A spatially constrained shifted asymmetric Laplace mixture model for the grayscale image segmentation

Sun, Hao; Yang, Xianqiang; Gao, Huijun

doi:10.1016/j.neucom.2018.10.039

Cited by 21 publications

(10 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(1) Extract the lead and body regions from chip images using the image segmentation algorithm with the asymmetric Laplace mixture model [24] and connected-component labelling algorithm;…”

Section: Chip Appearance Defect Detection Based On Multi-order Fractimentioning

confidence: 99%

Chip Appearance Inspection Method for High-Precision SMT Equipment

2021

Self Cite

View full text Add to dashboard Cite

In order to meet the defect-detection requirements of chips in high-precision surface mount technology (SMT) equipment widely used in the electronic industry, a chip appearance defect-detection method based on multi-order fractional discrete wavelet packet decomposition (DWPD) is proposed in this paper. First, lead and body regions were extracted from chip images using the image segmentation algorithm with asymmetric Laplace mixture model and connected-component labelling algorithm; then, the texture feature of the region to be inspected was extracted with the multi-order fractional DWPD algorithm and the geometric and gradient features were combined to form image features of the region to be inspected before the subset of features was selected from image features with the feature selection algorithm based on the variational Bayesian Gaussian mixture model; and finally, the support vector machine was used to determine whether the region to be inspected was defective. An experiment was conducted on a data set captured in high-precision SMT equipment. The accuracy of the proposed chip appearance defect-detection method is about 93%, which is more accurate than existing ones.

show abstract

“…(1) Extract the lead and body regions from chip images using the image segmentation algorithm with the asymmetric Laplace mixture model [24] and connected-component labelling algorithm;…”

Section: Chip Appearance Defect Detection Based On Multi-order Fractimentioning

confidence: 99%

Chip Appearance Inspection Method for High-Precision SMT Equipment

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…SAL distribution can be decomposed into Gaussian distribution and exponential distribution (Sun et al, 2016) as follows…”

Section: Statistical Description Of Shifted Asymmetric Laplace Distributionmentioning

confidence: 99%

LPV system identification with multiple-model approach based on shifted asymmetric laplace distribution

Yang

Liu

2021

International Journal of Systems Science

Self Cite

View full text Add to dashboard Cite

The robust linear parameters varying systems identification method with multiple-model approach is addressed in this paper. Various noise and outliers commonly exist in practical industrial processes and have a serious impact on data-driven system identification methods. A statistic approach is proposed in the paper where the centralised asymmetric Laplace (CAL) distribution is employed to model the noise and therefore the parameters estimation algorithm based on CAL distribution is robust to the symmetric/asymmetric noise and outliers. CAL distribution is represented as the product of exponential distribution and Gaussian distribution, and therefore the parameters estimation formulas are deduced in the expectation maximisation algorithm framework. The efficacy and the robustness of the proposed algorithm are verified by a numerical example and the continuous stirred tank reactor system experiment.

show abstract

“…With the rapid development of computers, technologies of image processing were applied widely in many areas, including quality estimation [1,2], infrared detection [3,4], disease recognition [5,6], agricultural identification [7,8], fingerprint identification [9], and many other aspects [10,11]. Especially, image analysis has become a very useful method in the study of cement microstructure based on the cement SEM (scanning electron microscope) image [12], as it is possible to analyze the mechanism of cement hydration reaction by observing the microstructure of cement in cement stone materials [13].…”

Section: Introductionmentioning

confidence: 99%

Renyi’s Entropy Based Multilevel Thresholding Using a Novel Meta-Heuristics Algorithm

Liu

Huang

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

Multi-level image thresholding is the most direct and effective method for image segmentation, which is a key step for image analysis and computer vision, however, as the number of threshold values increases, exhaustive search does not work efficiently and effectively and evolutionary algorithms often fall into a local optimal solution. In the paper, a meta-heuristics algorithm based on the breeding mechanism of Chinese hybrid rice is proposed to seek the optimal multi-level thresholds for image segmentation and Renyi’s entropy is utilized as the fitness function. Experiments have been run on four scanning electron microscope images of cement and four standard images, moreover, it is compared with other six classical and novel evolutionary algorithms: genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm, ant lion optimization algorithm, whale optimization algorithm, and salp swarm algorithm. Meanwhile, some indicators, including the average fitness values, standard deviation, peak signal to noise ratio, and structural similarity index are used as evaluation criteria in the experiments. The experimental results show that the proposed method prevails over the other algorithms involved in the paper on most indicators and it can segment cement scanning electron microscope image effectively.

show abstract

A spatially constrained shifted asymmetric Laplace mixture model for the grayscale image segmentation

Cited by 21 publications

References 25 publications

Chip Appearance Inspection Method for High-Precision SMT Equipment

Chip Appearance Inspection Method for High-Precision SMT Equipment

LPV system identification with multiple-model approach based on shifted asymmetric laplace distribution

Renyi’s Entropy Based Multilevel Thresholding Using a Novel Meta-Heuristics Algorithm

Contact Info

Product

Resources

About