Real-Time Generation of Full color Image Hologram with Compact Distance Look-up Table

2009 ◽  
Author(s):  
Hiroshi Yoshikawa ◽  
Takeshi Yamaguchi ◽  
Ryota Kitayama
Keyword(s):  
Real Time ◽  
Color Image ◽  
Full Color ◽  
Look Up Table

scholarly journals Fast Calculation of Full Color Image Hologram Using Distance Look-Up Table

2010 ◽  
Vol 64 (11) ◽  
pp. 1744-1749
Author(s):  
Ryota Kitayama ◽  
Naoyuki Omura ◽  
Takeshi Yamaguchi ◽  
Hiroshi Yoshikawa
Keyword(s):  
Color Image ◽  
Fast Calculation ◽  
Full Color ◽  
Look Up Table

Panorama real-time unrolling of catadioptric omni-directional images with look-up table based on FPGA

2009 ◽  
Vol 28 (12) ◽  
pp. 3135-3137
Author(s):  
Bin WANG ◽  
Zhi-hui XIONG ◽  
Gang CHENG ◽  
Li-dong CHEN ◽  
Mao-jun ZHANG
Keyword(s):  
Real Time ◽  
Look Up Table

scholarly journals Full-color nanorouter for high-resolution imaging

Nanoscale ◽  
2021 ◽  
Author(s):  
Mingjie Chen ◽  
Long Wen ◽  
Dahui Pan ◽  
David Cumming ◽  
Xianguang Yang ◽  
...  
Keyword(s):  
High Resolution ◽  
Color Image ◽  
Image Sensors ◽  
Structural Color ◽  
High Resolution Imaging ◽  
Scaling Effects ◽  
Full Color ◽  
Resolution Imaging

Pixel scaling effects have been a major issue for the development of high-resolution color image sensors due to the reduced photoelectric signal and the color crosstalk. Various structural color techniques...

scholarly journals Real-Time Vehicle Detection Framework Based on the Fusion of LiDAR and Camera

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 451 ◽  
Author(s):  
Limin Guan ◽  
Yi Chen ◽  
Guiping Wang ◽  
Xu Lei
Keyword(s):  
Real Time ◽  
Color Image ◽  
Evidence Theory ◽  
Depth Map ◽  
Vehicle Detection ◽  
Detection Accuracy ◽  
Distance Information ◽  
Detection Model ◽  
Detection Mode ◽  
Single Sensor

Vehicle detection is essential for driverless systems. However, the current single sensor detection mode is no longer sufficient in complex and changing traffic environments. Therefore, this paper combines camera and light detection and ranging (LiDAR) to build a vehicle-detection framework that has the characteristics of multi adaptability, high real-time capacity, and robustness. First, a multi-adaptive high-precision depth-completion method was proposed to convert the 2D LiDAR sparse depth map into a dense depth map, so that the two sensors are aligned with each other at the data level. Then, the You Only Look Once Version 3 (YOLOv3) real-time object detection model was used to detect the color image and the dense depth map. Finally, a decision-level fusion method based on bounding box fusion and improved Dempster–Shafer (D–S) evidence theory was proposed to merge the two results of the previous step and obtain the final vehicle position and distance information, which not only improves the detection accuracy but also improves the robustness of the whole framework. We evaluated our method using the KITTI dataset and the Waymo Open Dataset, and the results show the effectiveness of the proposed depth completion method and multi-sensor fusion strategy.

scholarly journals Image fusion algorithm based on improved K-singular value decomposition and Hadamard measurement matrix

2018 ◽  
Vol 13 ◽  
pp. 174830181879151
Author(s):  
Qiang Yang ◽  
Huajun Wang
Keyword(s):  
Singular Value Decomposition ◽  
Image Fusion ◽  
Compressive Sensing ◽  
Color Image ◽  
Singular Value ◽  
Measurement Matrix ◽  
Fusion Algorithm ◽  
Full Color ◽  
Value Decomposition ◽  
Time And Space Complexity

To solve the problem of high time and space complexity of traditional image fusion algorithms, this paper elaborates the framework of image fusion algorithm based on compressive sensing theory. A new image fusion algorithm based on improved K-singular value decomposition and Hadamard measurement matrix is proposed. This proposed algorithm only acts on a small amount of measurement data after compressive sensing sampling, which greatly reduces the number of pixels involved in the fusion and improves the time and space complexity of fusion. In the fusion experiments of full-color image with multispectral image, infrared image with visible light image, as well as multispectral image with full-color image, this proposed algorithm achieved good experimental results in the evaluation parameters of information entropy, standard deviation, average gradient, and mutual information.

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