Adaptive automatic object recognition in single and multi-modal sensor data

For single-modal data, object recognition and classification in a 3D point cloud is a non-trivial process due to the nature of the data collected from a sensor system where the signal can be corrupted by noise from the environment, electronic system, A/D converter, etc. Therefore, an adaptive system with a specific desired tolerance is required to perform classification and recognition optimally. The feature-based pattern recognition algorithm described below, is generalized for solving a particular global problem with minimal change. Since for the given class set, a feature set must be extracted accordingly. For instance, man-made urban object classification, rural and natural objects, and human organ classification would require different and distinct feature sets. This study is to compare the adaptive automatic object recognition in single sensor and the distributed adaptive pattern recognition in multi-sensor fusion. The similarity in automatic object recognition between single-sensor and multi-sensor fusion is the ability to learn from experiences and decide on a given pattern. Their main difference is that the sensor fusion makes a decision from the decisions of all sensors whereas the single sensor requires a feature extraction for a decision.