In this article, we propose a novel spatiotemporal salient region detection framework based on global scheme.
Ren et al. presented a two-stage 3D salient object detection framework, which first integrates the contrast region with the background, depth and orientation priors to achieve a saliency map and then reconstructs the saliency map globally [20].
It is worthy to note that while the core of the NEL Framework is the child and learning through play is one of its salient elements, the framework, however, stresses the importance of teachers' role in intentionally planning and guiding children's play to meet learning objectives for achieving desired learning goals.
In this work, we propose the COGNIMUSE1 database and we provide a computational framework for salient event detection and attention-based summarization.
In our framework, the salient local patterns for different video categories are explored by MIL, and intrinsic temporal dependencies between instances are explicitly exploited using the powerful chain CRF model.
The modelling analysis relies on a Lagrangian framework, the salient features of which consist in approximating the Lagrangian path in a reduced composition space made up of the mixture fraction variable, i.e. a conserved scalar introduced to represent the variations of composition, and a progress variable, i.e. a reactive scalar to follow the departures from chemical equilibrium.
In this framework, the salient structures of the input images are fused in the gradient domain, then the final fused image is reconstructed by solving a Poisson equation which forces the gradients of the fused image to be close to the fused gradients.
We propose a powerful unified framework for spatiotemporal salient region detection, which can obtain higher quality salient region detection results than existing methods no matter whether the camera is fixed or not.
The main contributions of the article are as follows: (1) We propose a powerful unified framework for spatiotemporal salient region detection, which can obtain higher quality salient region detection results than existing methods no matter whether the camera is fixed or not.
This article presents a new bottom-up framework for spatiotemporal salient region detection.
