Higher order statistics in computer vision: analysis of images and detection of extraneous objects in images
The aim of this work is to present methods for classifying images and locating extraneous objects within images. Our methods make use of higher order statistics, transforms of data into the frequency domain, and characteristics of the resulting clusters.
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| Veröffentlicht in: | Annals of the New York Academy of Sciences 2002-12, Vol.980 (1), p.152-167 |
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| container_end_page | 167 |
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| container_issue | 1 |
| container_start_page | 152 |
| container_title | Annals of the New York Academy of Sciences |
| container_volume | 980 |
| creator | Farhadi, Ali Shahshahani, Mehrdad |
| description | The aim of this work is to present methods for classifying images and locating extraneous objects within images. Our methods make use of higher order statistics, transforms of data into the frequency domain, and characteristics of the resulting clusters. |
| doi_str_mv | 10.1111/j.1749-6632.2002.tb04895.x |
| format | Article |
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| fulltext | fulltext |
| identifier | ISSN: 0077-8923 |
| ispartof | Annals of the New York Academy of Sciences, 2002-12, Vol.980 (1), p.152-167 |
| issn | 0077-8923 1749-6632 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_72848694 |
| source | MEDLINE; Wiley Online Library Journals Frontfile Complete |
| subjects | Animals Cluster Analysis Fourier Analysis Humans Image Processing, Computer-Assisted Models, Theoretical Pattern Recognition, Automated Photography Reproducibility of Results |
| title | Higher order statistics in computer vision: analysis of images and detection of extraneous objects in images |
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