A method for obtaining object surface topology in which image frames of a scene (e.g., video frames from a user passing a smartphone camera over an object) are transformed into dense feature vectors, and feature vectors are correlated to obtain high precision depth maps. Six dimensional pose is determined from the video sequence, and then used to register patches of pixels from the frames. Registered patches are aligned and then correlated to local shifts. These local shifts are converted to precision depth maps, which are used to characterize surface detail of an object. Feature vector transforms are leveraged in a signal processing method comprising several levels of interacting loops. At a first loop level, a structure from motion loop process extracts anchor features from image frames. At another level, an interacting loop process extracts surface texture, as noted. At additional levels, object forms are segmented from the images, and objects are counted and/or measured. At still a higher level, the lower level data structures providing feature extraction, 3D structure and pose estimation, and object surface registration are exploited by higher level loop processes for object identification (e.g., using machine learning classification), digital watermark or bar code reading and image recognition from the registered surfaces stored in lower level data structures.
Signal detection, recognition and tracking with feature vector transforms