Efficient computation of the Hutchinson metric between digitized images
The Hutchinson metric is a natural measure of the discrepancy between two images for use in fractal image processing. An efficient solution to the problem of computing the Hutchinson metric between two arbitrary digitized images is considered. The technique proposed here, based on the shape of the o...
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| Veröffentlicht in: | IEEE transactions on image processing 2004-12, Vol.13 (12), p.1581-1588 |
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| container_title | IEEE transactions on image processing |
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| creator | Drakopoulos, V. Nikolaou, N.P. |
| description | The Hutchinson metric is a natural measure of the discrepancy between two images for use in fractal image processing. An efficient solution to the problem of computing the Hutchinson metric between two arbitrary digitized images is considered. The technique proposed here, based on the shape of the objects as projected on the digitized screen, can be used as an effective way to establish the error between the original and the, possibly compressed, decoded image. To test the performance of our method, we apply it to compare pairs of fractal objects, as well as to compare real-world images with the corresponding reconstructed ones. |
| doi_str_mv | 10.1109/TIP.2004.837550 |
| format | Article |
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An efficient solution to the problem of computing the Hutchinson metric between two arbitrary digitized images is considered. The technique proposed here, based on the shape of the objects as projected on the digitized screen, can be used as an effective way to establish the error between the original and the, possibly compressed, decoded image. To test the performance of our method, we apply it to compare pairs of fractal objects, as well as to compare real-world images with the corresponding reconstructed ones.</description><identifier>ISSN: 1057-7149</identifier><identifier>EISSN: 1941-0042</identifier><identifier>DOI: 10.1109/TIP.2004.837550</identifier><identifier>PMID: 15575153</identifier><identifier>CODEN: IIPRE4</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Algorithms ; Applied sciences ; Artificial Intelligence ; Cluster Analysis ; Color ; Compressed ; Computational efficiency ; Computational geometry ; Computer Graphics ; Computer Simulation ; Decoding ; Digitization ; Exact sciences and technology ; Fractal analysis ; Fractals ; Gray-scale ; Hutchinson metric ; Image coding ; image comparison ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; Image processing ; Imaging, Three-Dimensional - methods ; Informatics ; Information Storage and Retrieval - methods ; Information, signal and communications theory ; Mathematical models ; Neural networks ; Numerical Analysis, Computer-Assisted ; Pattern Recognition, Automated - methods ; Reproducibility of Results ; Sensitivity and Specificity ; Shape ; Signal and communications theory ; Signal processing ; Signal Processing, Computer-Assisted ; Signal representation. 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An efficient solution to the problem of computing the Hutchinson metric between two arbitrary digitized images is considered. The technique proposed here, based on the shape of the objects as projected on the digitized screen, can be used as an effective way to establish the error between the original and the, possibly compressed, decoded image. 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| subjects | Algorithms Applied sciences Artificial Intelligence Cluster Analysis Color Compressed Computational efficiency Computational geometry Computer Graphics Computer Simulation Decoding Digitization Exact sciences and technology Fractal analysis Fractals Gray-scale Hutchinson metric Image coding image comparison Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image processing Imaging, Three-Dimensional - methods Informatics Information Storage and Retrieval - methods Information, signal and communications theory Mathematical models Neural networks Numerical Analysis, Computer-Assisted Pattern Recognition, Automated - methods Reproducibility of Results Sensitivity and Specificity Shape Signal and communications theory Signal processing Signal Processing, Computer-Assisted Signal representation. Spectral analysis Signal, noise Subtraction Technique Telecommunications and information theory Testing |
| title | Efficient computation of the Hutchinson metric between digitized images |
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