Stand-alone applications of vision are severely constrained by their limited power budget. This is one of the
main reasons why vision has not yet been widely incorporated into wireless sensor networks. For them, image
processing should be suscribed to the sensor node in order to reduce network traffic and its associated power
consumption. In this scenario, operating the conventional acquisition-digitization-processing chain is unfeasible
under tight power limitations. A bio-inspired scheme can be followed to meet the timing requirements while
maintaining a low power consumption. In our approach, part of the low-level image processing is conveyed to the
focal-plane thus speeding up system operation. Moreover, if a moderate accuracy is permissible, signal processing
is realized in the analog domain, resulting in a highly efficient implementation. In this paper we propose a circuit
to realize dynamic texture segmentation based on focal-plane spatial bandpass filtering of image subdivisions.
By the appropriate binning, we introduce some constrains into the spatial extent of the targeted texture. By
running time-controlled linear diffusion within each bin, a specific band of spatial frequencies can be highlighted.
Measuring the average energy of the components in that band at each image bin the presence of a targeted
texture can be detected and quantified. The resulting low-resolution representation of the scene can be then
employed to track the texture along an image flow. An application specific chip, based on this analysis, is being
developed for natural spaces monitoring by means of a network of low-power vision systems.