Towards Real Time Data Reduction and Feature Abstraction for Robotics Vision
Rafael B., Q. Renato, Luiz Leite, M. Bruno, Luiz Marcos Garcia Gonçalves · InTech eBooks · 2010
We have built useful mechanisms involving data reduction and feature abstraction that could be integrated and tested in attention control and recognition behaviors. To do that, the first step is data reduction. By using an efficient down-sampling schema, a structure derived from the classical pyramid, however much more compact, is constructed in real-time (2.7 ms in a PC 2.0 GHz). Then computer vision techniques, as shape from stereo, shape from motion, and other feature extraction processes are applied in order to obtain the desired features (each single filter costs about 500 µs). By using this model, tested behaviors have accomplished realtime performance mainly due to the data reduction (about 1800% of gain) and abstraction of features performed. A moving fovea representation could be implemented on the top of this low-level vision model, allowing tasks as overt attention to be done in real-time, that can be applied to accelerate some tasks. So the main contribution of this work is the schema for data reduction and feature abstraction. Besides, other experiments involving attention and recog-