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Journal articles on the topic 'Overcomplete pattern recognition'

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Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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1

Tay, Nuo Wi, Chu Kiong Loo, and Mitja Peruš. "Face Recognition with Quantum Associative Networks Using Overcomplete Gabor Wavelet." Cognitive Computation 2, no. 4 (May 29, 2010): 297–302. http://dx.doi.org/10.1007/s12559-010-9047-2.

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2

Luo, Jiebo, and Matthew Boutell. "Natural scene classification using overcomplete ICA." Pattern Recognition 38, no. 10 (October 2005): 1507–19. http://dx.doi.org/10.1016/j.patcog.2005.02.015.

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3

NAKACHI, Takayuki, Yukihiro BANDOH, and Hitoshi KIYA. "Secure Overcomplete Dictionary Learning for Sparse Representation." IEICE Transactions on Information and Systems E103.D, no. 1 (January 1, 2020): 50–58. http://dx.doi.org/10.1587/transinf.2019mup0009.

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4

Li, Xin. "Scalable video compression via overcomplete motion compensated wavelet coding." Signal Processing: Image Communication 19, no. 7 (August 2004): 637–51. http://dx.doi.org/10.1016/j.image.2004.05.006.

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5

Lin, Leping, Fang Liu, and Licheng Jiao. "Compressed sensing by collaborative reconstruction on overcomplete dictionary." Signal Processing 103 (October 2014): 92–102. http://dx.doi.org/10.1016/j.sigpro.2013.11.039.

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6

WANG, Zhe, Siwei LUO, and Liang WANG. "A Fast Algorithm for Learning the Overcomplete Image Prior." IEICE Transactions on Information and Systems E93-D, no. 2 (2010): 403–6. http://dx.doi.org/10.1587/transinf.e93.d.403.

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7

Han, Lili, Shujuan Li, Pengxin Ren, and Dingdan Xue. "Block cosparsity overcomplete learning transform image segmentation algorithm based on burr model." IET Image Processing 14, no. 10 (August 21, 2020): 2074–80. http://dx.doi.org/10.1049/iet-ipr.2019.1212.

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8

Bouleux, Guillaume, and Rémy Boyer. "Sparse-based estimation performance for partially known overcomplete large-systems." Signal Processing 139 (October 2017): 70–74. http://dx.doi.org/10.1016/j.sigpro.2017.04.010.

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9

Seran, Vidhya, and Lisimachos P. Kondi. "Drift controlled scalable wavelet based video coding in the overcomplete discrete wavelet transform domain." Signal Processing: Image Communication 22, no. 4 (April 2007): 389–402. http://dx.doi.org/10.1016/j.image.2007.01.003.

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10

Andreopoulos, Yiannis, Adrian Munteanu, Geert Van der Auwera, Jan Cornelis, and Peter Schelkens. "Single-rate calculation of overcomplete discrete wavelet transforms for scalable coding applications." Signal Processing 85, no. 6 (June 2005): 1103–24. http://dx.doi.org/10.1016/j.sigpro.2005.01.006.

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11

Deng, Guang, David B. H. Tay, and Slaven Marusic. "A signal denoising algorithm based on overcomplete wavelet representations and Gaussian models." Signal Processing 87, no. 5 (May 2007): 866–76. http://dx.doi.org/10.1016/j.sigpro.2006.08.008.

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12

Manjón, Jose V., Pierrick Coupé, Parnesh Raniga, Ying Xia, Patricia Desmond, Jurgen Fripp, and Olivier Salvado. "MRI white matter lesion segmentation using an ensemble of neural networks and overcomplete patch-based voting." Computerized Medical Imaging and Graphics 69 (November 2018): 43–51. http://dx.doi.org/10.1016/j.compmedimag.2018.05.001.

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13

Mozaffari, Behzad, and Mohammad A. Tinati. "An adaptive speech source separation algorithm under overcomplete-cases using Laplacian mixture modeling for mixture matrix estimation by adaptive EM-type algorithm in wavelet packet domain." International Journal of Speech Technology 11, no. 1 (March 2008): 33–42. http://dx.doi.org/10.1007/s10772-009-9033-9.

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14

Du, Jing, Zishuo Li, and Chao Wang. "Automated Road Damage Recognition based on the Sparse Coding Analysis of Vehicle Vibrations." MATEC Web of Conferences 271 (2019): 08006. http://dx.doi.org/10.1051/matecconf/201927108006.

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Abstract:
Road pavement damage inspection is a critical yet challenging task. At present, road pavement damage inspection is usually done by DOTs using a manual process. Another emerging method of inspection is via the use of sensors, such as the use of LiDAR. This study proposes an automated road damage recognition method via the Sparse Coding analysis of vehicle vibrations. Sparse Coding is a class of unsupervised methods that learn data patterns based on extracted overcomplete bases. Unlike frequency domain-based analysis, e.g. Spectral Analysis, Sparse Coding analysis preserves the temporal information of the vehicle vibration that contains important patterns related to road pavement damage. A preliminary study was performed with vehicle vibration data collected in College Station, Texas. Results confirm the feasibility of the proposed method in automated road pavement damage recognition. More data points should be collected in the future to further benchmark the effectiveness of the proposed method.
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15

Murdock, Calvin, George Cazenavette, and Simon Lucey. "Reframing Neural Networks: Deep Structure in Overcomplete Representations." IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 1. http://dx.doi.org/10.1109/tpami.2022.3149445.

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