3 accepted papers
Designing the Kalman filter for nonlinear robot systems with theoretical guarantees is challenging, especially when the dynamics model is unavailable. This paper proposes a data-driven Kalman filter algorithm using kernel-based Koop-man operators for unknown nonlinear robot systems. First, the Koopm…
In image labeling, local representations for image units (pixels, patches or superpixels) are usually generated from their surrounding image patches, thus long-range contextual information is not effectively encoded. In this paper, we introduce recurrent neural networks (RNNs) to address this issue.…
We adopt Convolutional Neural Networks (CNN) as our parametric model to learn discriminative features and classifiers for local patch classification. As visually similar pixels are indistinguishable from local context, we alleviate such ambiguity by putting a global scene constraint. We estimate the…