Study on Otsu Threshold Method for Image Segmentation Based on Genetic Algorithm

Xu, Zhi Bo; Chen, Pei Jiang; Yan, Shi; Wang, Tai Hua

doi:10.4028/www.scientific.net/amr.998-999.925

Cited by 3 publications

(2 citation statements)

References 1 publication

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“…In addition, quite remarkable findings were noticeable after manipulating the huge datasets using PSO centric SOMs compared to other algorithms. Since computer science generally addresses numerical data, thus for the manipulation purpose, the biological huge datasets were converted to binary values by using the Otsu method [56][57][58] for further process. For clear visualization and interpretation purpose, the datasets were represented considering the angles of proteins for detection of variation, patters and trends within the datasets.…”

Section: Resultsmentioning

confidence: 99%

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Self-organizing mapping based swarm intelligence for secondary and tertiary proteins classification

Kamal

Sarowar

Dey³

et al. 2017

Int. J. Mach. Learn. & Cyber.

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Proteins have a significant role in animals and human health. Interactions among proteins are complex and large. Proteins separations are challenging process in molecular biology. Computational tools help to simulate the analysis in order to reduce the training data into small testing data. Large proteins have been mapped using self-organizing maps (SOMs). Neural network based SOMs has a significant role in reducing the irregular shapes of proteins interactions. Iterative checking enables the organizations of all proteins. In next stage, particle swarm intelligence is applied to classify the proteins' families. In the current work, secondary (Two dimensional) and tertiary proteins (Three dimensional) proteins have been grouped. Two dimensional proteins contain fewer hydrocarbons than three dimensional proteins. For faster analysis, the angles of the proteins are taken into account. The SOMs is compared with Bounding Box approach. In final, the experimental evolutions show that swarm intelligence achieved faster processing through enabling less memory consumptions and time. Since PSO combines proteins datasets in fuzzy values, the compactness or integration of similar proteins are strong. On the other hand, Bounding Box uses the Crisp value. Therefore, it needs more space to organize the whole data. Without SOMs, swarm intelligence also results are poor due to the excessive time consuming and required storage area. Moreover, for almost all classification and clustering tools, it is observed that the overall classification task becomes slow, time consuming, space consuming and also less sensitive because of noises, irrelevant data in input datasets. Thus, the proposed SOM based PSO approach achieved less time consuming with efficient classification into secondary and tertiary proteins.

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Section: Resultsmentioning

confidence: 99%

“…However, the tertiary proteins generate two different angles. In the current work, Otsu method [56][57][58] is applied to check the angles of both these protein types. Basically, Otsu method helps to convert Secondary and tertiary proteins to binary values form where we get the idea about the angles of the both proteins classes.…”

Section: Introductionmentioning

confidence: 99%

Self-organizing mapping based swarm intelligence for secondary and tertiary proteins classification

Kamal

Sarowar

Dey³

et al. 2017

Int. J. Mach. Learn. & Cyber.

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Single image dehazing based on fusion of sky region segmentation

Zhou

2021

J. Phys.: Conf. Ser.

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In order to solve the problem of image sharpening with sky region, a single image dehazing method combining sky region segmentation was proposed. Firstly, a fusion sky segmentation method combining OTSU algorithm with Canny operator edge detection is proposed to divide foggy image into sky region and non-sky region. Secondly, a linear index was constructed for the sky region using the dark channel prior sky transmittance to modify the sky region transmittance. For the non-sky region, a fast guided filter was used to optimize the dark channel prior non-sky transmittance. The two were combined to obtain the final transmittance, and the atmospheric light value was optimized by means of average estimation. Finally, combined with the atmospheric scattering model, the image is reconstructed dehaze. The experimental results show that the proposed method can segment the sky more accurately, estimate the atmospheric light value and transmittance more accurately and efficiently, and has the advantages of high contrast, natural color and clear details.

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Multilevel Image Segmentation Using Modified Particle Swarm Optimization

De¹,

Haque

2017

Intelligent Analysis of Multimedia Information

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Particle Swarm Optimization (PSO) is a well-known swarm optimization technique. PSO is very efficient to optimize the image segmentation problem. PSO algorithm have some drawbacks as the possible solutions may follow the global best solution at one stage. As a result, the probable solutions may bound within that locally optimized solutions. The proposed chapter tries to get over the drawback of the PSO algorithm and proposes a Modified Particle Swarm Optimization (MfPSO) algorithm to segment the multilevel images. The proposed method is compared with the original PSO algorithm and the renowned k-means algorithm. Comparison of the above mentioned existing methods with the proposed method are applied on three real life multilevel gray scale images. For this purpose, three standard objective functions are applied to evaluate the quality of the segmented images. The comparison shows that the proposed MfPSO algorithm is done better than the PSO algorithm and the k-means algorithm to segment the real life multilevel gray scale images.

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Study on Otsu Threshold Method for Image Segmentation Based on Genetic Algorithm

Cited by 3 publications

References 1 publication

Self-organizing mapping based swarm intelligence for secondary and tertiary proteins classification

Self-organizing mapping based swarm intelligence for secondary and tertiary proteins classification

Single image dehazing based on fusion of sky region segmentation

Multilevel Image Segmentation Using Modified Particle Swarm Optimization

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