An Iris Image Acquisition Method Based on Embedded System

2013 ◽  
Vol 313-314 ◽  
pp. 1197-1200
Author(s):  
Qi Jian Sun ◽  
Zheng Ma
Keyword(s):  
Embedded System ◽  
Traditional Method ◽  
Quality Evaluation ◽  
Control Function ◽  
Image Acquisition ◽  
High Quality ◽  
Iris Image ◽  
Acquisition Speed ◽  
Pass Through ◽  
Acquisition Method

Capturing an iris image rapidly is required for iris image acquisition embedded system. For the traditional method, an iris image must firstly pass through the quality evaluation, which is complex, so the acquisition speed is very slow. This paper described an iris acquisition embedded system, and a new capturing method. The system includes an DSP DM642, and some relevant peripherals. When an image is acquired, it is filtered by the capture-control function, if passed then be stored the image, until capturing the assigned amount. Using this method, we can rapidly capture the high quality iris images at about 0.8s, and 83% of the stored images can be used for the next procedure. At the same time, it is convenient for the user to capture.

New automated iris image acquisition method

2005 ◽  
Vol 44 (5) ◽  
pp. 713 ◽  
Author(s):  
Kang Ryoung Park
Keyword(s):  
Image Acquisition ◽  
Iris Image ◽  
Acquisition Method

Full-parallax and high-quality multiview 3D image acquisition method using camera slider

2018 ◽  
Vol 2018 (4) ◽  
pp. 411-1-411-6
Author(s):  
Byeong-Jun Kim ◽  
Ki-Chul Kwon ◽  
Jae-Min Lee ◽  
Young-Tae Lim ◽  
Nam Kim
Keyword(s):  
Image Acquisition ◽  
3D Image ◽  
High Quality ◽  
Acquisition Method

scholarly journals An Underwater Image Enhancement Algorithm Based on Generative Adversarial Network and Natural Image Quality Evaluation Index

2021 ◽  
Vol 9 (7) ◽  
pp. 691
Author(s):  
Kai Hu ◽  
Yanwen Zhang ◽  
Chenghang Weng ◽  
Pengsheng Wang ◽  
Zhiliang Deng ◽  
...  
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Quality Evaluation ◽  
High Efficiency ◽  
Natural Image ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Image Quality Evaluation ◽  
Adversarial Network ◽  
Underwater Image

When underwater vehicles work, underwater images are often absorbed by light and scattered and diffused by floating objects, which leads to the degradation of underwater images. The generative adversarial network (GAN) is widely used in underwater image enhancement tasks because it can complete image-style conversions with high efficiency and high quality. Although the GAN converts low-quality underwater images into high-quality underwater images (truth images), the dataset of truth images also affects high-quality underwater images. However, an underwater truth image lacks underwater image enhancement, which leads to a poor effect of the generated image. Thus, this paper proposes to add the natural image quality evaluation (NIQE) index to the GAN to provide generated images with higher contrast and make them more in line with the perception of the human eye, and at the same time, grant generated images a better effect than the truth images set by the existing dataset. In this paper, several groups of experiments are compared, and through the subjective evaluation and objective evaluation indicators, it is verified that the enhanced image of this algorithm is better than the truth image set by the existing dataset.

A Novel Embedded System Architecture for Fire Detection Based on Image Processing

2014 ◽  
Vol 608-609 ◽  
pp. 454-458
Author(s):  
Wei Bai ◽  
Chen Yuan Hu
Keyword(s):  
Image Processing ◽  
Embedded System ◽  
System Architecture ◽  
Cooperative Control ◽  
Processing System ◽  
Fire Detection ◽  
System Level ◽  
High Definition ◽  
The Embedded System ◽  
Pass Through

This paper presents novel logic/software co-work architecture for embedded high definition image processing platform, which is built by the considerations of system level, board hardware level, and the tasks partition between CPU processing and programmable logic based on the latest launched System on Chip Field Programmable Gate Array (Soc FPGA) – Xilinx ZC7020. For this case, we comprehensive analyze of the critical data paths: the uniform Advanced Extensible Interface (AXI) processing between processing system (PS) and processing logic (PL), including high definition video pass through PL to PS and PS software processing send to PL for speed up. We have included the transplant of opensource Linux, multiprocessing cooperative control and boot loader in PS side. Since the general platform is proposed, a fire detection approach based on high definition image processing is implemented. Experiment results indicated the feasibility and universality of the embedded system architecture.

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