Fahed Abdallah

TL;DR: This survey focuses on high level prior, embedded at the loss function level, and categorizes the articles according to the nature of the prior: the object shape, size, topology, and the inter-regions constraints.

TL;DR: This paper introduces BB-UNet (Bounding Box U-Net), a deep learning model that integrates location as well as shape prior onto model training and outperforms state-of-the-art methods in fully supervised learning frameworks and registers relevant results given the weakly supervised domain.

TL;DR: A constrained version of a recent method called Multiview Spectral Clustering via integrating Nonnegative Embedding and Spectral Embedded is presented, which retains the advantages of this method but integrates two types of constraints: a consistent smoothness of the nonnegative embedding over all views and an orthogonality constraint over the columns of thenon negative embedding.

TL;DR: The proposed multi‐label cellular automata model showed promising performance in terms of its ability to capture and model the details and complexities of changes in land‐use patterns, and outperformed a model combining CA and artificial neural networks.

TL;DR: Person re-identification is one of the indispensable elements for visual surveillance and assigns consistent labeling for the same person within the field of view of the same camera or even across ...