Attention can improve a simple model for object recognition

Object recognition is one of the most important tasks of the visual cortex. Even though it has been closely studied in the field of computer vision and neuroscience, the underlying processes in the visual cortex are not completely understood. A model that lately has gained attention is the HMAX model, which describes a feedforward hierarchical structure. This model shows a degree of scale and translation invariance. Our work explores and compares the HMAX model with a simpler model for object recognition emulating simple cells in the primary visual cortex, V1. This model shows a better performance than the HMAX model for translation and scale invariance experiments when an attentional mechanism is employed in realistic conditions.