Fast segmentation algorithm of PCB image using 2D OTSU improved by adaptive genetic algorithm and integral image

Ma, Jun; Cheng, Xiaodong

doi:10.1007/s11554-023-01272-0

Cited by 7 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ning [5] proposed an optimization algorithm of the hybrid whale algorithm for two-dimensional (2D) Otsu's multi-threshold image segmentation to address many problems of the traditional Otsu's method, which reduces the computation time and has better segmentation results. Ma and et al [6] proposed an improved Otsu's method, which transforms the threshold selection of 2D Otsu's method into an optimization problem of inter-class variability by using an adaptive genetic algorithm.…”

Section: Threshold Methods (1) Iterative Methodsmentioning

confidence: 99%

Comparison of line-structured light stripe segmentation algorithms

Han,

Gao,

et al. 2023

Third International Computing Imaging Conference (CITA 2023)

View full text Add to dashboard Cite

The line-structured light vision sensor is widely used in 3D reconstruction and scanning, quality inspection and other fields. In this technique, the segmentation of light stripe image is an important preprocessing steps, which separates light stripe and extract ROI from an original image. The segmentation of light stripe is the basis of centerline extraction and also affects the efficiency of centerline extraction. In our work, the 3D reconstruction of wood surface needs to be realized through using line-structured light sensor, and then the 3D defects need to be detected. The measured wood has a curved, rough and scattering surface. The acquired light stripe is relative complex. Several algorithms of image segmentation are studied, and the segmentation performance and operation efficiency are compared and analyzed, so as to help us find the optimum technique.

show abstract

Section: Threshold Methods (1) Iterative Methodsmentioning

confidence: 99%

Comparison of line-structured light stripe segmentation algorithms

Han,

Gao,

et al. 2023

Third International Computing Imaging Conference (CITA 2023)

View full text Add to dashboard Cite

show abstract

“…Hybrid algorithms, like the amalgamation of Otsu with K-means clustering, have enhanced accuracy in multi-light spot center detection but impose greater computational demands [ 38 ]. Additionally, the 2D Otsu algorithm, when coupled with adaptive energy segmentation and genetic algorithms, demonstrates efficiency, albeit with lingering challenges in handling complex textures and color variations [ 39 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

Advancing image segmentation with DBO-Otsu: Addressing rubber tree diseases through enhanced threshold techniques

Xie,

Wu,

Tang

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Addressing the profound impact of Tapping Panel Dryness (TPD) on yield and quality in the global rubber industry, this study introduces a cutting-edge Otsu threshold segmentation technique, enhanced by Dung Beetle Optimization (DBO-Otsu). This innovative approach optimizes the segmentation threshold combination by accelerating convergence and diversifying search methodologies. Following initial segmentation, TPD severity levels are meticulously assessed using morphological characteristics, enabling precise determination of optimal thresholds for final segmentation. The efficacy of DBO-Otsu is rigorously evaluated against mainstream benchmarks like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM), and compared with six contemporary swarm intelligence algorithms. The findings reveal that DBO-Otsu substantially surpasses its counterparts in image segmentation quality and processing speed. Further empirical analysis on a dataset comprising TPD cases from level 1 to 5 underscores the algorithm’s practical utility, achieving an impressive 80% accuracy in severity level identification and underscoring its potential for TPD image segmentation and recognition tasks.

show abstract