Fusing Detected Humans in Multiple Perception Sensors Network

Fusing Animals and Humans

AI has many techniques and tools at its disposal, yet seems to be lacking some special “juice” needed to create a true being. We propose that the missing ingredients are a general theory of motivation and an operational understanding of natural language. The motivation part comes largely from our animal heritage: a real-world agent must continually respond to external events rather than depend ...

Fusing Multiple Multiband Images

We consider the problem of fusing an arbitrary number of multiband, i.e., panchromatic, multispectral, or hyperspectral, images belonging to the same scene. We use the well-known forward observation and linear mixture models with Gaussian perturbations to formulate the maximum-likelihood estimator of the endmember abundance matrix of the fused image. We calculate the Fisher information matrix f...

Fusing Multiple Neuroimaging Modalities to Assess Group Differences in Perception-Action Coupling

In the last few decades, non-invasive neuroimaging has revealed macro-scale brain dynamics that underlie perception, cognition and action. Advances in non-invasive neuroimaging target two capabilities; 1) increased spatial and temporal resolution of measured neural activity, and 2) innovative methodologies to extract brain-behavior relationships from evolving neuroimaging technology. We target ...

Fusing multiple Bayesian knowledge sources

We address the problem of information fusion in uncertain environments. Imagine there are multiple experts building probabilistic models of the same situation and we wish to aggregate the information they provide. There are several problems we may run into by naively merging the information from each. For example, the experts may disagree on the probability of a certain event or they may disagr...

Fusing Sensors for Occupancy Sensing in Smart Buildings