[Show abstract][Hide abstract] ABSTRACT: In this paper, we present a monocular camera based terrain classification scheme. The uniqueness of the proposed scheme is that it inherently incorporates spatial smoothness while segmenting a image, without requirement of post-processing smoothing methods. The algorithm is extremely fast because it is build on top of a Random Forest classifier. We present comparison across features and classifiers. The baseline algorithm uses color, texture and their combination with classifiers such as SVM and Random Forests. We further enhance the algorithm through a label transfer method. The efficacy of the proposed solution can be seen as we reach a low error rates on both our dataset and other publicly available datasets.
[Show abstract][Hide abstract] ABSTRACT: In this paper, we propose a convex optimization based approach for piecewise planar reconstruction. We show that the task
of reconstructing a piecewise planar environment can be set in an L
∞ based Homographic framework that iteratively computes scene plane and camera pose parameters. Instead of image points, the
algorithm optimizes over inter-image homographies. The resultant objective functions are minimized using Second Order Cone
Programming algorithms. Apart from showing the convergence of the algorithm, we also empirically verify its robustness to
error in initialization through various experiments on synthetic and real data. We intend this algorithm to be in between
initialization approaches like decomposition methods and iterative non-linear minimization methods like Bundle Adjustment.