Hamiltonian path based shadow removal

Clement Fredembach, Graham Finlayson

Research output: Contribution to conferencePaper


For some computer vision tasks, the presence of shadows in images can cause problems. For example, object tracks can be lost as an object crosses over a shadow boundary. Recently, it has been shown that it is possible to remove shadows from images. Assuming that the location of the shadows are known, shadow-free images are obtained in three steps. First, the image is differentiated. Second, the derivatives at the shadow edge are set to zero. Third, reintegration delivers an image without shadows. While this process can work well, the resultant shadow free image often has artifacts and, moreover, the reintegration is an expensive computational procedure. In this paper we propose a method which can produce shadow free images quickly and without artifacts. Our algorithm is based on two observations. First, that shadows in images are closed regions and if they are not closed artifacts can result during reintegration. Thus we propose to extend the existing methods and enforce the constraint that shadow boundaries must be closed prior to reintegration. Second, that the standard reintegration method used (solving a 2D Poisson equation) also, necessarily, introduces artifacts. The solution here is to reintegrate shadow and non shadow regions almost separately. Specifically, we reintegrate the image along a Hamiltonian path that enters and exists the shadow regions once. Detail that was masked out at the shadow boundary is then infilled in a second step. The resulting reintegrated image has much fewer artifacts. Moreover, since the reintegration method is path based it is both simple and fast. Experiments validate our approach.
Original languageEnglish
Publication statusPublished - Sep 2005
Event16th British Machine Vision Conference - Oxford Brookes University, Oxford, United Kingdom
Duration: 5 Sep 20058 Sep 2005


Conference16th British Machine Vision Conference
Country/TerritoryUnited Kingdom

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