Hue provides a useful and intuitive cue that is used in a variety of computer vision applications. Hue is an attractive feature as it captures intrinsic information about the colour of objects or surfaces in a scene. Moreover, hue is invariant to confounding factors such as illumination brightness. However hue is not stable to all of the types of confounding factors that one might reasonably encounter. Specifically, the RGBs captured in images are sometimes raised to the power gamma. This is done for two reasons. First, to make the images suitable for display (since monitors have an intrinsic non-linearity). Second, applying a gamma is the simplest way to change the contrast in images. It has also been observed that digital cameras often apply a scene dependent gamma type function (which is unknown to the user). In this paper we show that a simple photometric ratio in log RGB space cancels both brightness and gamma. Furthermore, some simple manipulation reveals that the brightness/gamma invariant can usefully be interpreted as a hue in a log opponent colour space. We carried out indexing experiments to evaluate the usefulness of the derived hue correlate. In situations where gamma is held fixed, the new hue supports recognition equal to conventional definitions. In situations where gamma varies the new correlate supports better indexing. The new hue is also found to predict some psychophysical data quite accurately.
|Number of pages||10|
|Publication status||Published - Sep 2001|
|Event||The British Machine Vision Conference - Manchester, United Kingdom|
Duration: 10 Sep 2001 → 13 Sep 2001
|Conference||The British Machine Vision Conference|
|Period||10/09/01 → 13/09/01|