Colour provides important information in many image processing tasks such as object identification and
tracking. Different images of the same object frequently yield different colour values due to undesired
variations in lighting and the camera. In practice, controlling the source of these fluctuations is difficult,
uneconomical or even impossible in a particular imaging environment. This thesis is concerned with the
question of how to best align the corresponding clusters of colour histograms to reduce or remove the
effect of these undesired variations.
We introduce feature based histogram alignment (FBHA) algorithms that enable flexible alignment
transformations to be applied. The FBHA approach has three steps, 1) feature detection in the colour
histograms, 2) feature association and 3) feature alignment. We investigate the choices for these three
steps on two colour databases : 1) a structured and labeled database of RGB imagery acquired under controlled
camera, lighting and object variation and 2) grey-level video streams from an industrial inspection
application. The design and acquisition of the RGB image and grey-level video databases are a key contribution
of the thesis. The databases are used to quantitatively compare the FBHA approach against
existing methodologies and show it to be effective. FBHA is intended to provide a generic method for
aligning colour histograms, it only uses information from the histograms and therefore ignores spatial
information in the image. Spatial information and other context sensitive cues are deliberately avoided
to maintain the generic nature of the algorithm; by ignoring some of this important information we gain
useful insights into the performance limits of a colour alignment algorithm that works from the colour
histogram alone, this helps understand the limits of a generic approach to colour alignment
