Change Detection in Synthetic Aperture Radar Images Based on Fuzzy Active Contour Models and Genetic Algorithms

Shi, Jiao; Wu, Jiaji; Paul, Anand; Jiao, Licheng; Gong, Maoguo

doi:10.1155/2014/870936

Cited by 25 publications

(10 citation statements)

References 44 publications

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“…For enhancing the quality of the video and multimedia traffic, the work in [17] was found. The authors of this work proposed techniques for triggering handover based on the quality of multimedia information.…”

Section: Handover Triggeringmentioning

confidence: 99%

A Velocity-Aware Handover Trigger in Two-Tier Heterogeneous Networks

2018

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Abstract:The unexpected change in user equipment (UE) velocity is recognized as the primary explanation for poor handover quality. In order to resolve this issue, while limiting ping-pong (PP) events we carefully and dynamically optimized handover parameters for each UE unit according to its velocity and the coverage area of the access point (AP). In order to recognize any variations in velocity, we applied Allan variance (AVAR) to the received signal strength (RSS) from the serving AP. To assess our approach, it was essential to configure a heterogeneous network context (LTE-WiFi) and interconnect Media-Independent Handover (MIH) and Proxy Mobile IPv6 (PMIPv6) for seamless handover. Reproduction demonstrated that our approach does not only result in a gain in relatively accurate velocity but in addition reduces the number of PP and handover failures (HOFs).

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Section: Handover Triggeringmentioning

confidence: 99%

A Velocity-Aware Handover Trigger in Two-Tier Heterogeneous Networks

2018

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“…In Jiao Shi et al (2014) [48] an unsupervised change detection approach for synthetic aperture radar images based on a fuzzy active contour model and a genetic algorithm was presented. The aim was to enhance the active contour model (ACM) by employing IT2 FS to properly handle uncertainties in the difference images (DIs).…”

Section: T2 Fs In Edge Detectionmentioning

confidence: 99%

Review of Recent Type-2 Fuzzy Image Processing Applications

et al. 2017

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This paper presents a literature review of applications using type-2 fuzzy systems in the area of image processing. Over the last years, there has been a significant increase in research on higher-order forms of fuzzy logic; in particular, the use of interval type-2 fuzzy sets and general type-2 fuzzy sets. The idea of making use of higher orders, or types, of fuzzy logic is to capture and represent uncertainty that is more complex. This paper is focused on image processing systems, which includes image segmentation, image filtering, image classification and edge detection. Various applications are presented where general type-2 fuzzy sets, interval type-2 fuzzy sets, and interval-value fuzzy sets are used; some are compared with the traditional type-1 fuzzy sets and others methodologies that exist in the literature for these areas in image processing. In all accounts, it is shown that type-2 fuzzy sets outperform both traditional image processing techniques as well as techniques using type-1 fuzzy sets, and provide the ability to handle uncertainty when the image is corrupted by noise.

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“…Recently, various schemes have been implemented to enhance the quality of multimedia traffic [4], but such schemes are yet to offer good estimates of the traffic load and throughput.…”

Section: Astesj Issn: 2415-6698mentioning

confidence: 99%