Image segmentation based on gray stretch and threshold algorithm

Liu, Leiming; Yang, Ning; Lan, Jinhui; Li, Juanjuan

doi:10.1016/j.ijleo.2015.01.033

Cited by 33 publications

(24 citation statements)

References 13 publications

(11 reference statements)

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“…To solve the above-mentioned problems, we propose a novel dehazing method based on precise estimation of the atmospheric light A and transmittance. Our experiments show that the Gray-level threshold segmentation algorithm [19] can not only locate a specific target position but is very efficient. The modified least-square filter method can help retain the details of the images and suppress noise as well, hence offering a good visual sensory experience.…”

Section: Introductionmentioning

confidence: 92%

“…In He's method [18], it is confirmed that the atmospheric light A is set by selecting the first pixel maximum pixel value in the entire image, which results in low A precision. In the paper, Gray-level threshold segmentation algorithm [19] is employed to separate and locate the approximate region of atmospheric light value A, and then the maximum value of atmospheric light is identified by the skyline method. Hence, the atmospheric light A is obtained accurately.…”

Section: Dark-channel Prior Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Scene-Awareness Based Single Image Dehazing Technique via Automatic Estimation of Sky Area

Shao

et al. 2019

IEEE Access

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The occurrence of fog, mist, smog, or haze significantly reduces the visibility of the scenes and images, resulting in limited recognition of computer vision and computer graphics. So, removing haze from images is a must. In this paper, we regard image dehazing as a mathematical inversion process and image restoration based on atmospheric scattering models. The atmospheric light can be accurately estimated by combining the gray threshold segmentation and the skyline method. The improved least squares filtering method is used to optimize the transmittance map so that the edge details can be highlighted and the halo effect can be alleviated. A large number of test images show that our algorithm can achieve better experimental results than the other seven most advanced dehazing strategies. INDEX TERMS Grey-level threshold segmentation, skyline, least-square filter, halo artifact, dehazing.

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Section: Introductionmentioning

confidence: 92%

Section: Dark-channel Prior Algorithmmentioning

confidence: 99%

Scene-Awareness Based Single Image Dehazing Technique via Automatic Estimation of Sky Area

Shao

et al. 2019

IEEE Access

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“…After binarization, the aluminum foil portion in the binarized aluminum foil image is white, and the nonaluminum foil portion is black. e threshold required for binarization can be obtained by using the maximum between-class variance method [12][13][14]15]. For foil images I(x, y), the foil and background segmentation threshold is denoted as T. e ratio of pixels belonging to the aluminum foil to the image is denoted as ω 0 , and its average gray is recorded as μ 0 .…”

Section: Separation Of the Aluminum Foil From The Backgroundmentioning

confidence: 99%

Texture Detection of Aluminum Foil Based on Top‐Hat Transformation and Connected Region Segmentation

Wang

2020

Advances in Materials Science and Engineering

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A new method of texture detection for aluminum foil based on digital image processing technology is proposed. Top-hat transformation and image segmentation technology based on the connected domain are used to change the method of determining texture fraction by using human experience. Compared with the brightness method, pit detection method, and EBSD technology, this method can complete quantitative detection efficiently, automatically, and accurately, and reduce the detection time and manpower. It eliminates the instability of manual detection and ensures the accuracy of detection. By this method, the error of test results can be controlled within 1.6%, which is much better than 7.3% of the brightness method and 4% of the pitting method. It provides more accurate test results for the production process control of aluminum foil.

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“…(3) The position and velocity of particle swarm are updated according to fitness and equation (6). Then whether the algorithm reaches the termination condition is determined, including the convergence of the fitness function to stability and the maximum number of iterations.…”

Section: Fig 1 Plane Projection Of Two-dimensional Histogrammentioning

confidence: 99%

“…Yuan et al [5] proposed an improved OTSU method based on weighted target variance to separate defects from background in rail images and found that the improved OTSU method could accurately segment various rail images. Liu et al [6] proposed an image binarization method and found that the method had excellent image segmentation effect. Zhang et al [7] proposed an improved two-dimensional fuzzy Fisher algorithm for image segmentation and found that the algorithm had the performance of fast image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Threshold image target segmentation technology based on intelligent algorithms

Cai

Zhang

et al. 2020

Computer Optics

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This paper briefly introduces the optimal threshold calculation model and particle swarm optimization (PSO) algorithm for image segmentation and improves the PSO algorithm. Then the standard PSO algorithm and improved PSO algorithm were used in MATLAB software to make simulation analysis on image segmentation. The results show that the improved PSO algorithm converges faster and has higher fitness value; after the calculation of the two algorithms, it is found that the improved PSO algorithm is better in the subjective perspective, and the image obtained by the improved PSO segmentation has higher regional consistency and takes shorter time in the perspective of quantitative objective data. In conclusion, the improved PSO algorithm is effective in image segmentation.

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Image segmentation based on gray stretch and threshold algorithm

Cited by 33 publications

References 13 publications

Scene-Awareness Based Single Image Dehazing Technique via Automatic Estimation of Sky Area

Scene-Awareness Based Single Image Dehazing Technique via Automatic Estimation of Sky Area

Texture Detection of Aluminum Foil Based on Top‐Hat Transformation and Connected Region Segmentation

Threshold image target segmentation technology based on intelligent algorithms

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