Cubixel: a novel paradigm in image processing using three-dimensional pixel representation

This paper introduces the innovative concept of the Cubixel—a three-dimensional representation of the traditional pixel—alongside the derived metric, Volume of the Void (VoV), which measures spatial disparities within images. By converting pixels into Cubixels, we can analyze the image’s 3D properti...

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Veröffentlicht in:	Multimedia tools and applications Jg. 84; H. 23; S. 26215 - 26265
1. Verfasser:	Aburass, Sanad
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.07.2025 Springer Nature B.V
Schlagworte:	Artificial intelligence Comparative studies Computer Communication Networks Computer Science Computer vision Data Structures and Information Theory Deep learning Edge detection Image processing Image segmentation Machine learning Medical imaging Morphology Multimedia Information Systems Pixels Representations Special Purpose and Application-Based Systems Track 6: Computer Vision for Multimedia Applications Urban environments Volumetric analysis Image processing Image representation Computer vision
ISSN:	1573-7721, 1380-7501, 1573-7721
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Abstract	This paper introduces the innovative concept of the Cubixel—a three-dimensional representation of the traditional pixel—alongside the derived metric, Volume of the Void (VoV), which measures spatial disparities within images. By converting pixels into Cubixels, we can analyze the image’s 3D properties, thereby enriching image processing and computer vision tasks. Utilizing Cubixels, we’ve developed algorithms for advanced image segmentation, edge detection, texture analysis, and feature extraction, yielding a deeper comprehension of image content. Our empirical experimental results on benchmark images and datasets showcase the applicability of these concepts. Further, we discuss future applications of Cubixels and VoV in various domains, particularly in medical imaging, where they have the potential to significantly enhance diagnostic processes. By interpreting images as complex ‘urban landscapes’, we envision a new frontier for deep learning models that simulate and learn from diverse environmental conditions. The integration of Cubixels into deep learning architectures promises to revolutionize the field, providing a pathway towards more intelligent, context-aware artificial intelligence systems. With this groundbreaking work, we aim to inspire future research that will unlock the full potential of image data, transforming both theoretical understanding and practical applications. Our code is available at https://github.com/sanadv/Cubixel .
AbstractList	This paper introduces the innovative concept of the Cubixel—a three-dimensional representation of the traditional pixel—alongside the derived metric, Volume of the Void (VoV), which measures spatial disparities within images. By converting pixels into Cubixels, we can analyze the image’s 3D properties, thereby enriching image processing and computer vision tasks. Utilizing Cubixels, we’ve developed algorithms for advanced image segmentation, edge detection, texture analysis, and feature extraction, yielding a deeper comprehension of image content. Our empirical experimental results on benchmark images and datasets showcase the applicability of these concepts. Further, we discuss future applications of Cubixels and VoV in various domains, particularly in medical imaging, where they have the potential to significantly enhance diagnostic processes. By interpreting images as complex ‘urban landscapes’, we envision a new frontier for deep learning models that simulate and learn from diverse environmental conditions. The integration of Cubixels into deep learning architectures promises to revolutionize the field, providing a pathway towards more intelligent, context-aware artificial intelligence systems. With this groundbreaking work, we aim to inspire future research that will unlock the full potential of image data, transforming both theoretical understanding and practical applications. Our code is available at https://github.com/sanadv/Cubixel . This paper introduces the innovative concept of the Cubixel—a three-dimensional representation of the traditional pixel—alongside the derived metric, Volume of the Void (VoV), which measures spatial disparities within images. By converting pixels into Cubixels, we can analyze the image’s 3D properties, thereby enriching image processing and computer vision tasks. Utilizing Cubixels, we’ve developed algorithms for advanced image segmentation, edge detection, texture analysis, and feature extraction, yielding a deeper comprehension of image content. Our empirical experimental results on benchmark images and datasets showcase the applicability of these concepts. Further, we discuss future applications of Cubixels and VoV in various domains, particularly in medical imaging, where they have the potential to significantly enhance diagnostic processes. By interpreting images as complex ‘urban landscapes’, we envision a new frontier for deep learning models that simulate and learn from diverse environmental conditions. The integration of Cubixels into deep learning architectures promises to revolutionize the field, providing a pathway towards more intelligent, context-aware artificial intelligence systems. With this groundbreaking work, we aim to inspire future research that will unlock the full potential of image data, transforming both theoretical understanding and practical applications. Our code is available at https://github.com/sanadv/Cubixel.
Author	Aburass, Sanad
Author_xml	– sequence: 1 givenname: Sanad orcidid: 0000-0002-7757-3953 surname: Aburass fullname: Aburass, Sanad email: saburass@luther.edu, saburass@miu.edu organization: Department of Computer Science, Luther College, Department of Computer Science, Maharishi International University
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Cites_doi	10.1049/ipr2.12419 10.2147/NAN.S66439 10.1109/JBHI.2023.3336726 10.1155/2018/7068349 10.1016/j.image.2013.06.003 10.1007/s10462-021-10061-9 10.1007/s10462-022-10176-7 10.1016/j.vlsi.2019.07.005 10.1007/s11042-020-09634-7 10.1007/s41095-020-0174-8 10.1007/978-3-030-30493-5_48 10.14569/IJACSA.2020.0111216 10.1016/j.autcon.2021.103675 10.1109/ACCESS.2019.2956508 10.1109/TPAMI.2020.2982166 10.3844/jcssp.2022.757.769 10.1016/S0927-0507(05)80124-0 10.1007/s11831-023-09919-8 10.1109/JSTARS.2021.3076005 10.1109/TPAMI.2021.3053577 10.1007/978-94-010-0510-4_8 10.3390/electronics10101187 10.1007/s11042-019-08070-6 10.1007/978-3-031-47448-4_5 10.1016/j.injury.2022.04.013 10.1007/s00371-021-02166-7 10.1016/j.imu.2023.101393 10.1038/sdata.2018.161 10.1007/s41095-022-0271-y 10.1016/j.neucom.2018.01.091 10.1090/conm/453 10.1109/TIP.2022.3184250 10.1016/j.eswa.2021.114574
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SubjectTerms	Artificial intelligence Comparative studies Computer Communication Networks Computer Science Computer vision Data Structures and Information Theory Deep learning Edge detection Image processing Image segmentation Machine learning Medical imaging Morphology Multimedia Information Systems Pixels Representations Special Purpose and Application-Based Systems Track 6: Computer Vision for Multimedia Applications Urban environments Volumetric analysis
Title	Cubixel: a novel paradigm in image processing using three-dimensional pixel representation
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