Although there are several techniques that video encoders may use to improve error resilience, it is largely recognized that intra coding refreshment plays a major role. This technique is especially useful for video encoders that rely on predictive (inter) coding to remove temporal redundancy because, in these conditions, the decoded quality can decay very rapidly due to error propagation if er...

We address the problem of determining the optimal model complexity for shape modeling This complexity is a compromise between model speci city and generality We show that the error of a model can be split into two components the model error and the tting error of which the rst one can be used to optimize the model complexity based on the speci c application This strategy improves over tradition...

Several shape error concealment techniques for objectbased video have already been proposed in the literature. These techniques, however, are either spatial or temporal, in the sense that only information from one of these domains is considered during the concealment process. While spatial error concealment techniques only use spatially adjacent shape information to perform the concealment, tem...

We address the problem of determining the optimal model complexity for shape modeling. This complexity is a compromise between model speciicity and generality. We show that the error of a model can be split into two components, the model error and the tting error, of which the rst one can be used to optimize the model complexity based on the speciic application. This strategy improves over trad...

In this paper we introduce a novel certified shape optimization strategy named Certified Descent Algorithm (CDA) to account for the numerical error introduced by the Finite Element approximation of the shape gradient. We present a goal-oriented procedure to derive a certified upper bound of the error in the shape gradient and we construct a fully-computable, constant-free a posteriori error est...

The Active Shape Model(ASM) is an iterative algorithm for image interpretation based upon a Point Distribution Model. Each iteration of the ASM has two steps: Image data interrogation followed by shape approximation. Here we consider the shape approximation step in detail. We present a new method of shape approximation which uses directional constraints. We show how the error term for the shape...