A novel vision system is proven at a conceptual level to help unmanned remotely operated vehicles (ROVs) interpret underwater oceanic scenes and clarify noisy image sequences. The images contain objects of interest (metal cylinders of a known oil rig structure) and background (water). After contrast stretching for enhancement, images are segmented using a quickly convergent method based on Markov random fields (MRFs). This iterated conditional mode MRF uses deterministic relaxation to rapidly converge. Cylinders are analysed to determine the camera's viewing direction, range and twist off the vertical. The camera's position is then calculated given knowledge of the node in view. Successive viewpoints from a sequence of images are fed through a Kalman filter to predict the next viewpoint. Placing this in a 3D computer model of the structure allows a 2D predicted image to be projected. This is combined with the next acquired image to improve object recognition by the MRF segmentation method. This novel predictive feedback method shows better resilience to noise, matching noisy images to the clearer model that would otherwise go unrecognised.
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