| 摘要 |
A given point of interest in an image is defined by two properties, a local attribute, such as color, and a neighborhood function that describes a similarity pattern. The color value is not influenced by nearby background regions of the image, and functions as a descriptor for each location. The neighborhood function distinguishes locations of similar color from one another, by capturing patterns of change in the local color. The neighborhood function measures the similarity between the local color and colors at nearby points, and reduces the measured similarity values that lie beyond contrast boundaries. Through the computation of such a transform for points of interest in an image, corresponding points in other images can be readily identified.
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