Finding "Anomalies" in an Arbitrary Image
Abstract
A fast and general method to extract "anomalies" in an arbitrary image is proposed. The basic idea is to compute a probability density for sub-regions in an image, conditioned upon the areas surrounding the sub-regions. Linear estimation and Independent Component Analysis (ICA) are combined to obtain the probability estimates. Pseudo non-parametric correlation is used to group sets of similar surrounding patterns, from which a probability for the occurrence of a given sub-region is derived. A carefully designed multi-dimensional histogram, based on compressed vector representations, enables efficient and high-resolution extraction of anomalies from the image. Our current (unoptimized) implementation performs anomaly extraction in about 30 seconds for a 640/spl times/480 image using a 700 MHz PC. Experimental results are included that demonstrate the performance of the proposed method.
Cite
Text
Honda and Nayar. "Finding "Anomalies" in an Arbitrary Image." IEEE/CVF International Conference on Computer Vision, 2001. doi:10.1109/ICCV.2001.937669Markdown
[Honda and Nayar. "Finding "Anomalies" in an Arbitrary Image." IEEE/CVF International Conference on Computer Vision, 2001.](https://mlanthology.org/iccv/2001/honda2001iccv-finding/) doi:10.1109/ICCV.2001.937669BibTeX
@inproceedings{honda2001iccv-finding,
title = {{Finding "Anomalies" in an Arbitrary Image}},
author = {Honda, Toshifumi and Nayar, Shree K.},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {2001},
pages = {516-523},
doi = {10.1109/ICCV.2001.937669},
url = {https://mlanthology.org/iccv/2001/honda2001iccv-finding/}
}