In the measurement of the wheel tread in rail vehicles, line laser vision measurement technology has a good application prospect. However, the intensity and location of the ambient light will constantly change in the ...
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In the measurement of the wheel tread in rail vehicles, line laser vision measurement technology has a good application prospect. However, the intensity and location of the ambient light will constantly change in the actual application scenarios. Traditional laser stripe segmentation algorithms often fail to produce accurate results, leading to decreased measurement precision in wheel tread. To solve the problem, a segmentation algorithm for laser stripes was proposed. Firstly, the SSR algorithm and frame subtraction were utilized to remove the background noise. Then, the OTSU method was used for the preliminary segmentation. After that, smoothing Images and reducing noise were performed with geometric mean filtering and morphological closing. Finally, the segmentation function which was based on the gray scale distribution characteristics of each region of the image was established to achieve the accurate segmentation of laser stripes. Laser stripe segmentation experiments, laser stripe segmentation comparison experiments, and wheel tread geometry extraction experiments were designed and conducted under the ambient light interference. The experimental results show that the segmentation success rate of the proposed algorithm is not <90.625 %. The proposed algorithm has a superior segmentation effect compared to other algorithms. The proposed algorithm can improve the measurement accuracy. For flange height measurement, the mean error decreased from 0.298 mm to 0.161 mm, and the standard deviation decreased from 0.600 to 0.548. For flange width measurement, the mean error remained constant at 0.200 mm, and the standard deviation decreased from 0.681 to 0.536. Under the condition that the ambient light intensity is in the range of 37lux similar to 1050 lx and the laser power is not <50mW, the proposed algorithm can better realize the adaptive segmentation of laser stripes.
Medical image segmentation demands higher segmentation accuracy especially when the images are affected by noise. This paper proposes a novel technique to segment medical images efficiently using an intuitionistic fuz...
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Medical image segmentation demands higher segmentation accuracy especially when the images are affected by noise. This paper proposes a novel technique to segment medical images efficiently using an intuitionistic fuzzy divergence-based thresholding. A neighbourhood-based membership function is defined here. The intuitionistic fuzzy divergence-based image thresholding technique using the neighbourhood-based membership functions yield lesser degradation of segmentation performance in noisy environment. Its ability in handling noisy images has been validated. The algorithm is independent of any parameter selection. Moreover, it provides robustness to both additive and multiplicative noise. The proposed scheme has been applied on three types of medical image datasets in order to establish its novelty and generality. The performance of the proposed algorithm has been compared with other standard algorithms viz. Otsu's method, fuzzy C-means clustering, and fuzzy divergence-based thresholding with respect to (1) noise-free images and (2) ground truth images labelled by experts/clinicians. Experiments show that the proposed methodology is effective, more accurate and efficient for segmenting noisy images.
This paper introduces an algorithm based on the context of MRF (Markov random field) model, and this method achieved oil spilling detection and segmentation. In this paper, the two important elements are initial label...
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ISBN:
(纸本)9781510822023
This paper introduces an algorithm based on the context of MRF (Markov random field) model, and this method achieved oil spilling detection and segmentation. In this paper, the two important elements are initial labelling field and potential parameter estimation. The algorithm model chooses optical pyramid of saliency map as initial label field and Ising model as segmentation function. Using the GMM (Gaussian Mixture Model) and MAP (Maximum a Posterior) get local optimal result by ICM (Iteration Condition Model) method. This paper is also deeply researching the potential parameter which is the impact factor in segmentation function. Through studying the relationship between potential function and every scale-levels of saliency pyramid, the paper gets the better result which is more accuracy segmentations and keeping more texture information. The series experiments prove this method having false alarming rejection and noise suppression function in Oceanic SAR images.
We have studied the segmentation of two-letter AB heterosequences composed of subsequences with different composition and distribution of A and B monomer units along the chain. Our approach is based on the segmentatio...
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We have studied the segmentation of two-letter AB heterosequences composed of subsequences with different composition and distribution of A and B monomer units along the chain. Our approach is based on the segmentation function S(k) introduced in the present work and on the Jensen-Shannon divergence measure determined with respect to the probabilities of the lengths of uniform blocks of A and B monomer units. It is shown that the function S(k) is extremely sensitive to the sequence statistics. Even visual analysis of S(k) allows judgment on some features of sequence statistics. In particular, function S(k) is constant for random copolymers, it is an oscillating function for random block copolymers and shows monotonic growth up to some constant value for proteinlike copolymers. However, due to significant fluctuations observed for short sequences, the function S(k) can be effectively used only for segmentation of a heterosequence composed of very long subsequences. On the other hand, we find that the Jensen-Shannon divergence measure does not allow one to judge the type of statistics, but is extremely efficient for segmentation of a heterosequence. Therefore, the two introduced functions, being mutually complementary, provide an effective approach for recognizing and segmentation of heterosequences. As an example, the methods developed are applied for concatenating sequences of different proteins.
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