scholarly journals Incremental Low Rank Noise Reduction for Robust Infrared Tracking of Body Temperature during Medical Imaging

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1301 ◽  
Author(s):  
Bardia Yousefi ◽  
Hossein Memarzadeh Sharifipour ◽  
Mana Eskandari ◽  
Clemente Ibarra-Castanedo ◽  
Denis Laurendeau ◽  
...  
Keyword(s):  
Body Temperature ◽  
Medical Imaging ◽  
Noise Reduction ◽  
Additive Noise ◽  
Infrared Imaging ◽  
Tracking System ◽  
Region Of Interest ◽  
Motion Artifacts ◽  
Low Rank ◽  
Value Decomposition

Thermal imagery for monitoring of body temperature provides a powerful tool to decrease health risks (e.g., burning) for patients during medical imaging (e.g., magnetic resonance imaging). The presented approach discusses an experiment to simulate radiology conditions with infrared imaging along with an automatic thermal monitoring/tracking system. The thermal tracking system uses an incremental low-rank noise reduction applying incremental singular value decomposition (SVD) and applies color based clustering for initialization of the region of interest (ROI) boundary. Then a particle filter tracks the ROI(s) from the entire thermal stream (video sequence). The thermal database contains 15 subjects in two positions (i.e., sitting, and lying) in front of thermal camera. This dataset is created to verify the robustness of our method with respect to motion-artifacts and in presence of additive noise (2–20%—salt and pepper noise). The proposed approach was tested for the infrared images in the dataset and was able to successfully measure and track the ROI continuously (100% detecting and tracking the temperature of participants), and provided considerable robustness against noise (unchanged accuracy even in 20% additive noise), which shows promising performance.

scholarly journals Design and Experimental Characterization of a Discovery and Tracking System for Optical Camera Communications

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2925
Author(s):  
Antonio Mederos-Barrera ◽  
Cristo Jurado-Verdu ◽  
Victor Guerra ◽  
Jose Rabadan ◽  
Rafael Perez-Jimenez
Keyword(s):  
Tracking System ◽  
Region Of Interest ◽  
Experimental Methodology ◽  
Visible Light Communications ◽  
Spatial Features ◽  
Tracking Algorithms ◽  
Receiver Architecture ◽  
Vehicle Networks ◽  
Time Required ◽  
Static Conditions

Visible light communications (VLC) technology is emerging as a candidate to meet the demand for interconnected devices’ communications. However, the costs of incorporating specific hardware into end-user devices slow down its market entry. Optical camera communication (OCC) technology paves the way by reusing cameras as receivers. These systems have generally been evaluated under static conditions, in which transmitting sources are recognized using computationally expensive discovery algorithms. In vehicle-to-vehicle networks and wearable devices, tracking algorithms, as proposed in this work, allow one to reduce the time required to locate a moving source and hence the latency of these systems, increasing the data rate by up to 2100%. The proposed receiver architecture combines discovery and tracking algorithms that analyze spatial features of a custom RGB LED transmitter matrix, highlighted in the scene by varying the cameras’ exposure time. By using an anchor LED and changing the intensity of the green LED, the receiver can track the light source with a slow temporal deterioration. Moreover, data bits sent over the red and blue channels do not significantly affect detection, hence transmission occurs uninterrupted. Finally, a novel experimental methodology to evaluate the evolution of the detection’s performance is proposed. With the analysis of the mean and standard deviation of novel K parameters, it is possible to evaluate the detected region-of-interest scale and centrality against the transmitter source’s ideal location.

scholarly journals A Flexible Coding Scheme Based on Block Krylov Subspace Approximation for Light Field Displays with Stacked Multiplicative Layers

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4574
Author(s):  
Joshitha Ravishankar ◽  
Mansi Sharma ◽  
Pradeep Gopalakrishnan
Keyword(s):  
Light Field ◽  
Krylov Subspace ◽  
Motion Parallax ◽  
Low Rank ◽  
Coding Scheme ◽  
Krylov Subspace Approximation ◽  
New Type ◽  
Value Decomposition ◽  
Subspace Approximation ◽  
Inter Frame

To create a realistic 3D perception on glasses-free displays, it is critical to support continuous motion parallax, greater depths of field, and wider fields of view. A new type of Layered or Tensor light field 3D display has attracted greater attention these days. Using only a few light-attenuating pixelized layers (e.g., LCD panels), it supports many views from different viewing directions that can be displayed simultaneously with a high resolution. This paper presents a novel flexible scheme for efficient layer-based representation and lossy compression of light fields on layered displays. The proposed scheme learns stacked multiplicative layers optimized using a convolutional neural network (CNN). The intrinsic redundancy in light field data is efficiently removed by analyzing the hidden low-rank structure of multiplicative layers on a Krylov subspace. Factorization derived from Block Krylov singular value decomposition (BK-SVD) exploits the spatial correlation in layer patterns for multiplicative layers with varying low ranks. Further, encoding with HEVC eliminates inter-frame and intra-frame redundancies in the low-rank approximated representation of layers and improves the compression efficiency. The scheme is flexible to realize multiple bitrates at the decoder by adjusting the ranks of BK-SVD representation and HEVC quantization. Thus, it would complement the generality and flexibility of a data-driven CNN-based method for coding with multiple bitrates within a single training framework for practical display applications. Extensive experiments demonstrate that the proposed coding scheme achieves substantial bitrate savings compared with pseudo-sequence-based light field compression approaches and state-of-the-art JPEG and HEVC coders.

Local Hypergraph Laplacian Regularized Low-Rank Representation for Noise Reduction of Hyperspectral Images

2017 ◽  
Vol 37 (5) ◽  
pp. 0510001 ◽  
Author(s):  
薛志祥 Xue Zhixiang ◽  
余旭初 Yu Xuchu ◽  
谭 熊 Tan Xiong ◽  
付琼莹 Fu Qiongying
Keyword(s):  
Noise Reduction ◽  
Hyperspectral Images ◽  
Low Rank ◽  
Low Rank Representation ◽  
Hypergraph Laplacian
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