journal article·2023Open access

Color Edge Detection Using Multidirectional Sobel Filter and Fuzzy Fusion

Slim Ben Chaabane, Anas Bushnag

Computers, Materials & Continua· Volume 74· Issue 2

Abstract

A new model is proposed in this paper on color edge detection that uses the second derivative operators and data fusion mechanism. The second-order neighborhood shows the connection between the current pixel and the surroundings of this pixel. This connection is for each RGB component color of the input image. Once the image edges are detected for the three primary colors: red, green, and blue, these colors are merged using the combination rule. Then, the final decision is applied to obtain the segmentation. This process allows different data sources to be combined, which is essential to improve the image information quality and have an optimal image segmentation. Finally, the segmentation results of the proposed model are validated. Moreover, the classification accuracy of the tested data is assessed, and a comparison with other current models is conducted. The comparison results show that the proposed model outperforms the existing models in image segmentation.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Remote-Sensing Image Classification · Remote Sensing and Land Use · Spectroscopy and Chemometric Analyses

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (23)

Show source-provided references
1. Color image segmentation using feedforward neural networks with FCM10.1007/s11633-016-0975-5 ↗
2. Baldevbhai · IOSR Journal of Electronics and Communication Engineering · 2012 · 1 · 2410.9790/2834-0122445 ↗
3. Meta-heuristics for feature selection and classification in diagnostic breast cancer10.32604/cmc.2022.029605 ↗
4. Fuzzy hybrid coyote optimization algorithm for image thresholding10.32604/cmc.2022.026625 ↗
5. A hybrid agglomerative method for colour image segmentation
6. Color image segmentation using homogeneity method and data fusion techniques
7. Automatic leukaemia segmentation approach for blood cancer classification using microscopic images10.32604/cmc.2022.030879 ↗
8. A new method for the estimation of mass functions in the Dempster-Shafer’s evidence theory: application to color image segmentation
9. Color image segmentation using multi-level thresholding approach and data fusion techniques: Application in the breast cancer cells images
10. Improved skin lesions detection using color space and artificial intelligence techniques10.1080/09546634.2019.1708239 ↗
11. Improved skin lesion edge detection method using ant colony optimization
12. Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks10.1016/j.cmpb.2018.05.027 ↗
13. Breast cancer detection using image processing techniques
14. Color edges extraction using statistical features and automatic threshold technique: Application to the breast cancer cells10.1186/1475-925X-13-S1-I1 ↗
15. A color image edge detection algorithm based on color difference
16. Morphological transformation in color space based edge detection of skin lesion images10.1007/978-981-16-4149-7_23 ↗
17. A multi-level threshold method for edge detection and segmentation based on entropy
18. Edge detection in medical images with quasi high-pass filter based on local statistics10.1016/j.bspc.2017.08.011 ↗
19. A real-time cutting model based on finite element and order reduction10.32604/csse.2022.024950 ↗
20. Optimal multi-level thresholding using a two-stage Otsu optimization approach10.1016/j.patrec.2008.10.003 ↗
21. Implementation of Sobel, Prewitt, Roberts edge detection on FPGA
22. Cervical cancer detection in pap smear whole slide images using convNet with transfer learning and progressive resizing
23. A fast and accurate vascular tissue simulation model based on point primitive method10.32604/iasc.2021.013541 ↗