Month: February 2015

Abstract

Central catadioptric omnidirectional images exhibit serious nonlinear distortions due to the involved quadratic mirrors. Therefore, features based on the conventional pin-hole model are hard to achieve satisfactory performances when directly applied to the distorted omnidirectional images. This paper analyzes the catadioptric geometry to facilitate modeling the nonlinear distortions of omnidirectional images. Different to the conventional imaging model, the prior information is considered in catadioptric system. A parameterized neighborhood mapping model is proposed to efficiently calculate the neighborhood of an object based on its measurable radial distance in the image plane. On the basis of the parameterized nonlinear model, a distortion-invariant fragment-based joint-feature mixture model of Gaussian is presented for human target tracking in omnidirectional vision. Under the framework of Gaussian Mixture Model, the problem of feature matching is converted into the feature clustering. The joint probability distribution of a joint-feature class is modeled by a mixture of Gaussian. A weight contribution mechanism is designed to flexibly weight the fragments contribution based on their responses, which leads to a robust tracking even under serious partial occlusion. Finally, experiments validate the advantage of the proposed algorithm over other conventional approaches.
full text

Abstract

Cognitive robotic systems nowadays are intensively involving learning algorithms to achieve highly adaptive and intelligent behaviors, including actuation, sensing, perception and adaptive control. Deep learning has emerged as an effective approach in image-based robotic perception and actions. Towards cognitive robotic perception based on deep learning, this paper focuses the Constrained Restricted Boltzmann Machine (RBM) on visual images for sparse feature representation. Inspired by sparse coding, the sparse constraints are performed on the hidden layer of RBM to obtain sparse and effective feature representation from perceived visual images. The RBM with Sparse Constraint (RBMSC) is proposed with a generalized optimization problem, where the constraints are applied on the probability density of hidden units directly to obtain more sparse representation. This paper presents three novel RBM variants, namely L 1-RBM, L 2-RBM, and L 1/2-RBM constrained by L 1-norm, L 2-norm, and L 1/2-norm on RBM, respectively. A Deep Belief Network with two hidden layers is built for comparison between each RBM variants. The experiments on MNIST database (Mixed National Institute of Standards and Technology database) show that the L 1/2-RBM can obtain more sparse representation than RBM, L 1-RBM, L 2-RBM, and Sparse-RBM (SRBM) in terms of sparseness metric. For further verification, the proposed methods are still tested on MNIST Variations dataset. The recognition results from perceived images in MNIST and MNIST Variations demonstrate that our proposed constrained RBM variants are feasible for object cognitive and perception, and the proposed L 1/2-RBM and L 1-RBM outperforms RBM and SRBM in terms of object recognition.

full text