Vision is perhaps the most important sense for humans. Among the different complex
tasks accomplished by the Human Visual System, the categorization is a fundamental process that
allows humans to effectively interpret their surroundings efficiently and rapidly. Computer Vision
researchers are increasingly using algorithms from Machine Learning to build robust and reusable
machine vision systems that act taking into account the visual content of images. Since learning
is a key component of biological vision systems, the design of artificial vision systems that learn
and adapt represent one of the most important trend in modern Computer Vision research. Despite
the advances in the context of single sensor imaging devices, this technology is still quite far
from the ability of automatically categorize and exploit the visual content of the scene during (or
after) acquisition time. Different constraints should be considered in order to transfer the ability
of inferring the category of a scene in imaging devices domain. Indeed, these devices have limited
resources in terms of memory and computational power, and the image data format change over
time through the imaging pipeline (i.e., from Bayer Pattern at acquisition time to JPEG format
after acquisition time). This Chapter presents Computer Vision and Machine Learning techniques
within the application contexts of scene recognition and red-eye detection. The techniques introduced
here could be used in building complex imaging pipeline in which image categorization
(e.g., scene recognition, red-eye detection) is exploited to drive other tasks (e.g., white balance,
red eye removal).
