  Humanoid robots are designed to operate in human centered environments. They
face changing, dynamic environments in which they need to fulfill a multitude
of challenging tasks. Such tasks differ in complexity, resource requirements,
and execution time. Latest computer architectures of humanoid robots consist of
several industrial PCs containing single- or dual-core processors. According to
the SIA roadmap for semiconductors, many-core chips with hundreds to thousands
of cores are expected to be available in the next decade. Utilizing the full
power of a chip with huge amounts of resources requires new computing paradigms
and methodologies.
  In this paper, we analyze a resource-aware computing methodology named
Invasive Computing, to address these challenges. The benefits and limitations
of the new programming model is analyzed using two widely used computer vision
algorithms, the Harris Corner detector and SIFT (Scale Invariant Feature
Transform) feature matching. The result indicate that the new programming model
together with the extensions within the application layer, makes them highly
adaptable; leading to better quality in the results obtained.
