The aim of this thesis is to design an efficient and fast algorithm for image recognition to be embedded in low-power devices. By using the brain-inspired hyperdimensional computing (HD), the input image is directly projected into the binary space where all the computations are performed by the cheap xor-popcount operator. The HD alone cannot compete with the state of the art, for this reason some feature extractors have been added to the HD. The thesis is composed of six chapters. The first one is introductory and describes the state of the art, the related issues and how the HD could be exploited for Computer Vision applications.