Publication:
Pixel Intensity-Based Contrast Algorithm (PICA) for Image Edges Extraction (IEE)

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dc.citedby1
dc.contributor.authorAl-Ghaili A.M.en_US
dc.contributor.authorKasim H.en_US
dc.contributor.authorAl-Hada N.M.en_US
dc.contributor.authorOthman M.en_US
dc.contributor.authorSaleh M.A.en_US
dc.contributor.authorJihua W.en_US
dc.contributor.authorid26664381500en_US
dc.contributor.authorid57203863798en_US
dc.contributor.authorid55976109600en_US
dc.contributor.authorid24824928800en_US
dc.contributor.authorid55567294600en_US
dc.contributor.authorid55929419400en_US
dc.date.accessioned2023-05-29T08:12:57Z
dc.date.available2023-05-29T08:12:57Z
dc.date.issued2020
dc.descriptionEdge detection; Extraction; Medical imaging; Pixels; Code complexity; Complex background; Computation time; Open sources; Pixel intensities; Real-time application; Single images; Video motion; Image enhancementen_US
dc.description.abstractIn this paper, images' pixels are exploited to extract objects' edges. This paper has proposed a Pixel Intensity based Contrast Algorithm (PICA) for Image Edges Extraction (IEE). This paper highlights three contributions. Firstly, IEE process is fast and PICA has less computation time when processing different images' sizes. Secondly, IEE is simple and uses a $2\times 4$ mask which is different from other masks where it doesn't require while-loop(s) during processing images. Instead, it has adopted an if-conditional procedure to reduce the code complexity and enhance computation time. That is, the reason why this design is faster than other designs and how it contributes to IEE will be explained. Thirdly, design and codes of IEE and its mask are available, made an open source, and in-detail presented and supported by an interactive file; it is simulated in a video motion design. One of the PICA's features and contributions is that PICA has adopted to use less while-loop(s) than traditional methods and that has contributed to the computation time and code complexity. Experiments have tested 526 samples with different images' conditions e.g., inclined, blurry, and complex-background images to evaluate PICA's performance in terms of computation time, enhancement rate for processing a single image, accuracy, and code complexity. By comparing PICA to other research works, PICA consumes 5.7 mS to process a single image which is faster and has less code complexity by $u\times u$. Results have shown that PICA can accurately detect edges under different images' conditions. Results have shown that PICA has enhanced computation time rate for processing a single image by 92.1% compared to other works. PICA has confirmed it is accurate and robust under different images' conditions. PICA can be used with several types of images e.g., medical images and useful for real-time applications. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9125896
dc.identifier.doi10.1109/ACCESS.2020.3005050
dc.identifier.epage119220
dc.identifier.scopus2-s2.0-85088087619
dc.identifier.spage119200
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85088087619&doi=10.1109%2fACCESS.2020.3005050&partnerID=40&md5=6c020ffc47738d591714039126b76892
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25697
dc.identifier.volume8
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.titlePixel Intensity-Based Contrast Algorithm (PICA) for Image Edges Extraction (IEE)en_US
dc.typeArticleen_US
dspace.entity.typePublication
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