Implicit Modeling of Object Topology with Guidance from Temporal View Attention

Abstract

Object recognition developed to the most common approach of detecting arbitrary objects based on their appearance. However, viewpoint dependency, occlusions, algorithmic constraints, and noise are hindrances for proper object detection from a single view. As blob based segmentation cannot support learning and understanding of the object under consideration, contour based approaches are more prospective. As a consequence of aforementioned obstacles, objects are segmented often partly with more or less drop outs in contour that yields poor recognition performance. Since recognition of the "yet unknown" by the mammalian brain is supported by curiosity and experimental willingness, unknown objects are observed at least from a number of different viewpoints. These different views are considered by cognitive processes, yielding an implicit view of the object under observation. It is the objective of this paper to present an approach based on findings from biological studies and cognitive science, which enables the cognitive investigation of natural scenes and their further cognitive understanding. We proposed in another paper the architecture and a simulation of the first five bottom layers implementing the striate visual cortex as the first level of cognitive modeling of behaviors. In this work we focus on the aggregation layer, which forms object prototypes from geon recipes. The proposed implementation is exemplified again with the Necker cube.