Pixel intensity-based contrast algorithm (PICA) for image edges extraction (IEE)

• Main Authors: Al-Ghaili, Abbas M., Kasim, Hairoladenan, Al-Hada, Naif Mohammed, Othman, Marini, Saleh, Muneer Aziz, Jihua, Wang
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2020
• Subjects: TP Chemical technology
• Online Access: http://eprints.utm.my/id/eprint/90484/1/MuneerAzizSaleh2020_PixelIntensityBasedContrastAlgorithm.pdf; http://eprints.utm.my/id/eprint/90484/; http://dx.doi.org/10.1109/ACCESS.2020.3005050

In 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.