scholarly journals Multi-Sensor Array Imaging System for Synthesizing Arbitrary Viewpoint Images and Its Depth Estimation Method

2007 ◽  
Vol 61 (11) ◽  
pp. 1633-1641 ◽  
Author(s):  
Nao Yuki ◽  
Takayuki Hamamoto ◽  
Ryutaro Oi ◽  
Kiyoharu Aizawa
Keyword(s):  
Sensor Array ◽  
Imaging System ◽  
Estimation Method ◽  
Depth Estimation ◽  
Array Imaging

scholarly journals Precise Estimation of NDVI with a Simple NIR Sensitive RGB Camera and Machine Learning Methods for Corn Plants

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3208 ◽  
Author(s):  
Liangju Wang ◽  
Yunhong Duan ◽  
Libo Zhang ◽  
Tanzeel U. Rehman ◽  
Dongdong Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Professional Training ◽  
Imaging System ◽  
Estimation Method ◽  
Machine Learning Algorithms ◽  
Plant Phenotyping ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Corn Plants ◽  
Hyperspectral Camera

The normalized difference vegetation index (NDVI) is widely used in remote sensing to monitor plant growth and chlorophyll levels. Usually, a multispectral camera (MSC) or hyperspectral camera (HSC) is required to obtain the near-infrared (NIR) and red bands for calculating NDVI. However, these cameras are expensive, heavy, difficult to geo-reference, and require professional training in imaging and data processing. On the other hand, the RGBN camera (NIR sensitive RGB camera, simply modified from standard RGB cameras by removing the NIR rejection filter) have also been explored to measure NDVI, but the results did not exactly match the NDVI from the MSC or HSC solutions. This study demonstrates an improved NDVI estimation method with an RGBN camera-based imaging system (Ncam) and machine learning algorithms. The Ncam consisted of an RGBN camera, a filter, and a microcontroller with a total cost of only $70 ~ 85. This new NDVI estimation solution was compared with a high-end hyperspectral camera in an experiment with corn plants under different nitrogen and water treatments. The results showed that the Ncam with two-band-pass filter achieved high performance (R2 = 0.96, RMSE = 0.0079) at estimating NDVI with the machine learning model. Additional tests showed that besides NDVI, this low-cost Ncam was also capable of predicting corn plant nitrogen contents precisely. Thus, Ncam is a potential option for MSC and HSC in plant phenotyping projects.

Optimum Focusing in the Ultrasonic Annular Array Imaging System

1988 ◽  
pp. 49-61
Author(s):  
T. K. Song ◽  
J. I. Koo ◽  
S. B. Park
Keyword(s):  
Imaging System ◽  
Annular Array ◽  
Array Imaging

Acoustical Holography Matrix Array Imaging System for the Underwater Inspection of Offshore Oil Platform Weldments

1980 ◽  
pp. 97-117
Author(s):  
H. D. Collins ◽  
R. P. Gribble ◽  
T. E. Hall ◽  
W. M. Lechelt ◽  
J. T. Luebke ◽  
...  
Keyword(s):  
Imaging System ◽  
Array Imaging ◽  
Offshore Oil ◽  
Acoustical Holography ◽  
Matrix Array

Design and Development of Capacitance Sensor Array for Analyzing the Non-Homogeneity in Biological Materials

2018 ◽  
pp. 70-96 ◽  
Author(s):  
Sahana Apparsamy ◽  
Kamalanand Krishnamurthy
Keyword(s):  
Sensor Array ◽  
Soft Tissues ◽  
Imaging System ◽  
Learning Algorithm ◽  
Automated Analysis ◽  
Biological Materials ◽  
Jaccard Index ◽  
Capacitance Sensor ◽  
Laser Imaging ◽  
Morphological Variations

Soft tissues are non-homogeneous deformable structures having varied structural arrangements, constituents, and composition. This chapter explains the design of a capacitance sensor array for analyzing and imaging the non-homogeneity in biological materials. Further, tissue mimicking phantoms are developed using Agar-Agar and Polyacrylamide gels for testing the developed sensor. Also, the sensor employs an unsupervised learning algorithm for automated analysis of non-homogeneity. The reconstructed capacitance image can also be sensitive to topographical and morphological variations in the sample. The proposed method is further validated using a fiberoptic-based laser imaging system and the Jaccard index. In this chapter, the design of the sensor array for smart analysis of non-homogeneity along with significant results are presented in detail.

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