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Learning motion-difference features using Gaussian restricted Boltzmann machines for efficient human action recognition

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Modular hierarchical feature learning with deep neural networks for face verification

Chen, X., Xiao, B., Wang, C., Cai, X., Lv, Z., & Shi, Y. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738761 · Cited by 6 · see citing works

Multiscale Fields of PatternsPreprint

Felzenszwalb, P. F., & Oberlin, J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.0924 · Cited by 6 · see citing works

PerSnake: a real-time pedestrian instance segmentation network using contour representation

Guo, Z., Wang, X., Zhang, Z., Huang, Y., & Ma, X. (2023) · Machine Vision and Applications · doi:10.1007/s00138-023-01419-w · Cited by 4 · see citing works

Deep Learning

Okatani, T. (2014) · The Journal of The Institute of Image Information and Television Engineers · doi:10.3169/itej.68.466 · Cited by 4 · see citing works

Grouping Active Contour Fragments for Object Recognition

Zheng, W., Song, S., Chang, H., & Chen, X. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37331-2_22 · Cited by 4 · see citing works

Modeling object appearance using Context-Conditioned Component Analysis

Turmukhambetov, D., Campbell, N. D. F., Prince, S. J. D., & Kautz, J. (2015) · 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.2015.7299043 · Cited by 3 · see citing works

Analysis of contour evolution methods for segmentation of medical images

Divya, S., & B, J. K. (2015) · International Conference on Innovations in Information, Embedded and Communication Systems · doi:10.1109/iciiecs.2015.7192938 · Cited by 2 · see citing works

Modelling supra-classical logic in a Boltzmann neural network: I representation

Blanchette, G., Robins, A. V., & Labuschagne, W. A. (2021) · Journal of Logic and Computation · doi:10.1093/logcom/exab054 · Cited by 2 · see citing works

Transformation of Markov Random Fields for marginal distribution estimation

Saito, M., & Okatani, T. (2015) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2015.7298680 · Cited by 5 · see citing works

Shape-Based Pedestrian Segmentation in Still Images

Jacques, J. C. S., & Musse, S. R. (2015) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2015.25 · Cited by 2 · see citing works

A hand shape recognizer from simple sketches

Zhu, X., Sang, R., Jia, X., & Wong, K.-Y. K. (2013) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1109/ivcnz.2013.6727004 · Cited by 1 · see citing works

Deep conditional neural network for image segmentation

Wang, Q., Yuan, C., & Liu, Y. (2016) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2016.7552924 · Cited by 1 · see citing works

Tracking and detecting objects in image sequence

Wang, L. (2016) · doi:10.32657/10356/65878 · Cited by 1 · see citing works

Closed-Loop Deep Vision

Carneiro, G., Liao, Z., & Chin, T.-J. (2013) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2013.6691492

Interactions entre Rang et Parcimonie en Estimation Pénalisée, et Détection d'Objets Structurés

Savalle, P.-A. (2014) · HAL · doi:10.70675/c0c462ddzfc3az4a32za3caz2440bc09e854

Contour-HOG: A Stub Feature based Level Set Method for Learning Object Contour

Yang, Z. Q., Kong, Y., & Fu, Y. (2012) · British Machine Vision Conference (BMVC) · doi:10.5244/c.26.15

International Journal of Computer Vision manuscript No. (will be inserted by the editor) The Shape Boltzmann Machine A Strong Model of Object Shape

Eslami, S. M. A., Heess, N., Williams, C. K. I., & Winn, J. M. (2015)

Multi-Scale Shape Boltzmann Machine: A Shape Model Based on Deep Learning Method.

Yang, J., Zhang, X., & Wang, X. (2017) · IIKI

An Object Shape Completion Using the Shape Boltzmann Machine

(2013)

Centered convolutional deep Boltzmann machine for 2D shape modeling

Yang, J., Liu, S., & Wang, X. (2021) · Personal and Ubiquitous Computing · doi:10.1007/s00779-020-01487-z · Cited by 3 · see citing works

Disjunctive Normal Shape Boltzmann Machine

Erdil, E., Mesadi, F., Taşdizen, T., & Çetin, M. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952578 · Cited by 6 · see citing works