# Development of Height Indicators using Omnidirectional Images and Global Appearance Descriptors
## 5 Experiments and Results
### Results
Therefore, the results obtained indoors (Figure 17) show how the linear trend tends to degrade and the standard deviation tends to be higher in the case of the greater height gaps when using the panoramic image. This behaviour is specially remarkable when the 2D-DFT Vertical Phase method is used (Figure 17c). The techniques based on the orthographic projection present again a quite linear behaviour with a relatively low deviation, since they gather elements that are located at a similar distance from the camera (mainly the floor plane). The use of local-features presents the same general problems (lack of linearity and relatively high deviation in high altitude gaps). Figure 17: Results of the experiments in conditions (**c1**) (blue continuous) and (**c2**) (green dashed curves), considering all the combinations _method-image projection-descriptor_ shown in Table 3. Indoor images. (**a**) Method 1 + Pano. Im. + FS. (**b**) Method 1 + Pano. Im. + 2D-DFT. (**c**) Method 2 + Pano. Im. + 2D-DFT. (**d**) Method 3 + Orthogr. View + FS. (**e**) Method 3 + Orthogr. View + 2D-DFT. (**f**) Method 4 + Pano. Im. + FS. (**g**) Method 4 + Pano. Im. + 2D-DFT. (**h**) Method 4 + Orthogr. View + FS. (**i**) Method 4 + Orthogr. View + 2D-DFT. (**j**) Method 4 + Unit Sphere Proj. + FFT. (**k**) Method 5 + SURF. (**l**) Method 5 + SURF + RANSAC. Finally, Figures 20 and 21 present the results of the experiment in condition (**c3**) (considering positive and negative gaps respectively), according to the configurations shown in Table 4. These results show again a quite linear behaviour, which is similar, in several cases, to the behaviour of local-appearance methods, and the ability of the algorithms to distinguish between positive and negative displacements. It is worth highlighting the results of the orthonormal projection because they present, in general, a quite linear behaviour with a relatively low standard deviation. In general, outdoor experiments present a more linear tendency comparing to indoors. Also, the height indicators present clearly higher absolute values when working with indoor images, except for methods based on the orthographic projection. Multiscale analysis techniques and the CRS applied to the orthographic projection are the techniques that present a lower difference between indoor and outdoor images and the standard deviation of their results is relatively low. Figure 19: Two sample panoramic scenes captured indoors from different heights. The objects contained in the scenes experience movements with different magnitude. Figure 18: Variation of the angle of incidence of rays from two points of the environment that are situated at a different distance when the height of the visual system changes. Figure 20: Results of the experiments in conditions (**c3**) using outdoor (blue continuous curves) and indoor (green dashed curves) sets of images and considering all the combinations _method-image projection-descriptor_ shown in Table 3. Only positive vertical height gaps are considered in this experiment. (**a**) Method 1 + Pano. Im. + FS. (**b**) Method 1 + Pano. Im. + 2D-DFT. (**c**) Method 2 + Pano. Im. + 2D-DFT. (**d**) Method 3 + Orthogr. View + FS. (**e**) Method 3 + Orthogr. View + 2D-DFT. (**f**) Method 4 + Pano. Im. + FS. (**g**) Method 4 + Pano. Im. + 2D-DFT. (**h**) Method 4 + Orthogr. View + FS. (**i**) Method 4 + Orthogr. View + 2D-DFT. (**j**) Method 4 + Unit Sphere Proj. + SFT. (**k**) Method 5 + SURF. (**l**) Method 5 + SURF + RANSAC.