A computer-implemented method of producing a segmented image of a scene, the segmented image including pixels each assigned to a respective one of a set of segments, the method comprising: providing a plurality of images of the scene, each image corresponding to a respective acquisition of a physical signal, the plurality of images including at least two images corresponding to different physical signals of different natures, the plurality of images including at least an infrared image and one or both of an RGB image and a depth image, wherein the infrared image is a thermal image; and generating the segmented image based on the plurality of images, by determining a distribution of labels that minimizes an energy defined on a Markov Random Field graph comprising nodes and edges, each node corresponding to a respective pixel and being associated to a respective label, each edge corresponding to a respective pair of pixels having a distance below a predetermined threshold, the labels corresponding to all different assignments of a pixel to a respective one of the set of segments, the energy comprising a smoothness term that penalizes assignment of pairs of pixels that correspond to an edge to different segments, the penalization for any given pair decreasing as a difference between physical signal vectors corresponding to the pixels of the given pair increases, wherein the physical signal vectors are vectors each assigned to a respective pixel, wherein each coordinate of the physical signal vectors being the value of a respective physical signal acquired by a corresponding one of the plurality of images, the plurality of images being pixel-to-pixel aligned, the energy further comprising a data term that penalizes assignment of pixels each to any segment, the penalization being based for each pixel on a respective probability distribution that provides for each of the set of segments a respective probability that the pixel belongs to the segment independently of other pixels, the penalization for any given pixel increasing as the probability provided by the probability distribution respective to the given pixel decreases, the probability distributions being learnt based on a clustering of pixels, the clustering of pixels being based on the physical signal vectors corresponding to the pixels, the clustering being performed via a spectral clustering, comprising an over-segmentation of the pixels, the spectral clustering being followed by an iteration of a fusion of segments, the number of segments reached when the iteration ends corresponding to the labels available for the Markov Random Field minimization. providing a plurality of images of the scene, each image corresponding to a respective acquisition of a physical signal, the plurality of images including at least two images corresponding to different physical signals of different natures, the plurality of images including at least an infrared image and one or both of an RGB image and a depth image, wherein the infrared image is a thermal image; and generating the segmented image based on the plurality of images, by determining a distribution of labels that minimizes an energy defined on a Markov Random Field graph comprising nodes and edges, each node corresponding to a respective pixel and being associated to a respective label, each edge corresponding to a respective pair of pixels having a distance below a predetermined threshold, the labels corresponding to all different assignments of a pixel to a respective one of the set of segments, the energy comprising a smoothness term that penalizes assignment of pairs of pixels that correspond to an edge to different segments, the penalization for any given pair decreasing as a difference between physical signal vectors corresponding to the pixels of the given pair increases, wherein the physical signal vectors are vectors each assigned to a respective pixel, wherein each coordinate of the physical signal vectors being the value of a respective physical signal acquired by a corresponding one of the plurality of images, the plurality of images being pixel-to-pixel aligned, the energy further comprising a data term that penalizes assignment of pixels each to any segment, the penalization being based for each pixel on a respective probability distribution that provides for each of the set of segments a respective probability that the pixel belongs to the segment independently of other pixels, the penalization for any given pixel increasing as the probability provided by the probability distribution respective to the given pixel decreases, the probability distributions being learnt based on a clustering of pixels, the clustering of pixels being based on the physical signal vectors corresponding to the pixels, the clustering being performed via a spectral clustering, comprising an over-segmentation of the pixels, the spectral clustering being followed by an iteration of a fusion of segments, the number of segments reached when the iteration ends corresponding to the labels available for the Markov Random Field minimization.