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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Bibliographic Details
Published in:Multimedia tools and applications Vol. 84; no. 23; pp. 26215 - 26265
Main Author: Aburass, Sanad
Format: Journal Article
Language:English
Published: New York Springer US 01.07.2025
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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 .
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ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-024-20081-6