Fuzzy Set Methods in Computer Vision

Keller, James M.; Krishnapuram, Raghu

doi:10.1007/978-1-4615-3640-6_6

Cited by 18 publications

(2 citation statements)

References 27 publications

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“…Further, the improvement of the performance of the power grid in electric vehicle parking with the assistance of fuzzy logic was analyzed by Hussain et al [26]. Keller and Krishnapuram [27] studied FS mechanisms in computer vision. Keller and Matsakis [28] described high-level computer vision employing the notion of FS.…”

Section: A Literature Reviewmentioning

confidence: 99%

Decision-Making Approach Based on Bipolar Complex Fuzzy Uncertain Linguistic Aggregation Operators

Mahmood,

Rehman,

Emam

et al. 2024

IEEE Access

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There are many computer vision technologies available, each having advantages and disadvantages of its own. Effective visual data analysis requires choosing the best technology and giving its deployment top priority. A strong framework for decision-making (DM) is required, one that can manage the ambiguities, imprecision, dual aspects, and linguistic terms present in real-world computer vision applications. Thus, in this script, we investigate the DM framework under the structure of a bipolar complex fuzzy uncertain linguistic set (BCFULS). The theory of BCFULS is also devised in this manuscript, which can model the data that contains ambiguities, extra fuzzy information, dual aspects, and linguistic terms simultaneously. For this DM framework, we inaugurate averaging/geometric aggregation operators (AOs) within the structure of BCFULS and analyze the related properties. After that, we employ the inaugurated DM framework to prioritize the numerous types of computer vision by considering artificial data and achieve that "Feature Matching" is the finest computer vision. Finally, this script contains a comparative analysis of this work with numerous current works to depict the supremacy and advantages of the invented work.

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Section: A Literature Reviewmentioning

confidence: 99%

Decision-Making Approach Based on Bipolar Complex Fuzzy Uncertain Linguistic Aggregation Operators

Mahmood,

Rehman,

Emam

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“… Analysis of discriminate parameters: In image processing, there are many techniques based on fuzzy logic for segmentation, detection, contrast enhancement (Keller et al 1992) and extraction (Pal et al, 1986).  Clustering algorithms: The aim of these algorithms is to label a set of data into C groups, so that obtained groups contain the most possible similar individuals.…”

Section: Data Descriptionmentioning

confidence: 99%