An efficient local binary pattern based plantar pressure optical sensor image classification using convolutional neural networks
Tài liệu tham khảo
Drazan, 2018, Simple implantable wireless sensor platform to measure pressure and force, Med. Eng. Phys., 59, 81, 10.1016/j.medengphy.2018.06.006
Chaudhury, 2016, An image contrast-based pressure sensor, Sens. Actuators A Phys., 245, 63, 10.1016/j.sna.2016.04.057
Wang, 2018, Flexible pressure sensor based on PVDF nanofiber, Sens. Actuators A Phys., 280, 319, 10.1016/j.sna.2018.07.057
Bhatta, 2012, Rapid detection of foot-and-Mouth disease virus with optical microchip sensors, Procedia Chem., 6, 2, 10.1016/j.proche.2012.10.124
Li, 2017, Convolutional neural network based clustering and manifold learning method for diabetic plantar pressure imaging dataset, J. Med. Imaging Health Inform., 7, 639, 10.1166/jmihi.2017.2082
Cao, 2018, Diabetic plantar pressure analysis using image fusion, Multimed. Tools Appl., 10.1007/s11042-018-6269-x
Wang, 2018, Histogram of oriented gradient based plantar pressure image feature extraction and classification employing fuzzy support vector machine, J. Med. Imaging Health Inform., 8, 842, 10.1166/jmihi.2018.2310
Wang, 2017, Case-based reasoning for product style construction and fuzzy analytic hierarchy process evaluation modeling using consumers linguistic variables, IEEE Access, 5, 4900, 10.1109/ACCESS.2017.2677950
Buldt, 2018, Foot posture is associated with plantar pressure during gait: a comparison of normal, planus and cavus feet, Gait Posture, 62, 235, 10.1016/j.gaitpost.2018.03.005
Stewart, 2018, Region-specific foot pain and plantar pressure in people with rheumatoid arthritis: a cross-sectional study, Clin. Biomech., 55, 14, 10.1016/j.clinbiomech.2018.04.002
Earnest, 2019, shoes and shoe modifications, 229
Azevedo, 2017, Plantar pressure asymmetry and risk of stress injuries in the foot of young soccer players, Phys. Ther. Sport., 24, 39, 10.1016/j.ptsp.2016.10.001
Torp, 2019, External feedback during walking improves measures of plantar pressure in individuals with chronic ankle instability, Gait Posture, 67, 236, 10.1016/j.gaitpost.2018.10.023
Hafer, 2013, Reliability of plantar pressure platforms, Gait Posture, 38, 544, 10.1016/j.gaitpost.2013.01.028
Zhao, 2018, A review of image set classification, Neurocomputing
Haralick, 1979, Statistical and structural approaches to texture, Proc. IEEE, 67, pp. 786, 10.1109/PROC.1979.11328
Bibicu, 2013, Thyroid nodule recognition based on feature selection and pixel classification methods, J. Digit. Imaging, 26, 119, 10.1007/s10278-012-9475-5
Hati, 2017, An image texture insensitive method for saliency detection, J. Vis. Commun. Image Represent., 43, 212, 10.1016/j.jvcir.2017.01.007
Seo, 2019, Hierarchical convolutional neural networks for fashion image classification, Expert Syst. Appl., 116, 328, 10.1016/j.eswa.2018.09.022
Timo Ojala, Matti Pietikäinen, Topi Mäenpää, Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, 4, 7, 971-987, https://doi.org/10.1109/TPAMI.2002.1017623.
Liu, 2017, Fusion of color histogram and LBP-based features for texture image retrieval and classification, Inf. Sci. (Ny), 390, 95, 10.1016/j.ins.2017.01.025
Mahale, 2017, Image inconsistency detection using local binary pattern (LBP), Procedia Comput. Sci., 115, 501, 10.1016/j.procs.2017.09.097
Yuan, 2018, Polarization image texture feature extraction algorithm based on CS-LBP operator, Procedia Comput. Sci., 131, 295, 10.1016/j.procs.2018.04.167
Yang, 2013, A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image, Neurocomputing, 120, 365, 10.1016/j.neucom.2012.10.032
Öztürk, 2018, Application of feature extraction and classification methods for histopathological image using GLCM, LBP, LBGLCM, GLRLM and SFTA, Procedia Comput. Sci., 132, 40, 10.1016/j.procs.2018.05.057
Ricardo Backes, 2017, LBP maps for improving fractal based texture classification, Neurocomputing, 266, 1, 10.1016/j.neucom.2017.05.020
Lei, 2017, A novel approach for cirrhosis recognition via improved LBP algorithm and dictionary learning, Biomed. Signal Process. Control, 38, 281, 10.1016/j.bspc.2017.06.014
Lahiani, 2018, Hand gesture recognition method based on HOG-LBP features for mobile devices, Procedia Comput. Sci., 126, 254, 10.1016/j.procs.2018.07.259
Sharma, 2018, An analysis of convolutional neural networks for image classification, Procedia Comput. Sci., 132, 377, 10.1016/j.procs.2018.05.198
Traore, 2018, Deep convolution neural network for image recognition, Ecol. Inform., 48, 257, 10.1016/j.ecoinf.2018.10.002
Paoletti, 2018, A new deep convolutional neural network for fast hyperspectral image classification, Isprs J. Photogramm. Remote. Sens., 145, 120, 10.1016/j.isprsjprs.2017.11.021
Wan, 2018, Deep convolutional neural networks for diabetic retinopathy detection by image classification, Comput. Electr. Eng., 72, 274, 10.1016/j.compeleceng.2018.07.042
Gotlieb, 1990, Texture descriptors based on co-occurrence matrices, Comput. Vision Graph. Image Process., 51, 70, 10.1016/S0734-189X(05)80063-5
Kaewchote, 2018, Image recognition method using Local Binary Pattern and the Random forest classifier to count post larvae shrimp, Agric. Nat. Resour., 52, 371
Mäenpää, 2004, Classification with color and texture: jointly or separately?, Pattern Recognit., 37, 1629, 10.1016/j.patcog.2003.11.011
Digge, 2018, Expanded age indication for Ponseti method for correction of congenital idiopathic talipes equinovarus: a systematic review, J. Foot Ankle Surg., 57, 155, 10.1053/j.jfas.2017.08.015
Singh, 2016, Efficient technique for rice grain classification using back-propagation neural network and wavelet decomposition, Iet Comput. Vis., 10, 780, 10.1049/iet-cvi.2015.0486
Zheng, 2018, Data augmentation on mice liver cirrhosis microscopic images employing convolutional neural networks and support vector machine, J. Ambient Intell. Humaniz. Comput., 10.1007/s12652-018-0951-8
Álvar-Ginés, 2018, Noise robust and rotation invariant framework for texture analysis and classification, Appl. Math. Comput., 335, 124
Rakesh, 2015, Self-calibration of dead reckoning sensor for skid-steer mobile robot localization using neuro-fuzzy systems, Proc. ITITS 2015, Information Technology and Intelligent Transportation Systems