A robust method of camera response function estimation applicable to auto-exposure cameras is presented. The method uses the superposition property of light to solve for the response function directly using superposition constraints imposed by using different combinations of two (or more) lights that illuminate the same subject matter in varying proportions. An iterative optimization is utilised to simultaneously recover the exposure ratios of the images and the camera response function. Previously published multiple exposure methods that simultaneously estimate exposure ratio and response function suffer from a fundamental ambiguity. The use of the proposed superposition constraints solves this problem. We introduce a simple method for combining both superposition and homogeneity (from multiple exposure techniques) to accurately recover the response function from auto-exposure cameras.

A robust method of camera response function estimation, using pictures taken by differently illuminating the same subject matter, is presented. The method solves for the response function directly using superposition constraints imposed by different combinations of two (or more) lights to illuminate the same subject matter. Previous methods of computing camera response functions typically used differing exposures of identical subject matter, leading to uniqueness problems (underconstrained due to comparametric periodicity or fractal ambiguity). The method used in this paper overcomes this problem. Finally, we compare the method of the paper to previous methods and find the new method outperforms the previous work.

We present a system which allows wearable computer users to share their views of their current environments with each other. Our system uses an EyeTap: a device which allows the eye of the wearer to function both as a camera and a display. A wearer, by looking around his/her environment, "paints" or "builds" an environment map composed of images from the EyeTap device, along with head-tracking information recording the orientation of each image. The head-tracking algorithm uses a featureless image motion estimation algorithm coupled with a head mounted gyroscope. The environment map is then transmitted to another user, who, through their own head-tracking EyeTap system, browses the first user's environment solely by head motion, seeing the environment as though it were their own. As a result of browsing the transmitted environment map, the viewer builds and extends his/her own environment map, and thus this is a data-producing head-tracking system. These environment maps can then be shared reciprocally between wearers.