A Cognitive Modeling Approach for the Semantic Aggregation of Object Prototypes from Geometric Primitives: Toward Understanding Implicit Object Topology

Object recognition has developed to the most common approach for detecting arbitrary objects based on their appearance, where viewpoint dependency, occlusions, algorithmic constraints and noise are often hindrances for successful detection. Statistical pattern analysis methods, which are able to extract features from appearing images and enable the classification of the image content have reached a certain maturity and achieve excellent recognition on rather complex problems.

However, these systems do not seem directly scalable to human performance in a cognitive sense and appearance does not contribute to understanding the structure of objects. Syntactical pattern recognition methods are able to deal with structured objects, which may be constructed from primitives that were generated from extracted image features. Here, an eminent problem is how to aggregate image primitives in order to (re-) construct objects from such primitives.

In this paper, we propose a new approach to the aggregation of object prototypes by using geometric primitives derived from features out of image sequences and acquired from changing viewpoints. We apply syntactical rules for forming representations of the implicit object topology of object prototypes by a set of fuzzy graphs. Finally, we find a super-position of a prototype graph set, which can be used for updating and learning new object recipes in hippocampal like episodic memory that paves the way to cognitive understanding of natural scenes. The proposed implementation is exemplified with an object similar to the Necker cube.