1 research outputs found
Semantic Deep Intermodal Feature Transfer: Transferring Feature Descriptors Between Imaging Modalities
Under difficult environmental conditions, the view of RGB cameras may be
restricted by fog, dust or difficult lighting situations. Because thermal
cameras visualize thermal radiation, they are not subject to the same
limitations as RGB cameras. However, because RGB and thermal imaging differ
significantly in appearance, common, state-of-the-art feature descriptors are
unsuitable for intermodal feature matching between these imaging modalities. As
a consequence, visual maps created with an RGB camera can currently not be used
for localization using a thermal camera. In this paper, we introduce the
Semantic Deep Intermodal Feature Transfer (Se-DIFT), an approach for
transferring image feature descriptors from the visual to the thermal spectrum
and vice versa. For this purpose, we predict potential feature appearance in
varying imaging modalities using a deep convolutional encoder-decoder
architecture in combination with a global feature vector. Since the
representation of a thermal image is not only affected by features which can be
extracted from an RGB image, we introduce the global feature vector which
augments the auto encoder's coding. The global feature vector contains
additional information about the thermal history of a scene which is
automatically extracted from external data sources. By augmenting the encoder's
coding, we decrease the L1 error of the prediction by more than 7% compared to
the prediction of a traditional U-Net architecture. To evaluate our approach,
we match image feature descriptors detected in RGB and thermal images using
Se-DIFT. Subsequently, we make a competitive comparison on the intermodal
transferability of SIFT, SURF, and ORB features using our approach