Combined structure and motion extraction from visual data using evolutionary active learning

We present a novel stereo vision modeling framework that generates approximate, yet physically-plausible representations of objects rather than creating accurate models that are computationally expensive to generate. Our approach to the modeling of target scenes is based on carefully selecting a small subset of the total pixels available for visual processing. To achieve this, we use the estimation-exploration algorithm (EEA) to create the visual models: a population of three-dimensional models is optimized against a growing set of training pixels, and periodically a new pixel that causes disagreement among the models is selected from the observed stereo images of the scene and added to the training set. We show here that using only 5 % of the available pixels, the algorithm can generate approximate models of compound objects in a scene. Our algorithm serves the dual goals of extracting the 3D structure and relative motion of objects of interest by modeling the target objects in terms...

• Type: Conference
• Year: 2009
• Where: GECCO
• Authors: Krishnanand N. Kaipa, Josh C. Bongard, Andrew N. Meltzoff
• Keywords: Algorithm, Compound Object, GECCO 2009, Optimization, Vision Modeling Framework