2022
DOI: 10.1155/2022/6532852
|View full text |Cite
|
Sign up to set email alerts
|

Design and Implementation of Local Threshold Segmentation Based on FPGA

Abstract: In the process of the development of image processing technology, image segmentation is a very important image processing technology in the field of machine vision, pedestrian detection, medical imaging, and so on. However, the traditional image segmentation technology cannot solve the problems of reflection and uneven illumination. This paper presents a local threshold segmentation method based on FPGA, which can automatically select the optimal threshold according to different gray levels of images. First, t… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
1
1
1

Relationship

1
2

Authors

Journals

citations
Cited by 3 publications
(2 citation statements)
references
References 12 publications
0
2
0
Order By: Relevance
“…The local thresholding based on FGPA proposed to solve the uneven illumination and reflection problem by adopting the mean value of neighbourhood blocks and Gaussian weighted sum design idea in the local neighbourhood [7]. An algorithm for more precise and faster segmentation of the target curve, known as quasi-bimodal threshold segmentation (QBTS), which converts the multimodal histogram into a quasi-bimodal histogram proposed by Ruan et al [8].…”
Section: A Threshold Based Segmentationmentioning
confidence: 99%
“…The local thresholding based on FGPA proposed to solve the uneven illumination and reflection problem by adopting the mean value of neighbourhood blocks and Gaussian weighted sum design idea in the local neighbourhood [7]. An algorithm for more precise and faster segmentation of the target curve, known as quasi-bimodal threshold segmentation (QBTS), which converts the multimodal histogram into a quasi-bimodal histogram proposed by Ruan et al [8].…”
Section: A Threshold Based Segmentationmentioning
confidence: 99%
“…As the number of parameters in the operation of the network model of CNN continues to grow, convolutional neural networks (CNNs) can be accelerated by using GPU (Graphics Processing Unit), ASIC (Application Specific Integrated Circuit), and FPGA-based architectures. However, the CPU (Central Processing Unit) is no longer suitable for large-scale parallel computing, and GPUs are not suitable for use in low-cost embedded systems due to their high cost and power consumption [8]. ASICs have lengthy design cycles and minimal reconfigurability [9].FPGAs are more reconfigurable than ASICs, consume less power than GPUs, and are simpler to deploy, and can be adjusted to handle various scenarios.…”
Section: Introductionmentioning
confidence: 99%