Examining 4.2 Analysis
the scatter plots in Figure 6, one notices that recognition rate is effected by occlusion. At low occlusion values, no recognition failures are reported, while at high occlusion values, recognition failures dominate. This indicates that recognition will almost always work if sufficient model surface area is visible. The decrease in recognition success after a fixed level of occlusion is reached (70%) indicates that spin-image matching does not work well when only a small portion of the model is visible. This is no surprise since spin-image descriptiveness comes from accumulation of surface area around a point. In the middle of Figure 6 are shown the experimental recognition rates versus scene occlusion. The rates are computed using a gaussian weighted running average to avoid the problems with binning. These plots show that recognition rate remains high for both forms of compression until occlusion of around 70% is reached, then the successful recognition rate begins to fall off. Examining the experiment scatter plots in Figure 6, one notices that the effect of clutter on recognition is uniform across all levels of occlusion until a high level of clutter is reached. This indicates that spin-image matching is independent of the clutter in the scene. On the right in Figure 6, plots of recognition rate versus amount of clutter also show that recognition rate is fairly independent of clutter. As clutter increases, there are slight variations about a fixed recognition rate. Most likely, these variations are due to non-uniform sampling of recognition runs and are not actual trends with respect to clutter. Above a high level of clutter, the successful recognitions decline, but from the scatter plots we see that at high levels of clutter, the number of experiments is small, so conclusions about recognition rate can not be made. In all of the plots showing the effect of clutter and occlusion, the true-positive rates are higher for recognition with spin-images without compression when compared with the true-positive rates for recognition with compression. This validates the expected decrease in the accuracy of spin-image matching when using compressed spin-images. However, it should be noted that the recognition rate for both matching algorithms remain high. For all levels of clutter and occlusion, matching without compression has an average recognition rate of 90.0% and matching with compression has an average recognition rate of 83.2%. Furthermore, the false-positives rate for both algorithms are low and nearly the same. Our …