The ColorChecker dataset is one of the most widely used image sets for evaluating and ranking illuminant estimation algorithms. However, this single set of images has at least 3 different sets of ground-truth (i.e., correct answers) associated with it. In the literature it is often asserted that one algorithm is better than another when the algorithms in question have been tuned and tested with the different ground-truths. In this short correspondence we present some of the background as to why the 3 existing ground-truths are different and go on to make a new single and recommended set of correct answers. Experiments reinforce the importance of this work in that we show that the total ordering of a set of algorithms may be reversed depending on whether we use the new or legacy ground-truth data.

This demonstration presents the current state of an on-going team project at Simon Fraser University in developing a virtual environment for helping to train surgeons in performing laparoscopic surgery. In collaboration with surgeons, an initial set of training procedures has been developed. Our goal has been to develop procedures in each of several general categories, such as basic hand-eye coordination, single-handed and bi-manual approaches and dexterous manipulation. The environment is based on an effective data structure that offers fast graphics and physically based modeling of both rigid and deformable objects. In addition, the environment supports both 3D and 5D input devices and devices generating haptic feedback. The demonstration allows users to interact with a scene using a haptic device.