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Motion estimation is usually based on the brightness constancy assumption. This assumption holds well for rigid objects with a Lambertian surface, but it is less appropriate for fluid and gaseous materials. For these materials an alternative assumption is required. This work examines three possible alternatives: gradient constancy, color constancy and brightness conservation (under this assumption the brightness of an object can diffuse to its neighborhood). Brightness conservation and color constancy are found to be adequate models. We propose a method for detecting regions of dynamic texture in image sequences. Accurate segmentation into regions of static and dynamic texture is achieved using a level set scheme. The level set function separates each image into regions that obey brightness constancy and regions that obey the alternative assumption. We show that the method can be simplified to obtain a less robust but fast algorithm, capable of real-time performance. Experimental results demonstrate accurate segmentation by the full level set scheme, as well as by the simplified method. The experiments included challenging image sequences, in which color or geometry cues by themselves would be insufficient.

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Abstract. Motion estimation is usually based on the brightness constancy assumption. This assumption holds well for rigid objects with a Lambertian surface, but it is less appropriate for fluid and gaseous materials. For these materials a variant of this assumption, which we call the brightness conservation assumption should be employed. Under this assumption an object's brightness can diffuse to its neighborhood. We propose a method for detecting regions of dynamic texture in image sequences. Segmentation into regions of static and dynamic texture is achieved by using a level set scheme. The level set function separates the images into areas obeying brightness constancy and those which obey brightness conservation. Experimental results on challenging image sequences demonstrate the success of the segmentation scheme and validate the model.