Early vision stages represent a considerably heavy computational load. A huge amount of data needs to be processed under strict timing and power requirements. Conventional architectures usually fail to adhere to the specifications in many application fields, especially when autonomous vision-enabled devices are to be implemented, like in lightweight UAVs, robotics or wireless sensor networks. A bioinspired architectural approach can be employed consisting of a hierarchical division of the processing chain, conveying the highest computational demand to the focal plane. There, distributed processing elements, concurrent with the photosensitive devices, influence the image capture and generate a pre-processed representation of the scene where only the information of interest for subsequent stages remains. These focal-plane operators are implemented by analog building blocks, which may individually be a little imprecise, but as a whole render the appropriate image processing very efficiently. As a proof of concept, we have developed a 176×144-pixel smart CMOS imager that delivers lighter but enriched representations of the scene. Each pixel of the array contains a photosensor and some switches and weighted paths allowing reconfigurable resolution and spatial filtering. An energy-based image representation is also supported. These functionalities greatly simplify the operation of the subsequent digital processor implementing the high level logic of the vision algorithm. The resulting figures, 5.6mW@30fps, permit the integration of the smart image sensor with a wireless interface module (Imote2 from Memsic Corp.) for the development of vision-enabled WSN applications.