Function-valued image segmentation using functional kernel density estimation

Introduction. More and more image acquisition systems record high-dimensional vectors for each pixel, as it is the case for hyperspectral imaging or dynamic PET imaging, to cite only them. In hyperspectral imaging, each pixel is associated with a light-spectrum containing up to several hundreds of radiance values, each corresponding to narrow spectral bands. In dynamic PET images, each pixel is associated with a time activity curve giving a radioactivity measurement throughout the (discretized) time after radiotracer injection. In both cases, the data accessible for each pixel is a vector coming from the discretization of a function with physical meaning. It is thus interesting to extend classical image processing techniques on function-valued images.