Retraction Note: Underwater image segmentation based on computer vision and research on recognition algorithm

Hybrid image segmentation within a computer vision hierarchy A generic model of a computer vision system is presented which highlights the critical role of image segmentation. A hybrid segmentation approach, utilising both edge-based and region-based techniques, is proposed for improved quality of segmentation. An image segmentation architecture is outlined and test results are presented and discussed.

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Image Segmentation Methods for Flood Monitoring System

Water ◽

10.3390/w12061825 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1825

Author(s):

Nur Muhadi ◽

Ahmad Abdullah ◽

Siti Bejo ◽

Muhammad Mahadi ◽

Ana Mijic

Keyword(s):

Computer Vision ◽

Image Segmentation ◽

Digital Images ◽

Rapid Development ◽

Jaccard Index ◽

Monitoring Systems ◽

Dice Similarity Coefficient ◽

Hybrid Technique ◽

Segmentation Methods ◽

Flood Monitoring

Flood disasters are considered annual disasters in Malaysia due to their consistent occurrence. They are among the most dangerous disasters in the country. Lack of data during flood events is the main constraint to improving flood monitoring systems. With the rapid development of information technology, flood monitoring systems using a computer vision approach have gained attention over the last decade. Computer vision requires an image segmentation technique to understand the content of the image and to facilitate analysis. Various segmentation algorithms have been developed to improve results. This paper presents a comparative study of image segmentation techniques used in extracting water information from digital images. The segmentation methods were evaluated visually and statistically. To evaluate the segmentation methods statistically, the dice similarity coefficient and the Jaccard index were calculated to measure the similarity between the segmentation results and the ground truth images. Based on the experimental results, the hybrid technique obtained the highest values among the three methods, yielding an average of 97.70% for the dice score and 95.51% for the Jaccard index. Therefore, we concluded that the hybrid technique is a promising segmentation method compared to the others in extracting water features from digital images.

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A Novel Neutrosophic Image Segmentation Based on Improved Fuzzy C-Means Algorithm (NIS-IFCM)

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420550113 ◽

2019 ◽

Vol 34 (05) ◽

pp. 2055011

Author(s):

Jing Zhao ◽

Xiaoli Wang ◽

Ming Li

Keyword(s):

Computer Vision ◽

Particle Swarm Optimization ◽

Image Segmentation ◽

Classical Problem ◽

Local Optimum ◽

The Novel ◽

Swarm Optimization ◽

Novel Method ◽

Fuzzy C Means Algorithm ◽

Novel Algorithm

Image segmentation is a classical problem in the field of computer vision. Fuzzy [Formula: see text]-means algorithm (FCM) is often used in image segmentation. However, when there is noise in the image, it easily falls into the local optimum, which results in poor image boundary segmentation effect. A novel method is proposed to solve this problem. In the proposed method, first, the image is transformed into a neutrosophic image. In order to improve the ability of global search, a combined FCM based on particle swarm optimization (PSO) is proposed. Finally, the proposed algorithm is applied to the neutrosophic image segmentation. The results of experiments show that the novel algorithm can eliminate image noise more effectively than the FCM algorithm, and make the boundary of the segmentation area clearer.

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Virtual scene dynamic interactive image segmentation technology based on OpenGL module

International Journal of Electrical Engineering Education ◽

10.1177/0020720919891080 ◽

2019 ◽

pp. 002072091989108

Author(s):

Li Jing-Hui ◽

Gan Sheng-Jiang

Keyword(s):

Image Segmentation ◽

Expectation Maximization Algorithm ◽

Gaussian Mixture ◽

Recognition Algorithm ◽

Experimental Simulation ◽

Virtual Scene ◽

Interactive Image Segmentation ◽

Multi Scale ◽

Gauss Mixture ◽

Drawing Method

A virtual scene dynamic interactive image segmentation technology based on OpenGL sky module is proposed to improve the performance of image dynamic interactive recognition algorithm and the recognition effect of image segmentation. First, an automatic dynamic interactive image segmentation algorithm is proposed, a probability segmentation model of Gauss mixture fracture based on multi-scale crack enhancement filter is established, and expectation maximization algorithm is used to determine the parameters of GMM (Gaussian Mixture Model) and the underlying segmentation; second, the drawing function of OpenGL is used to draw the graph, and the drawing method needs to be calculated. That is to say, a mathematical model is established, which converts the graph into the calculation of vertex coordinates for drawing. It uses an extended cost function to guide dynamic interactive image segmentation using a set of cracks from the population to be segmented. Finally, the validity of the algorithm is verified by experimental simulation.

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Research on Underwater Polarization Image Segmentation Inspired by Biological Optic Nerve

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.347-350.2178 ◽

2013 ◽

Vol 347-350 ◽

pp. 2178-2184

Author(s):

Hui Bin Wang ◽

Yu Rong Wu ◽

Jie Shen ◽

Zhe Chen

Keyword(s):

Neural Network ◽

Image Segmentation ◽

Network Model ◽

Neural Network Model ◽

Underwater Image ◽

The Neural Network ◽

Intensity Information ◽

Segmentation Methods ◽

Polarization Image ◽

Feedback Neural Network

Due to effects of the light by water and other particles, the quality of underwater image will degrade. The traditional underwater image segmentation methods based on intensity and spectrum have difficulty in determining boundary. Inspired by the visual system of mantis shrimps, this paper constructed a feedback neural network model, in which the parameters were optimized using machine learning method. Based on this model, we combine the polarization and intensity information to achieve the underwater polarization image segmentation. The results of experiment prove that the neural network model designed in this paper can improve the accuracy of underwater image segmentation.

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