## 3 Results and Discussion  
The proposed approach was tested on two sets, one containing 16 scans and the other eight. The scans were acquired by the Riegl LMS Z360i laser scanner (angular resolution of \(\Delta\theta=\Delta\varphi=0.12^{\circ}\)) generating 2.25 million ranges (creating a 750 \(\times\) 3,000 pixels image), spanning 360\({}^{\circ}\) horizontally and 90\({}^{\circ}\) vertically with maximum ranges on order of 80 m. Seven, six-mega-pixel size images were acquired per scan using a Nikon-D100 camera with a 14 mm lens, and were processed in full resolution. Overlap between images was \(\sim\)13.5%, equivalent to \(\sim\)270 pixels. The first set covers a courtyard-like square and offers a typical urban environment that is dominated by manmade objects. The second set covers mainly an open park area, bordered by a castle facade, with trees and vegetation in a variety of forms and at different distances from the scanner (Figure 3). Both sets feature a significant amount of occlusions and clutter, featuring, cars, buildings, and other complex objects, in addition to trees. As a first step, the response of the object for different cues derived from the input channels was examined. Beginning with the range channel (Figure 3) the cornerness, sum of derivatives, and sum of the absolute values of the derivatives were tested (Figures 4-6). For each of the cues, the distributions related to \"tree\" and \"non-tree\" classes are presented. These are made in reference to manually  
For the intensity channel, the two cues tested were the intensity values and cornerness (Figures 7 and 8). Between the two, the cornerness has the more pronounced separation, relative to the intensity cue where tree and non-tree classes are mixed. With the RGB related cues (Figure 9) the Luv decomposition was tested for the \(u\) (Figure 10), \(v\) (Figure 11) and \(L\) (Figure 12) values. As can be seen, the \(L\) value has no contribution to the separation, while \(u\) and \(v\) show the ability to resolve trees and non-trees segments (their combine effect is evaluated in the following). The HSV model was tested for the hue and saturation channels. Contrasting the \(u\) and \(v\) channels, neither channel adequately separated the two classes (data not shown). Among all tested cues, the range- and intensity-cornerness values provide the highest descriptive power for tree related points. Following the testing of the single channel distinguishing power using a 1D histogram, we can test in a similar manner the distinguishing power of pairing two individual cues using a 2D histogram. The first natural cue coupling is the intensity and range cornerness measures since these two cues provide the most distinct separation individually. Their combination (Figure 13) shows indeed that the two enable separating the two classes, and that the correlation between the two classes is limited. Namely, each of them contributes to the separation (this can be noticed in the two-dimensional spread). Comparing the Hue-Saturation combination against the \(u\)-\(v\) combination (Figure 14(a,b) respectively), the \(u\)-\(v\) separation appear to perform better relative to the Hue-saturation. It is noted that the trees are leafless, and therefore this set may bias this conclusion. Even though the distinction power is limited compared to the range and intensity cornerness measures, they still show a level of separation which can be of value. Figure 8: Intensity cornerness values, and their tree/non-tree distribution. Figure 7: The intensity channel, and the intensity values tree/non-tree distribution. Figure 11: \(v\) values of the Luv representation of the color channel, and their tree/non-tree distribution. Figure 12: \(L\) values of the Luv representation of the color channel, and their tree/non-tree distribution. Figure 10: \(u\) values of the Luv representation of the color channel, and their tree/non-tree distribution. Figure 9: The RGB channel  
Figure 14: **(a)** 2D distribution of the combined _u-v_ cues.