Image Processing for Automated Flaw Detection and CMYK model for Color Image Segmentation using Type 2 Fuzzy Sets

In realistic image processing, it is a problem of image foreground extraction. For a large number of color image processing, an important requirement is the automation of the extraction process. In this paper, by automatically setting foreground seed, we improve the image existing segmentation algorithm; by automatically searching image segmentation region, we accomplish image segmentation with the GrabCut algorithm, which is based on Gaussian Mixture Model and boundary computing. The improved algorithm in this paper can achieve the automation of image segmentation, without user participation in the implementation process, at the same time, it improves the efficiency of image segmentation, and gets a good result of image segmentation in complex background.

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An interval type-2 FCM for color image segmentation

International Journal of Advanced Computer Research ◽

10.19101/ijacr.2019.940114 ◽

2020 ◽

Vol 10 (46) ◽

pp. 12-17 ◽

Cited By ~ 1

Author(s):

Abdullah Hamad ◽

Sadegh Aminifar ◽

Muhammadamin Daneshwar

Keyword(s):

Image Segmentation ◽

Color Image ◽

Color Image Segmentation ◽

Interval Type

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K-Hyperline Clustering-Based Color Image Segmentation Robust to Illumination Changes

Symmetry ◽

10.3390/sym10110610 ◽

2018 ◽

Vol 10 (11) ◽

pp. 610 ◽

Cited By ~ 3

Author(s):

Senquan Yang ◽

Pu Li ◽

HaoXiang Wen ◽

Yuan Xie ◽

Zhaoshui He

Keyword(s):

Image Processing ◽

Image Segmentation ◽

Clustering Algorithm ◽

Color Image ◽

Clustering Algorithms ◽

Color Image Segmentation ◽

Color Classification ◽

Clustering Problem ◽

Illumination Changes ◽

Segmentation Approach

Color image segmentation is very important in the field of image processing as it is commonly used for image semantic recognition, image searching, video surveillance or other applications. Although clustering algorithms have been successfully applied for image segmentation, conventional clustering algorithms such as K-means clustering algorithms are not sufficiently robust to illumination changes, which is common in real-world environments. Motivated by the observation that the RGB value distributions of the same color under different illuminations are located in an identical hyperline, we formulate color classification as a hyperline clustering problem. We then propose a K-hyperline clustering algorithm-based color image segmentation approach. Experiments on both synthetic and real images demonstrate the outstanding performance and robustness of the proposed algorithm as compared to existing clustering algorithms.

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Segmentación de Imágenes Basada en Entropía de Pixel

KnE Engineering ◽

10.18502/keg.v3i1.1456 ◽

2018 ◽

Vol 3 (1) ◽

pp. 523

Author(s):

Rony Caballero ◽

Aránzazu Berbey ◽

Alberto Cogley

Keyword(s):

Image Processing ◽

Image Segmentation ◽

Light Scattering ◽

Color Image ◽

Color Image Segmentation ◽

Preliminary Results

In this research, a new methodology for color image segmentation is proposed. In this approach, a pixel entropy derivative based rule is used. The algorithm is tested not only with good quality images, but also with some of them with light scattering and absortion problems. Preliminary results shows good performance of this algorithm.Keywords: Image processing, segmentation, entropy, feedback.

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Research on color image segmentation

Mongolian Journal of Agricultural Sciences ◽

10.5564/mjas.v25i03.1183 ◽

2018 ◽

Vol 25 (03) ◽

pp. 138-143

Author(s):

Wang He Xi Ge Tu ◽

Bolormaa D

Keyword(s):

Image Processing ◽

Image Analysis ◽

Image Segmentation ◽

Digital Image Processing ◽

Color Image ◽

Color Image Segmentation ◽

Primary Analysis ◽

Programming Tools ◽

Segmentation Methods ◽

Segmentation Image

The basic foundation for the development of the image processing is image segments. Primary analysis, such as analysis of images and visualization of images, begins with segmentation. Image segmentation is one of the important parts of digital image processing. Depending on the accuracy and accuracy of the segmentation, the results of the image analysis, including the size of the object, the size of the object, and so on. In the first section of this study, briefly describe the types of image segments. Also use Mathlab language's powerful modern programming tools to explore the image segmentation methods and compare the results. As a result of the experiment, it is more accurate to accurately measure the trajectory of the image segmentation of the image as a result of the Otsu-based method of B space. This will apply to further research. Өнгөний мэдээлэлд суурилсан дүрс сегментчлэх аргын судалгаа Хураангуй: Дүрс боловсруулах судалгааны ажлын үндсэн суурь нь дүрс сегментчлэл юм. Дүрсэнд анализ хийх, дүрсийг ойлгох зэрэг анхан шатны боловсруулалт нь дүрс сегментчлэхээс эхэлдэг. Дүрс сегментчлэл нь дижитал дүрс боловсруулалтын чухал хэсгүүдийн нэг юм. Сегментчлэлийг хэр зэрэг үнэн зөв, нарийвчлал сайтай хийснээс шалтгаалан, дараагийн дүрс таних, обьектын хэмжээ зэрэг дүрс шинжлэлийн алхамын үр дүн ихээхэн хамаардаг. Энэхүү судалгааны ажлын эхний хэсэгт дүрс сегментчлэх арга төрлүүдийн талаар товч танилцуулна. Мөн орчин үеийн програмчлалын хүчтэй хэрэгсэл болох Mathlab хэлний функцуудыг ашиглан дүрс сегментчилж гарсан үр дүнгийн харьцуулалтыг танилцууллаа. Туршилтын үр дүнд RGB өнгөний орон зайн B бүрэлдэхүүнд суурилсан Otsu-ийн аргийг ашиглан дүрсийг сементчилэх нь уламжлалт дүрс сегментчилэх аргаас нэн сайн үр дүнтай илүү нарийвчлалтай байна. Үүнийг цаашдын судалгааны ажилдаа хэрэглэх болно. Түлхүүр үг: RGB дүрс, босго (Threshold) утга, гистограм, Otsu-ийн арга, дүрс боловсруулалт

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