The design and the implementation of algorithms on FPGA-based architectures, is a complex task, above all for image processing. Many vision applications (video monitoring, obstacle detection from a vehicle) require real time performance. This paper analyzes only a classical function involved in these applications: pixel characterization by an attribute vector, and pixel classification as belonging or not to an interest class. Typical attributes are color and texture. Color is described by the chrominance given by the a and b coordinates in the CIE-Lab color space. Texture is only computed from the L* coordinate, describing the local intensity variations in a neighborhood of every pixel. AdaBoost has been selected in order to learn how to classify every pixel from its attribute vector. From a learning data base, it is learnt off line how to select and combine a given number of weak classifiers; then, the classifier parameters are loaded on an FPGA-based kit. This paper proposes different architectures and presents some results obtained from images acquired from a robot, in order to classify a pixel as Ground or Obstacle.
['Mario Alberto Ibarra-Manzano', 'Michel Devy', 'Jean-Louis Boizard']
Real-time classification based on color and texture attributes on an FPGA-based architecture