical Image Analysis · doi:10.1016/j.media.2011.08.004 · Cited by 249 · see citing works
Segmentation of intravascular ultrasound images: a knowledge-based approach
Milan Sonka, A., Zhang, X., Siebes, M., Bissing, M. S., DeJong, S. C., Collins, S. M., & McKay, C. R. (1995) · IEEE Transactions on Medical Imaging · doi:10.1109/42.476113 · Cited by 235 · see citing works
Maximum likelihood segmentation of ultrasound images with Rayleigh distribution
Sarti, A., Corsi, C., Mazzini, E., & Lamberti, C. (2005) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2005.1504017 · Cited by 213 · see citing works
Carneiro, G., Georgescu, B., Good, S., & Comaniciu, D. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.928917 · Cited by 205 · see citing works
Carneiro, G., Nascimento, J. C., & Freitas, A. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2169273 · Cited by 202 · see citing works
Variational B-Spline Level-Set: A Linear Filtering Approach for Fast Deformable Model Evolution
Bernard, O., Friboulet, D. P., Thévenaz, P., & Unser, M. A. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2017343 · Cited by 172 · see citing works
Lynch, M., Ghita, O., & Whelan, P. F. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2007.904681 · Cited by 163 · see citing works
Harmony potentials for joint classification and segmentation
Gonfaus, J. M., Boix, X., van de Weijer, J., Bagdanov, A. D., Serrat, J., & Gonzàlez, J. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5540048 · Cited by 154 · see citing works
Forward sequential algorithms for best basis selection
Cotter, S. F., Rao, B. D., Kreutz-Delgado, K., & Adler, J. (1999) · IEE Proceedings - Vision Image and Signal Processing · doi:10.1049/ip-vis:19990445 · Cited by 150 · see citing works
Jacob, G., Noble, J. A., Behrenbruch, C., Kelion, A. D., & Banning, A. P. (2002) · IEEE Transactions on Medical Imaging · doi:10.1109/42.996341 · Cited by 142 · see citing works
A unified framework for atlas matching using active appearance models
Cootes, Beeston, Edwards, & Taylor (1999) · Information Processing in Medical Imaging · Cited by 141 · see citing works
Combinative multi-scale level set framework for echocardiographic image segmentation
Lin, N., Yu, W., & Duncan, J. S. (2003) · Medical Image Analysis · doi:10.1016/s1361-8415(03)00035-5 · Cited by 139 · see citing works
A Unified Framework for Atlas Matching Using Active Appearance Models
Cootes, T. F., Beeston, C., Edwards, G. J., & Taylor, C. (1999) · Lecture notes in computer science · doi:10.1007/3-540-48714-x_24 · Cited by 131 · see citing works
Fast orthogonal least squares algorithm for efficient subset model selection
Chen, S., & Wigger, J. (1995) · IEEE Transactions on Signal Processing · doi:10.1109/78.398734 · Cited by 128 · see citing works
Intrinsic dimensionality estimation with optimally topology preserving maps
Brüske, J., & Sommer, G. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.682189 · Cited by 125 · see citing works
Database-Guided Segmentation of Anatomical Structures with Complex Appearance
Georgescu, B., Zhou, X. S., Comaniciu, D., & Gupta, A. K. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2005.119 · Cited by 122 · see citing works
Left ventricular volume estimation for real-time three-dimensional echocardiography
Corsi, C., Saracino, G., Sarti, A., & Lamberti, C. (2002) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2002.804418 · Cited by 115 · see citing works
An information fusion framework for robust shape tracking
Zhou, X. S., Gupta, A. K., & Comaniciu, D. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.3 · Cited by 112 · see citing works
Object segmentation by alignment of poselet activations to image contours
Brox, T., Bourdev, L. D., Maji, S., & Malik, J. (2011) · doi:10.1109/cvpr.2011.5995659 · Cited by 101 · see citing works
Kernelized structural SVM learning for supervised object segmentation
Bertelli, L., Yu, T., Vu, D., & Gokturk, S. B. (2011) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2011.5995597 · Cited by 100 · see citing works
An Effective Algorithm for Extracting Serial Endocardial Borders from 2-Dimensional Echocardiograms
Zhang, L., & Geiser, E. A. (1984) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1984.325359 · Cited by 93 · see citing works
Registration-assisted segmentation of real-time 3-D echocardiographic data using deformable models
Zagrodsky, V., Walimbe, V., Castro‐Pareja, C. R., Qin, J. X., Song, J., & Shekhar, R. (2005) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2005.852057 · Cited by 79 · see citing works
Mignotte, M., Meunier, J., & Tardif, J. (2001) · Pattern Analysis and Applications · doi:10.1007/pl00010988 · Cited by 74 · see citing works
Robust Shape Tracking With Multiple Models in Ultrasound Images
Nascimento, J. C., & Marques, J. S. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.915552 · Cited by 73 · see citing works
Essential Matrix Estimation Using Gauss-Newton Iterations on a Manifold
Uwe Helmke, B., Hüper, K., Lee, P. Y., & Moore, J. B. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-006-0005-0 · Cited by 70 · see citing works
Space-time segmentation using level set active contours applied to myocardial gated SPECT
Debreuve, É., Barlaud, M., Aubert, G., Laurette, I., & Darcourt, J. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.932748 · Cited by 70 · see citing works
Chen, T., Babb, J. S., Kellman, P., Axel, L., & Kim, D. C. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.918327 · Cited by 69 · see citing works
Active Scheduling of Organ Detection and Segmentation in Whole-Body Medical Images
Zhan, Y., Zhou, X. S., Peng, Z., & Krishnan, A. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85988-8_38 · Cited by 68 · see citing works
Object detection and segmentation from joint embedding of parts and pixels
Maire, M., Yu, S. X., & Perona, P. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126490 · Cited by 53 · see citing works
Learning-based ventricle detection from cardiac MR and CT images
Weng, J., Singh, A. P., & Chiu, M. (1997) · IEEE Transactions on Medical Imaging · doi:10.1109/42.611346 · Cited by 47 · see citing works
Real-time segmentation by Active Geometric Functions
Duan, Q., Angelini, E. D., & Laine, A. F. (2009) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2009.09.001 · Cited by 47 · see citing works
Newton-like methods for numerical optimization on manifolds
Hüper, K., & Trumpf, J. (2005) · 2018 52nd Asilomar Conference on Signals, Systems, and Computers · doi:10.1109/acssc.2004.1399106 · Cited by 46 · see citing works
Carneiro, G., Nascimento, J. C., & Freitas, A. (2010) · IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI) · doi:10.1109/isbi.2010.5490181 · Cited by 35 · see citing works
A boosting regression approach to medical anatomy detection
Zhou, S. K., Zhou, J., & Comaniciu, D. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383139 · Cited by 34 · see citing works
Carneiro, G., & Nascimento, J. C. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5540013 · Cited by 32 · see citing works
Bernard, O., Touil, B., Gelas, A., Prost, R., & Friboulet, D. P. (2007) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2007.4379270 · Cited by 23 · see citing works
Manifold Learning for Object Tracking with Multiple Motion Dynamics
Nascimento, J. C., & Silva, J. G. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15558-1_13 · Cited by 17 · see citing works
Reiber, J. H. C., Viddeleer, A. R., Koning, G., Schalij, M. J., & Lange, P. (1996) · International journal of cardiac imaging · doi:10.1007/bf01880736 · Cited by 14 · see citing works
Manifold Learning for Object Tracking With Multiple Nonlinear Models
Nascimento, J. C., Silva, J. G., Marques, J. S., & Lemos, J. M. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2303652 · Cited by 9 · see citing works
Top-Down Segmentation of Non-rigid Visual Objects Using Derivative-Based Search on Sparse Manifolds
Nascimento, J. C., & Carneiro, G. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.256 · Cited by 4 · see citing works
Flagged first, then by citations
1 of the 81 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Deep Learning for Cardiac Image Segmentation: A Review
Chen, C., Qin, C., Qiu, H., Tarroni, G., Duan, J., Bai, W., & Rueckert, D. (2020) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2020.00025 · Cited by 915 · see citing works
Deep Learning in Medical Ultrasound Analysis: A Review
Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S. X., Ni, D., & Wang, T. (2019) · Engineering · doi:10.1016/j.eng.2018.11.020 · Cited by 798 · see citing works
Deep learning for cardiac image segmentation: A review
Chen, C., Qin, C., Qiu, H., Tarroni, G., Duan, J., Bai, W., & Rueckert, D. (2019) · City Research Online (City University London) · Cited by 358 · see citing works
A survey of 2D shape representation: Methods, evaluations, and future research directions
Kurnianggoro, L., Wahyono, W., & Jo, K.-H. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.02.093 · Cited by 51 · see citing works
Nascimento, J. C., & Carneiro, G. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2922959 · Cited by 14 · see citing works
Automated Recognition of Ultrasound Cardiac Views Based on Deep Learning with Graph Constraint
Gao, Y., Zhu, Y., Liu, B., Hu, Y., Yu, G., & Guo, Y. (2021) · Diagnostics · doi:10.3390/diagnostics11071177 · Cited by 12 · see citing works
Exploiting Uncertainty of Deep Neural Networks for Improving Segmentation Accuracy in MRI Images
Norouzi, A., Emami, A., Najarian, K., Karimi, N., Samavi, S., & Soroushmehr, S. M. R. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682530 · Cited by 11 · see citing works
Semi-supervised self-growing generative adversarial networks for image recognition
Xu, Z., Wang, H., & Yang, Y. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09602-1 · Cited by 9 · see citing works
A variational image segmentation method exploring both intensity means and texture patterns
Li, Y., Zhao, Q., Feng, X., Wang, W., Zhang, R., & Yan, A. (2019) · Signal Processing Image Communication · doi:10.1016/j.image.2019.05.002 · Cited by 5 · see citing works
Zhu, X., Xia, W., Bao, Z., Zhong, Y., Fang, Y., Yang, F., Gu, X., Ye, J., & Huang, W. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.618481 · Cited by 3 · see citing works
3D AUTOCUT: a 3D segmentation algorithm based on cellular automata
Neto, E. C., Cortez, P. C., Rodrigues, V. E., Cavalcante, T. S., & Valente, I. R. S. (2017) · Electronics Letters · doi:10.1049/el.2017.2776 · Cited by 2 · see citing works
Generation of Random Fields for Image Segmentation Techniques: A Review
Pemula, R., Kumar, S. V., & Nagaraju, C. H. (2022) · International Journal of Image and Graphics · doi:10.1142/s0219467823500225 · Cited by 2 · see citing works
Hash based manifold learning technique to generating random fields for image segmentation
Pemula, R., & Raju, C. N. (2018) · Cluster Computing · doi:10.1007/s10586-018-2433-3 · Cited by 2 · see citing works
Man-made target detection method based on the red-edge spectral information in natural background
Li, T., Li, J., Xiao, J., Liu, X., & Tang, H. (2023) · Applied Optics and Photonics China (AOPC) · doi:10.1117/12.2652038 · Cited by 2 · see citing works
Norouzi, A., Emami, A., Soroushmehr, S. M. R., Karimi, N., Samavi, S., & Najarian, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.00406
Online Relational Manifold Learning for Multiview Segmentation in Echocardiography
Belous, G., Busch, A. W., Rowlands, D. D., & Gao, Y. (2018) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2018.8615773
Semi-Supervised Self-Growing Generative Adversarial Networks for Image RecognitionPreprint
Wang, H., Xu, Z., Xu, J., An, W., Lei Zhang, A., & Dai, Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.03850
- Authors
- Nascimento, J. C., & Carneiro, G.
- Affiliations
- Instituto Superior Técnico (PT)
- The University of Adelaide (AU)
- Venue
- IEEE Transactions on Image Processing
- Published
- 2017-10
- Volume
- 26
- Issue
- 10
- Pages
- 4978-4990
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2734523407
- Cited by
- 17
- References
- 81
- Citations by year
- 2023: 1 · 2022: 1 · 2021: 2 · 2020: 2 · 2019: 6 · 2018: 4 · 2017: 1
- FWCI
- 1.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Image Processing (publishedVersion)
- PubMed (publishedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Funders
- FCT (PEst-OE/EEI/l.A0009/2013, PTDC/EEIPRO/042612014 (SPARSIS))
- Australian Research Council’s Discovery Projects funding scheme (DP140102794)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- deep belief network, appearance model, left ventricle segmentation, ultrasound images, lip segmentation, frontal face images
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
