Abstract: There is provided a method for annotating digital images for training a machine learning model, comprising: generating, from digital images and a plurality of dense depth maps, each associated with one of the digital images, an aligned three-dimensional stacked scene representation of a scene, where the digital images are captured by sensor(s) at the scene, and where each point in the three-dimensional stacked scene is associated with a stability score indicative of a likelihood the point is associated with a static object of the scene, removing from the three-dimensional stacked scene unstable points to produce a static three-dimensional stacked scene, detecting in at least one of the digital images static object(s) according to the static three-dimensional stacked scene, and classifying and annotating the static object(s). The machine learning model may be trained on the images annotated with a ground truth of the static object(s).

Abstract: A method for an unsupervised analysis of data according to a reordered distance matrix. According to preferred embodiments thereof, the present invention is useful for large scale multidimensional data, more preferably data having at least four dimensions. The present invention is also preferably used for data comprising a plurality of objects characterized by continuous variables, for example variables having a continuum of possible values rather than a plurality of discrete values.