Abstract
Aiming at the research of real-time data transmission and multi-scale image decomposition of embedded optical sensor array, the principle, method and fusion strategy of multi-sensor image fusion are studied comprehensively, thoroughly and systematically by combining the imaging characteristics of source image with multi-scale geometric analysis tools using machine learning algorithm. A new quality scalable video image coding framework is also proposed in this paper, which is implemented by a multi-scale online dictionary learning algorithm based on structured sparse video signals. For the purpose of different types of images and image fusion, a new high quality scalable video image coding framework based on machine learning algorithm is proposed on the basis of comprehensive analysis of prior information such as imaging mechanism of image sensor and imaging characteristics of source image. A multi-scale online dictionary learning algorithm based on machine learning for sparse video signal structure is proposed. Through the hierarchical structure of wavelet decomposition, the searching domain of online learning is optimized to a hierarchical sparse block, and its sparse representation coefficients are obtained by using machine learning sparse coding idea. The real-time data transmission of embedded optical sensor array based on machine learning and multi-scale image decomposition algorithm proposed in this paper have good fusion performance, which is of great significance for further research and engineering application of image fusion technology.
Similar content being viewed by others
References
Al-Ariki HD, Swamy MN (2017) A survey and analysis of multipath routing protocols in wireless multimedia sensor networks [J]. Wirel Netw 23(6):1823–1835
Arun KS, Govindan VK (2015) Optimizing visual dictionaries for effective image retrieval [J]. Int J Multimed Inf Retr 4(3):165–185
Barmpoutis A (2013) Tensor body: real-time reconstruction of the human body and avatar synthesis from RGB-D [J]. IEEE Trans Cybern 43(5):1347–1356
Cho SG, Yoshikawa M, Ding M, Takamatsu J, Ogasawara T (2019) Machine-learning-based hand motion recognition system by measuring forearm deformation with a distance sensor array [J]. Int J Intell Robot Applic 3(4):418–429
Choi, Lee, Jun (2015) SPEED-MAC: speedy and energy efficient data delivery MAC protocol for real-time sensor network applications [J]. Wirel Netw 21(3):883–898
Goh H, Thome N, Cord M et al (2017) Learning deep hierarchical visual feature coding [J]. IEEE Trans Neural Netw Learn Syst 25(12):2212–2225
Han M, Liu X, Pu H, Zhao L, Wang K, Xu D (2020) Real-time online optimal control of current-fed dual active bridges based on machine learning [J]. J Power Electron 20(1):43–52
Li L, Li S, Fu Y (2014) Learning low-rank and discriminative dictionary for image classification [J]. Image Vis Comput 32(10):814–823
Li F, Sheng J, Zhang SY (2017) Laplacian sparse dictionary learning for image classification based on sparse representation [J]. Front Inf Technol Electron Eng 18(11):1795–1805
Liu Q, Chang Y, Jia X (2013) A hybrid method of CSMA/CA and TDMA for real-time data aggregation in wireless sensor networks [J]. Comput Commun 36(3):269–278
Mertens F, Lobanov A (2015) Wavelet-based decomposition and analysis of structural patterns in astronomical images [J]. Astron Astrophys 574(10):10782–10789
Mohammadi MR, Fatemizadeh E, Mahoor MH (2014) PCA-based dictionary building for accurate facial expression recognition via sparse representation [J]. J Vis Commun Image Represent 25(5):1082–1092
Passieux JC, Bugarin F, David C, Périé JN, Robert L (2015) Multiscale displacement field measurement using digital image correlation: application to the identification of elastic properties [J]. Exp Mech 55(1):121–137
Rezaie-Balf M, Kisi O, Chua LHC (2019) Application of ensemble empirical mode decomposition based on machine learning methodologies in forecasting monthly pan evaporation [J]. Nord Hydrol 50(1–2):498–516
SnowFort: An Open Source Wireless Sensor Network for Data Analytics in Infrastructure and Environmental Monitoring [J]. IEEE Sensors Journal, 2014, 14(12):4253–4263.
Srinivas M, Naidu RR, Sastry CS, Mohan CK (2015) Content based medical image retrieval using dictionary learning [J]. Neurocomputing 168(C):880–895
Sun Y, Sudo K, Taniguchi Y (2016) Visual concept detection of web images based on group sparse ensemble learning [J]. Multimed Tools Appl 75(3):1409–1425
Wang D, Kong S (2014) A classification-oriented dictionary learning model: explicitly learning the particularity and commonality across categories [J]. Pattern Recogn 47(2):885–898
Wen B, Ravishankar S, Bresler Y (2015) Structured Overcomplete Sparsifying transform learning with convergence guarantees and applications [J]. Int J Comput Vis 114(2–3):137–167
Xiang S, Meng G, Wang Y, Pan C, Zhang C (2015) Image Deblurring with coupled dictionary learning [J]. Int J Comput Vis 114(2–3):248–271
Xie H, Liu H, Seneviratne LD et al (2014) An optical tactile Array probe head for tissue palpation during minimally invasive surgery [J]. IEEE Sensors J 14(9):3283–3291
Xing L, Schreibmann E, Levy D et al (2017) Multiscale Image Registration [J]. Math Bioscie Eng (Online) 3(2):389–418
Yang Y (2017) Top-down visual saliency via joint CRF and dictionary learning [J]. IEEE Trans Pattern Anal Mach Intell 39(3):576–588
Yang G, Xiao M, Zhang S (2013) Data aggregation scheme based on compressed sensing in wireless sensor network.[J]. J Netw 8(1):556–561
Yang YB, Zhu QH, Mao XJ, Pan LY (2015) Visual feature coding for image classification integrating dictionary structure [J]. Pattern Recogn 48(10):3067–3075
Ye H, Strunz K (2018) Multi-scale and frequency-dependent modeling of electric power transmission lines [J]. IEEE Trans Power Deliv 33(1):32–41
Yeh CH, Kang LW, Chiou YW, Lin CW, Fan Jiang SJ (2014) Self-learning-based post-processing for image/video deblocking via sparse representation [J]. J Vis Commun Image Represent 25(5):891–903
Zheng J, Jiang Z, Chellappa R (2016) Cross-view action recognition via transferable dictionary learning [J]. IEEE Trans Image Process 25(6):2542–2556
Zhou J, Semenovich D, Sowmya A, Wang J (2014) Dictionary learning framework for fabric defect detection [J]. J Text Inst 105(3):223–234
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Cai, M., Wang, S. & Wu, C. Research on real-time data transmission and multi-scale video image decomposition of embedded optical sensor array based on machine learning. Multimed Tools Appl 81, 41407–41427 (2022). https://doi.org/10.1007/s11042-020-09847-w
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-09847-w