The potential of the Internet of Things is leading to a paradigm shift with an ambitious long-term vision, in which battery-operated sensing nodes are empowered with sight and are capable of complex visual analysis tasks. Unfortunately, this is out of reach with the current technology. GreenEyes will develop a comprehensive set of new methodologies, algorithms and protocols, to empower wireless sensor networks with vision capabilities comparable to those achievable by power-eager smart camera systems. The key tenet is that most visual analysis tasks can be carried out based on a succinct representation of the image, which entails both global and local features, while it disregards the underlying pixel-level representation. Still, under severe energy constraints it is imperative to optimize the computation, the coding and the transmission of the features. On the computation and coding side, GreenEyes will tackle the problem by reversing the conventional compress-then-analyze paradigm. That is, image features are collected by sensing nodes, processed, and delivered to final destination(s) in order to enable higher level visual analysis tasks by means of either centralized or distributed detectors and classifiers, somewhat mimicking the processing of visual stimuli in the early visual system. The transmission of visual features is subject to tight application-dependent requirements (bandwidth/delay guarantees), and may be affected by network conditions. Therefore, on the communication side, GreenEyes will pursue the design of networking tools for wireless multimedia sensor networks for energy efficient distributed control, information delivery and in-network processing optimized for the visual analysis task.