From post

Pixel Features for Self-organizing Map Based Detection of Foreground Objects in Dynamic Environments.

, , , , и . SOCO-CISIS-ICEUTE, том 527 из Advances in Intelligent Systems and Computing, стр. 247-255. (2016)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed.

 

Другие публикации лиц с тем же именем

Neural controller for PTZ cameras based on nonpanoramic foreground detection., , , , и . IJCNN, стр. 404-411. IEEE, (2017)Optimization of Convolutional Neural Network Ensemble Classifiers by Genetic Algorithms., , , и . IWANN (2), том 11507 из Lecture Notes in Computer Science, стр. 163-173. Springer, (2019)A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica., , , , , , и . Medical Biol. Eng. Comput., 60 (4): 1159-1175 (2022)Foreground detection by probabilistic modeling of the features discovered by stacked denoising autoencoders in noisy video sequences., , , , и . Pattern Recognit. Lett., (2019)The Effect of Noise and Brightness on Convolutional Deep Neural Networks., , , и . ICPR Workshops (6), том 12666 из Lecture Notes in Computer Science, стр. 639-654. Springer, (2020)Pixel Features for Self-organizing Map Based Detection of Foreground Objects in Dynamic Environments., , , , и . SOCO-CISIS-ICEUTE, том 527 из Advances in Intelligent Systems and Computing, стр. 247-255. (2016)Foreground Detection Enhancement Using Pearson Correlation Filtering., , , и . IPMU (3), том 855 из Communications in Computer and Information Science, стр. 417-428. Springer, (2018)CADICA: A new dataset for coronary artery disease detection by using invasive coronary angiography., , , , , , и . Expert Syst. J. Knowl. Eng., (декабря 2024)Histopathological image analysis for breast cancer diagnosis by ensembles of convolutional neural networks and genetic algorithms., , , и . IJCNN, стр. 1-8. IEEE, (2021)Test time augmentation by regular shifting for deep denoising autoencoder networks., , , и . IJCNN, стр. 1-7. IEEE, (2021)