By Ting-Jun Fan
Surface houses play a vital position in lots of belief projects. item attractiveness, navigation, and inspection use floor houses ex tensively. Characterizing surfaces at various scales in given info is frequently the 1st and doubtless an important step. such a lot early study in ma chine conception depended on basically very coarse characterization of surfaces. within the previous few years, floor characterization has been receiving due awareness. Dr. T. J. Fan is among the only a few researchers who designed and im plemented a whole procedure for item attractiveness. He studied matters re lated to characterization of surfaces within the context of item attractiveness, after which makes use of the gains therefore built for spotting items. He makes use of a multi-view illustration of three-D gadgets for reputation, and he devel ops innovations for the segmentation of diversity photographs to procure beneficial properties for popularity. His matching procedure additionally permits him to acknowledge gadgets from their partial perspectives within the presence of different occluding gadgets. The efficacy of his procedure is proven in lots of examples.
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Extra info for Describing and Recognizing 3-D Objects Using Surface Properties
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.
3, will be referred to as Method 2. • Recently, an alternative method has been developed. This method uses Canny's edge operator  to detect curvature zero-crossings which correspond to surface discontinuities, and uses a method developed by Saint-Marc and Medioni  to detect the curvature extrema. 4, will be referred to as Method 3. 3. Surface Segmentation and Description Range Image ! IGaussian Smoothing I ! IDirectional Curvature Computation I K,d,9, () ! = 0°,45°,90° ,135° ! Extrema and Zero Crossings IScale 'Iracking (along IT) I !
2. Block diagram of Method 1. 2. Detecting Surface Features Range Image ! Smoothed Image ! IPrincipal Curvatures Computation I ! 11:1, 11:2, ! tP IExtrema and Zero Crossings of I 11:1 ! 3. Block diagram of Method 2. Each of these three methods produce a set of points of interest. The next step is to link these points into curves of interest. Finally, the curves corresponding to depth or surface discontinuities are extended as necessary to become the boundaries of closed patches. 5. Each step is explained in detail below.
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