Abstract: A method and system for detecting and classifying defects in a paved surface is disclosed. A sequence of images of the paved surface is obtained from at least one imaging device that can be mounted on a vehicle. The images are used to form a three-dimensional reconstruction. A machine learning process is used to train the system to recognize different kinds of defects and defect-free surfaces. Performing a pixel-by-pixel comparison of the images obtained for a particular paved surface with a database of images of surfaces with known defects provides a determination of the locations of defects in that paved surface. The system and method disclosed herein do not require the use of artificial lighting and are unaffected by transient changes in ambient light.

Abstract: A method and apparatus for calculating exact positioning using a digital camera and a GPS, comprising calibrating the camera; initiating GPS navigation; capturing and storing images and GPS coordinates; calculating ego-motion of the camera using a pre-defined number of stored images; and calculating current position of the camera using the last stored GPS coordinates and the calculated camera ego-motion.

Abstract: A method for generating a composite, three-dimensional-like image sequences, by the assembly of two or more separate image sequences is provided. The method comprises the steps of providing a main sequence of images; providing at least one secondary sequence of images, the contents of which are at least roughly related to the contents of the main sequence; selecting the positioned relationship of each of the secondary of images and main sequence of images; processing each of the secondary sequence of images so as to optimize its matching with the main sequence in the selected positioned relationship with it and displaying the main and secondary sequences of images at the same time and in substantially the same selected and positioned relationship.