We propose a segmentation method based on the geometric
representation of images as surfaces embedded in a higher
dimensional space, handling naturally multichannel
images. The segmentation is based on an active contour
embedded in the image manifold, along with a set of image
features. Hence, both data-fidelity and regularity terms
of the active contour are jointly optimized minimizing a
single Polaykov energy representing the hyper-surface of
this manifold. Compared to previous methods, our approach
is purely geometrical and does not require additional
weighting of the energy functional to drive the
segmentation to the image contours. The potential of such
a geometric approach lies in the general definition of
Riemannian manifolds, validating the proposed technique
for scale-space methods, volumetric data or catadioptric
images. We present here the segmentation technique called
Harmonic Active Contours, give an implementation for
multichannel images including gradient and region-based
segmentation criteria and apply it to color images.