CLPSO-based Fuzzy Color Image Segmentation

Borji, Ali; Hamidi, M.; Moghadam, Amir Masoud Eftekhari

doi:10.1109/nafips.2007.383892

Cited by 16 publications

(9 citation statements)

References 17 publications

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“…2) Complex fuzzy measure Definition 2: [37] Assume A, B, and C be three CFSs in a universe of discourse X with membership degrees of x) respectively. A distance of complex fuzzy sets if a function h : CF S(X) × CF S (X) → [0, 1] if it satisfies some conditions as follows:…”

Section: ) Complex Fuzzy Setmentioning

confidence: 99%

Adaptive Spatial Complex Fuzzy Inference Systems With Complex Fuzzy Measures

et al. 2023

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Fuzzy inference systems, in general, and complex fuzzy inference systems, in particular, play an increasingly important role in many fields, such as change detection, image classification, recognition problems, etc. Despite being the well-known technique to solve with time series data, the rulebase still has the considered limitation because of the directly affecting the results as well as the processing time of these methods. To overcome this limitation, this study proposes an Adaptive spatial complex inference system that can automatically infer and adapt to the new remotely sensed image. In the proposed model, to predict the image of time t + 1, the system will generate a new rulebase according to this expected image. This new rulebase and the previous Co-Spatial-CFIS+ rulebase are evaluated using a complex fuzzy measure. This measure is built by determining the intersection domain between two rule spaces; this intersection value estimates removing, merging, or adding a newly generated rule into the current rulebase. Finally, a more suitable set of rules is obtained for image prediction. To illustrate the efficiency of the proposed approach, it is applied to the remote sensing cloud image data of the U.S. Navy. Our model evaluated the model's effectiveness in comparison to the state-of-the-art along studies in detecting changes in remote sensing cloud images. Moreover, the findings of the experiments revealed that the proposed model could improve the change detection results in terms of R 2 , RMSE, time-consuming, and the number of rules.INDEX TERMS Complex fuzzy inference system; remote sensing images; rule pruning; rule-based system; image change detection.

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Section: ) Complex Fuzzy Setmentioning

confidence: 99%

Adaptive Spatial Complex Fuzzy Inference Systems With Complex Fuzzy Measures

et al. 2023

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“…Specifically, for the identification of apple and grape diseases, various methods are proposed, which somehow manage to classify set of diseases with acceptable accuracy and sensitivity [19][20][21][22]. In unsupervised methods, range of algorithms are proposed including K-means clustering [23], global thresholding with morphological operations [24], graph cut methods [25], color segmentation [26], CLPSObased fuzzy color segmentation [27], and adaptive approaches [28], to name but a few. Bhivini et al [2] introduced a framework to classify infected regions in apples.…”

Section: Literature Reviewmentioning

confidence: 99%

A probabilistic segmentation and entropy-rank correlation-based feature selection approach for the recognition of fruit diseases

Khan

Akram

Sharif

et al. 2021

J Image Video Proc.

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Agriculture plays a critical role in the economy of several countries, by providing the main sources of income, employment, and food to their rural population. However, in recent years, it has been observed that plants and fruits are widely damaged by different diseases which cause a huge loss to the farmers, although this loss can be minimized by detecting plants’ diseases at their earlier stages using pattern recognition (PR) and machine learning (ML) techniques. In this article, an automated system is proposed for the identification and recognition of fruit diseases. Our approach is distinctive in a way, it overcomes the challenges like convex edges, inconsistency between colors, irregularity, visibility, scale, and origin. The proposed approach incorporates five primary steps including preprocessing,Standard instruction requires city and country for affiliations. Hence, please check if the provided information for each affiliation with missing data is correct and amend if deemed necessary. disease identification through segmentation, feature extraction and fusion, feature selection, and classification. The infection regions are extracted using the proposed adaptive and quartile deviation-based segmentation approach and fused resultant binary images by employing the weighted coefficient of correlation (CoC). Then the most appropriate features are selected using a novel framework of entropy and rank-based correlation (EaRbC). Finally, selected features are classified using multi-class support vector machine (MC-SCM). A PlantVillage dataset is utilized for the evaluation of the proposed system to achieving an average segmentation and classification accuracy of 93.74% and 97.7%, respectively. From the set of statistical measure, we sincerely believe that our proposed method outperforms existing method with greater accuracy.

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“…Researchers also used Fuzzy set and Fussy logic techniques for solving segmentation problem. Borji et al presented CLPSO-based Fuzzy color image segmentation [9]. Cheng et al used Fuzzy homogeneity approach for the segmentation of color image [10].…”

Section: A Brief Overview Of Related Workmentioning

confidence: 99%

Infected Fruit Part Detection using K-Means Clustering Segmentation Technique

Dubey¹,

Dixit²,

Singh³

et al. 2013

IJIMAI

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Nowadays, overseas commerce has increased drastically in many countries. Plenty fruits are imported from the other nations such as oranges, apples etc. Manual identification of defected fruit is very time consuming. This work presents a novel defect segmentation of fruits based on color features with K-means clustering unsupervised algorithm. We used color images of fruits for defect segmentation. Defect segmentation is carried out into two stages. At first, the pixels are clustered based on their color and spatial features, where the clustering process is accomplished. Then the clustered blocks are merged to a specific number of regions. Using this two step procedure, it is possible to increase the computational efficiency avoiding feature extraction for every pixel in the image of fruits. Although the color is not commonly used for defect segmentation, it produces a high discriminative power for different regions of image. This approach thus provides a feasible robust solution for defect segmentation of fruits. We have taken apple as a case study and evaluated the proposed approach using defected apples. The experimental results clarify the effectiveness of proposed approach to improve the defect segmentation quality in aspects of precision and computational time. The simulation results reveal that the proposed approach is promising

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CLPSO-based Fuzzy Color Image Segmentation

Cited by 16 publications

References 17 publications

Adaptive Spatial Complex Fuzzy Inference Systems With Complex Fuzzy Measures

Adaptive Spatial Complex Fuzzy Inference Systems With Complex Fuzzy Measures

A probabilistic segmentation and entropy-rank correlation-based feature selection approach for the recognition of fruit diseases

Infected Fruit Part Detection using K-Means Clustering Segmentation Technique

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