Weighted kernel mapping model with spring simulation based watershed transformation for level set image segmentation

Zhang, Yingchun; Guo, He; Chen, Feng; Yang, Hongji

doi:10.1016/j.neucom.2017.01.044

Cited by 17 publications

(10 citation statements)

References 41 publications

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“…Therefore, the computational complexity of the CV model is O (11 N ). According to the studies in [13, 31], the computational complexity of the LIF model is O (( gl × gl + 2 × gw × gw + 1) × N ), where 1 < gl < 6 and gw ≥ 5.…”

Section: Resultsmentioning

confidence: 99%

Local difference‐based active contour model for medical image segmentation and bias correction

Niu

Cao

2019

IET image process

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This study proposes a local bias field and difference estimation (LBDE) model for medical image segmentation and bias field correction. Firstly, the LBDE model uses a linear combination of a given set of smooth orthogonal basis functions, which is called Chebyshev polynomial, to estimate the bias field. Then, a clustering criterion function is defined by considering the difference between the measured image and approximated image in a small region. By applying this difference in the local region, the LBDE model can obtain accurate segmentation results and estimation of the bias field. Finally, the energy functional is incorporated into a level set formulation with a regularisation term, and it is minimised via the level set evolution process. The LBDE model first appears as a two‐phase model and then extends to the multi‐phase one. Extensive experiments on medical images demonstrate that the LBDE model achieves more precise segmentation results in terms of Jaccard similarity and dice similarity coefficient than the comparative models. Therefore the proposed model can increase the segmentation accuracy and robustness to noise.

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Section: Resultsmentioning

confidence: 99%

Local difference‐based active contour model for medical image segmentation and bias correction

Niu

Cao

2019

IET image process

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“…The kernel function of SVDD can map nonlinear relations to higher-dimensional space and construct linear regression for processing [44]. In ID-SVDD, the kernel function also plays a key role.…”

Section: Methodsmentioning

confidence: 99%

Outlier Detection Using Improved Support Vector Data Description in Wireless Sensor Networks

Shi

Yongming

et al. 2019

Sensors

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Wireless sensor networks (WSNs) are susceptible to faults in sensor data. Outlier detection is crucial for ensuring the quality of data analysis in WSNs. This paper proposes a novel improved support vector data description method (ID-SVDD) to effectively detect outliers of sensor data. ID-SVDD utilizes the density distribution of data to compensate SVDD. The Parzen-window algorithm is applied to calculate the relative density for each data point in a data set. Meanwhile, we use Mahalanobis distance (MD) to improve the Gaussian function in Parzen-window density estimation. Through combining new relative density weight with SVDD, this approach can efficiently map the data points from sparse space to high-density space. In order to assess the outlier detection performance, the ID-SVDD algorithm was implemented on several datasets. The experimental results demonstrated that ID-SVDD achieved high performance, and could be applied in real water quality monitoring.

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“…After analyzing the image features of the tobacco strands, a method based on the Mean-shift algorithm was used in this work (17,18). It transformed the RGB image into gray scale, thus avoiding sensitivity issues and errors due to the different colors of the samples.…”

Section: Samplesmentioning

confidence: 99%

Measurement Method of the Width of the Strands of Cut Tobacco Based on Digital Image Processing

Liu

Chen

Zhao

et al. 2019

Beiträge Zur Tabakforschung International/Contributions to Tobacco Research

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SummaryThe width of cut tobacco strands is an important indicator for physical parameters as well as for the smoking quality. In some countries, cut width helps to distinguish fine-cut tobacco and pipe tobacco and thus differentiates taxation rate. A new method for rapid measurement of the width of cut tobacco strands was developed based on digital image processing, because the method described in ISO 20193, though easy to implement in factories, proved time consuming and generated high testing costs. The essence of this method is to determine the statistic width of incisions. The straight-line segments represent the width of strands of cut tobacco, from which the determination of the width for randomly placed tobacco strands could be achieved. Five kinds of samples (‘ISO collaborative study samples 0.4 mm, 1.0 mm, 1.6 mm and 3.0 mm’ and ‘Guangdong baked 0.9 mm’) were used to study the comparability of the measurement results between the method presented in this work and the current ISO method. Results show that accuracy and repeatability are comparable. In addition, the testing efficiency of the method presented in this work appears to be higher than the current ISO method, and it is thus a promising alternative method for measuring the width of strands of cut tobacco.

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Weighted kernel mapping model with spring simulation based watershed transformation for level set image segmentation

Cited by 17 publications

References 41 publications

Local difference‐based active contour model for medical image segmentation and bias correction

Local difference‐based active contour model for medical image segmentation and bias correction

Outlier Detection Using Improved Support Vector Data Description in Wireless Sensor Networks

Measurement Method of the Width of the Strands of Cut Tobacco Based on Digital Image Processing

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