Using Colour to Model Outliers

Computer vision applications are able to model and reconstruct three dimensional scenes from several pictures. In this work, we are interested in the group of algorithm that register each image with respect to the model and aim at constructing a model of the scene. At the lowest level, most of these algorithms are comparing the pixel values of the image to the ones predicted by the model to refine the result. As research advances, the models are getting better and better, but no matter how complex they are, there will always be unpredictable situations that cannot be handled by the model. A recurring example is when an object appears in one image of the set, but in none of the others. The situation occurs, for example, when a moving entity crosses rapidly the field of view of the camera. In this work, we study the error generated by such an unexpected object at a pixel level and how colour can improve the estimation. We will derive the expected error distribution that this hypotheti...

• Type: Conference
• Year: 2003
• Where: IMAGING
• Authors: David Hasler, Sabine Süsstrunk
• Keywords: Image Processing, Images, IMAGING 2003, Model, Outlier