  Vision and touch are two of the important sensing modalities for humans and
they offer complementary information for sensing the environment. Robots could
also benefit from such multi-modal sensing ability. In this paper, addressing
for the first time (to the best of our knowledge) texture recognition from
tactile images and vision, we propose a new fusion method named Deep Maximum
Covariance Analysis (DMCA) to learn a joint latent space for sharing features
through vision and tactile sensing. The features of camera images and tactile
data acquired from a GelSight sensor are learned by deep neural networks. But
the learned features are of a high dimensionality and are redundant due to the
differences between the two sensing modalities, which deteriorates the
perception performance. To address this, the learned features are paired using
maximum covariance analysis. Results of the algorithm on a newly collected
dataset of paired visual and tactile data relating to cloth textures show that
a good recognition performance of greater than 90\% can be achieved by using
the proposed DMCA framework. In addition, we find that the perception
performance of either vision or tactile sensing can be improved by employing
the shared representation space, compared to learning from unimodal data.
