AI Article Synopsis
- The article introduces a new mathematical model that applies a learning method similar to deep learning for solving inverse problems, particularly in the context of vague and abstract data representations.
- It develops a framework using L4 numbers, vectors, and matrices to process qualitative information related to spatial relationships, aiding in knowledge representation in AI and modeling reasoning.
- The proposed model has been validated through effective image reconstruction applications, showing promising results in denoising when using qualitative data from various scanning technologies.
Article Abstract
This article proposes the application of a new mathematical model of for solving inverse problems using a learning method, which is similar to using deep learning. In general, the spots represent vague figures in abstract "information spaces" or crisp figures with a lack of information about their shapes. However, crisp figures are regarded as a special and limiting case of spots. A basic mathematical apparatus, based on L4 numbers, has been developed for the representation and processing of qualitative information of elementary spatial relations between spots. Moreover, we defined L4 vectors, L4 matrices, and mathematical operations on them. The developed apparatus can be used in Artificial Intelligence, in particular, for knowledge representation and for modeling qualitative reasoning and learning. Another application area is the solution of inverse problems by learning. For example, this can be applied to image reconstruction using ultrasound, X-ray, magnetic resonance, or radar scan data. The introduced apparatus was verified by solving problems of reconstruction of images, utilizing only qualitative data of its elementary relations with some scanning figures. This article also demonstrates the application of a spot-based inverse Radon algorithm for binary image reconstruction. In both cases, the spot-based algorithms have demonstrated an effective denoising property.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921052 | PMC |
| http://dx.doi.org/10.3390/s23031247 | DOI Listing |
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