Snapshot Navigation in the Wavelet Domain
255
wavelet coefficients to deriving a bearing and we will combine orientational features from different levels, which will add more energy from the lower frequencies
which contain important information [31]. Finally, wavelets could be combined
with an infomax learning rule [2] or a neural network (NN) trained to recall the
route [23]. Alternatively, they could be used as the input to a biologically plausible network such as a mushroom body model [1]. As wavelets can be seen as
analogous to the processing performed in the early stages of vision, optimising
the wavelet coefficients or type for different NNs could not only improve navigation performance in robots but also shed light on aspects of the visual processing
in insects.
References
1. Ardin, P., Peng, F., Mangan, M., Lagogiannis, K., Webb, B.: Using an insect
mushroom body circuit to encode route memory in complex natural environments.
PLOS Comput. Biol. 12(2), 1–22 (2016)
2. Baddeley, B., Graham, P., Husbands, P., Philippides, A.: A model of ant route
navigation driven by scene familiarity. PLOS Comput. Biol. 8(1), 1–16 (2012)
3. Baddeley, B., Graham, P., Philippides, A., Husbands, P.: Holistic visual encoding
of ant-like routes: navigation without waypoints. Adapt. Behav. 19(1), 3–15 (2011)
4. Cartwright, B.A., Collett, T.S.: Landmark learning in bees. J. Comp. Physiol.
151(4), 521–543 (1983)
5. Collett, T.S., Graham, P., Durier, V.: Route learning by insects. Curr. Opin. Neurobiol. 13(6), 718–725 (2003)
6. Cooley, J.W., Tukey, J.W.: An algorithm for the machine calculation of complex
Fourier series. Math. Comput. 19(90), 297–301 (1965)
7. Differt, D., M¨ oller, R.: Insect models of illumination-invariant skyline extraction
from UV and green channels. J. Theor. Biol. 380, 444–462 (2015)
8. Franz, M.O., Sch¨ olkopf, B., Mallot, H.A., B¨ ulthoff, H.H.: Where did I take that
snapshot? Scene-based homing by image matching. Biol. Cybern. 79(3), 191–202
(1998)
9. Graham, P., Cheng, K.: Ants use the panoramic skyline as a visual cue during
navigation. Curr. Biol. 19(20), R935–R937 (2009)
10. Haar, A.: Zur Theorie der orthogonalen Funktionensysteme. Georg-AugustUniversitat, Gottingen (1909)
11. Harrison, J.F., Fewell, J.H., Stiller, T.M., Breed, M.D.: Effects of experience on
use of orientation cues in the giant tropical ant. Anim. Behav. (1989)
12. Khotanzad, A., Hong, Y.H.: Invariant image recognition by Zernike moments.
IEEE Trans. Pattern Anal. Mach. Intell. 12(5), 489–497 (1990)
13. Klotz, J.: Topographic orientation in two species of ants (hymenoptera: Formicidae). Insectes Soc. 34(4), 236–251 (1987)
14. Lee, C., Kim, D.: Visual homing navigation with Haar-like features in the snapshot.
IEEE Access 6, 33666–33681 (2018)
15. Lulham, A., Bogacz, R., Vogt, S., Brown, M.W.: An infomax algorithm can perform
both familiarity discrimination and feature extraction in a single network. Neural
Comput. 23(4), 909–926 (2011)
16. Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)
255
wavelet coefficients to deriving a bearing and we will combine orientational features from different levels, which will add more energy from the lower frequencies
which contain important information [31]. Finally, wavelets could be combined
with an infomax learning rule [2] or a neural network (NN) trained to recall the
route [23]. Alternatively, they could be used as the input to a biologically plausible network such as a mushroom body model [1]. As wavelets can be seen as
analogous to the processing performed in the early stages of vision, optimising
the wavelet coefficients or type for different NNs could not only improve navigation performance in robots but also shed light on aspects of the visual processing
in insects.
References
1. Ardin, P., Peng, F., Mangan, M., Lagogiannis, K., Webb, B.: Using an insect
mushroom body circuit to encode route memory in complex natural environments.
PLOS Comput. Biol. 12(2), 1–22 (2016)
2. Baddeley, B., Graham, P., Husbands, P., Philippides, A.: A model of ant route
navigation driven by scene familiarity. PLOS Comput. Biol. 8(1), 1–16 (2012)
3. Baddeley, B., Graham, P., Philippides, A., Husbands, P.: Holistic visual encoding
of ant-like routes: navigation without waypoints. Adapt. Behav. 19(1), 3–15 (2011)
4. Cartwright, B.A., Collett, T.S.: Landmark learning in bees. J. Comp. Physiol.
151(4), 521–543 (1983)
5. Collett, T.S., Graham, P., Durier, V.: Route learning by insects. Curr. Opin. Neurobiol. 13(6), 718–725 (2003)
6. Cooley, J.W., Tukey, J.W.: An algorithm for the machine calculation of complex
Fourier series. Math. Comput. 19(90), 297–301 (1965)
7. Differt, D., M¨ oller, R.: Insect models of illumination-invariant skyline extraction
from UV and green channels. J. Theor. Biol. 380, 444–462 (2015)
8. Franz, M.O., Sch¨ olkopf, B., Mallot, H.A., B¨ ulthoff, H.H.: Where did I take that
snapshot? Scene-based homing by image matching. Biol. Cybern. 79(3), 191–202
(1998)
9. Graham, P., Cheng, K.: Ants use the panoramic skyline as a visual cue during
navigation. Curr. Biol. 19(20), R935–R937 (2009)
10. Haar, A.: Zur Theorie der orthogonalen Funktionensysteme. Georg-AugustUniversitat, Gottingen (1909)
11. Harrison, J.F., Fewell, J.H., Stiller, T.M., Breed, M.D.: Effects of experience on
use of orientation cues in the giant tropical ant. Anim. Behav. (1989)
12. Khotanzad, A., Hong, Y.H.: Invariant image recognition by Zernike moments.
IEEE Trans. Pattern Anal. Mach. Intell. 12(5), 489–497 (1990)
13. Klotz, J.: Topographic orientation in two species of ants (hymenoptera: Formicidae). Insectes Soc. 34(4), 236–251 (1987)
14. Lee, C., Kim, D.: Visual homing navigation with Haar-like features in the snapshot.
IEEE Access 6, 33666–33681 (2018)
15. Lulham, A., Bogacz, R., Vogt, S., Brown, M.W.: An infomax algorithm can perform
both familiarity discrimination and feature extraction in a single network. Neural
Comput. 23(4), 909–926 (2011)
16. Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)