Autonomous robot navigation in outdoor environments remains a challenging and unsolved problem. A key issue is our ability to identify safe or navigable paths far enough ahead of the robot to allow smooth trajectories at acceptable speeds. Colour or texture-based labeling of safe path regions in image sequences is one way to achieve this far field prediction. A challenge for classifiers identif...

A distance for mixed nominal, ordinal and continuous data is developed by applying the Kullback–Leibler divergence to the general mixed-data model, an extension of the general location model that allows for ordinal variables to be incorporated in the model. The distance obtained can be considered as a generalization of the Mahalanobis distance to data with a mixture of nominal, ordinal and cont...

Superpixels have been widely used as a preprocessing step in various computer vision tasks. Spatial compactness and color homogeneity are the two key factors determining the quality of the superpixel representation. In this paper, these two objectives are considered separately and anisotropic superpixels are generated to better adapt to local image content. We develop a unimodular Gaussian gene...

We propose a new energy-based ([1]) online learning approach to the problem of extracting small features from high dimensional, noisy datasets. We demonstrate the power of this new technique by applying it to the Detection of cellular nuclei in large hyperspectral images. Thus, we prove that only a minimum number of random, wideband measurements is necessary to achieve efficient feature extract...

The dualistic structure of statistical manifolds in information geometry yields eight types (possibly mixed type) geodesic triangles passing through three given points, the triangle vertices. interior angles can sum up to $$\pi $$ like Euclidean/Mahalanobis flat geometry, or exhibit otherwise angle excesses defects. In this work, we initiate study dually spaces, termed Bregman manifolds, where ...

Joint Energy-based Model (JEM) of [11] shows that a standard softmax classifier can be reinterpreted as an energy-based model (EBM) for the joint distribution \(p(\boldsymbol{x}, y)\); resulting optimized to improve calibration, robustness and out-of-distribution detection, while generating samples rivaling quality recent GAN-based approaches. However, JEM exploits is inherently discriminative ...