Comparative Study of Color Image Segmentation by the Seeded Region Growing Algorithm

Charifi, Rajaa; Es-Sbai, Najia; Mansouri, Azadeh; Zennayi, Yahya

doi:10.1109/cist.2018.8596399

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…Chondro et al [9] proposed a computer-assisted region segmentation for plain chest radiographs that included enhancing avant-garde contrast-enhancing obscurity of the lung areas. Charifi et al [10] studied the seeded region growing (SRG) algorithm in 2 different color spaces as RGB and HSV. The implemented method were investigated for 3 different cases as utomated seed selection based on color and area features, region growth using Euclidean distance and overcome the over-segmentation.…”

Section: Related Workmentioning

confidence: 99%

Karınca Aslanı Optimizasyon Algoritması Kullanarak Görüntü Segmentasyonu İçin Modifiye Edilmiş Bölge Genişletme Yöntemi

Jama¹,

Baykan²

2020

European Journal of Science and Technology

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Image segmentation is a significant step in image processing that applies to various fields. These fields include machine vision, object detection, astronomy, biometric recognition systems (face, fingerprint, plate, and eye), medical imaging, video surveillance, and many other image-based technologies. Efficient image segmentation is one of the most important tasks and critical roles in automatic image processing. Especially in engineering studies, finding the most suitable solutions for problems is one of the important research topics. Bio-inspired algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Bat Algorithm (BAT), etc. are used to find the optimal solutions in search spaces and Ant Lion Optimization (ALO) is one of these algorithms. In recent years, bio-inspired algorithms are used to optimize the segmentation parameters of the images. This research proposes a modified region growing (RG) image segmentation approach using bio-inspired ALO. Region growing (RG) has three main problems as the selection of the right seeds, the number of seeds, and the region growing strategy. Therefore, ALO was used to solve seed selection problems in RG. In this study, firstly, the median filter was applied to the inputs to improve the quality of the images. Subsequently, the region growing segmentation was carried out using optimal seed points obtained from the ALO. For obtaining the optimal seeds, ALO was used to solve the limitations of RG during the segmentation process. The success of the proposed approach was tested using some images taken from the BSDS300 (Berkeley) dataset. The experimental results show that the proposed method segments almost all the images.

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Section: Related Workmentioning

confidence: 99%

Karınca Aslanı Optimizasyon Algoritması Kullanarak Görüntü Segmentasyonu İçin Modifiye Edilmiş Bölge Genişletme Yöntemi

Jama¹,

Baykan²

2020

European Journal of Science and Technology

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“…Region growing is an effective method for image segmentation [29]. The basic principle is to start from a seed point and find points around the seed point that meet the condition according to the specified criteria, then define them as the next seed points, and continuously iterate the whole process until there are no satisfying points around all the seed points.…”

Section: Using Region Growing Algorithm To Eliminate False Alarm Vehiclesmentioning

confidence: 99%

A moving vehicles extraction method in Satellite Videos

Dong

Yang

2020

MATEC Web Conf.

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With the continuous innovation of optical remote sensing technology and the increasing demand for spatial information, satellite videos, which can provide higher spatial and temporal resolution, have been paid a lot of attention. And moving vehicles extraction in satellite videos is one of the most important tasks. By analyzing the shortcomings of current satellite video moving vehicles extraction algorithms, and combining with the characteristics of satellite videos and moving vehicles, this paper proposes an algorithm to extract moving vehicles in satellite videos, that some vehicles are firstly separated from the background by using image extreme points and mean differences, and then the moving vehicles are extracted by joint detection of inter-frame vehicles motion. At the same time, based on the extracted moving vehicles, we also propose a method that can extract road masks by using only three frames. Finally, we use Jilin-1 satellite video data to prove the proposed methods in the experiment. And also this paper has compared the propose methods with another two algorithms, where the results show that the proposed methods greatly improve the accuracy and quality of moving vehicles detection in satellite videos.

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A Fast Abnormal Data Cleaning Algorithm for Performance Evaluation of Wind Turbine

Wang

2020

IEEE Trans. Instrum. Meas.

160

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Comparative Study of Color Image Segmentation by the Seeded Region Growing Algorithm

Cited by 5 publications

References 11 publications

Karınca Aslanı Optimizasyon Algoritması Kullanarak Görüntü Segmentasyonu İçin Modifiye Edilmiş Bölge Genişletme Yöntemi

Karınca Aslanı Optimizasyon Algoritması Kullanarak Görüntü Segmentasyonu İçin Modifiye Edilmiş Bölge Genişletme Yöntemi

A moving vehicles extraction method in Satellite Videos

A Fast Abnormal Data Cleaning Algorithm for Performance Evaluation of Wind Turbine

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