Computer vision systems have to deal with thousands, sometimes millions of pixel values from each frame, and the computational complexity of many problems related to the interpretation of image data is very high. The task becomes especially difficult if a system has to operate in real-time. Within the Combinatorial Pyramid framework, the proposed computational model of attention integrates bottom-up and top-down factors for attention. Neurophysiologic studies have shown that, in humans, these two factors are the main responsible ones to drive attention. Bottom-up factors emanate from the scene and focus attention on regions whose features are sufficiently discriminative with respect to the features of their surroundings. On the other hand, top-down factors are derived from cognitive issues, such as knowledge about the current task. Specifically, the authors only consider in this model the knowledge of a given target to drive attention to specific regions of the image. With respect to previous approaches, their model takes into consideration not only geometrical properties and appearance information, but also internal topological layout. Once the focus of attention has been fixed to a region of the scene, the model evaluates if the focus is correctly located over the desired target. This recognition algorithm considers topological features provided by the pre-attentive stage. Thus, attention and recognition are tied together, sharing the same image descriptors.
Plan Recognition for Exploratory Learning Environments Using Interleaved Temporal Search