By Ting-Jun Fan

ISBN-10: 1461244668

ISBN-13: 9781461244660

ISBN-10: 1461287871

ISBN-13: 9781461287872

Surface homes play a vital function in lots of belief projects. item popularity, navigation, and inspection use floor homes ex tensively. Characterizing surfaces at diverse scales in given information is usually the 1st and doubtless crucial step. such a lot early examine in ma chine notion trusted basically very coarse characterization of surfaces. within the previous couple of years, floor characterization has been receiving due consciousness. Dr. T. J. Fan is among the only a few researchers who designed and im plemented a whole process for item attractiveness. He studied concerns re lated to characterization of surfaces within the context of item reputation, after which makes use of the beneficial properties therefore built for spotting items. He makes use of a multi-view illustration of 3-D items for reputation, and he devel ops options for the segmentation of variety pictures to acquire gains for reputation. His matching technique additionally permits him to acknowledge gadgets from their partial perspectives within the presence of different occluding items. The efficacy of his strategy is confirmed in lots of examples.

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**Example text**

From these, zero-crossings and extrema in the 4 directions are computed. The advantage, of course, is that the tracking. becomes easier as it is performed along a single dimension, and the disadvantage is that a merging phase is necessary. 2, will be referred to as Method 1. • If the range images are relatively noise-free and shape exhibits itself at one level only (as is the case in most range images that we have scanned), then we use the straightforward implementation in which we compute first the two principal curvatures, then the zero-crossings and the extrema of the largest principal curvature.

This method is faster than method 1 since curvature is computed at only one scale for each pixel while, in Method 1, four directional curvatures are computed. 2. 10. Features detected for "cup" image using Method 2. space tracking is not used in Method 2, and this saves time, too. However, since only one (1' is used to smooth the range image, choosing the value of (1' crucially influences the results, and the localization problem becomes serious if (1' is too large. Basically, Method 2 is more suitable for those range images with less noise so that a smaller (1' can be chosen.

2. Detecting Surface Features 30 While these steps are rather straightforward in concept, their implementation requires resolution of many detailed issues such as how to compute curvature, which curvature properties to use, and how to combine information from different scales. 2 Detecting Surface Features The properties of curvature as they relate to the input were observed for one-dimensional signals. Extension to the 2-dimensional case requires some caution: It seems natural to compute at every point the two principal curvatures and to concentrate on the behavior of the one with the largest magnitude.

### Describing and Recognizing 3-D Objects Using Surface Properties by Ting-Jun Fan

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