Despite the fact that computer vision systems place an important role in our society, its structure does not follow any standard. The implementation of computer vision application require high performance platforms, such as GPUs or FPGAs, and very specialized image sensors. Nowadays, each manufacturer and research lab develops their own vision platform independently without considering any inter-compatibility. This Thesis introduces a new computer vision platform that can be used in a wide spectrum of applications. The characteristics of the platform has been defined after the implementation of three different computer vision applications, based on: SOC, FPGA and GPU respectively. As a result, a new modular platform has been defined with the following interchangeably elements: Sensor, Image Processing Pipeline, Processing Unit, Acceleration unit and Computer Vision Stack. This thesis also presents an FPGA synthetizable algorithm for performing geometric transformations on the fly, with a latency under 90 horizontal lines. All the software elements of this platform have an Open Source licence; over the course of this thesis, more than 200 patches have been contributed and accepted into different Open Source projects like the Linux Kernel, Yocto Project and U-boot, among others, promoting the required ecosystem for the creation of a community around this novel system. The platform has been validated in an industrial product, Qtechnology QT5022, used on diverse industrial applications; demonstrating the great advantages of a generic computer vision system as a platform for reusing elements and comparing results objectively