Visual Manifestations of Primary Metaphors Through Mise-en-scène Techniques

Grady distinguishes two main types of metaphor in order to provide a solution in the controversies stemming from the conceptual theory of metaphor: correlation-based metaphors and resemblance metaphors. In “correlation-based metaphors”, the source domain is sensory-motor, while the target domain is not. On the contrary, “resemblance metaphors” are originated by a physical or conceptual perception which is common in both domains, by the association of concepts with common features. Primary metaphors are the minimal units of correlation-based metaphors; they are inherent in human nature and the result of the nature of our brain, our body and the world that we inhabit. We acquire them automatically and we cannot avoid them. Furthermore, as corporal experiences are universal, so are primary metaphors. In this paper, I will argue that primary metaphors manifest themselves visually through scene-setting techniques such as composition, framing, camera movement or lighting. Film-makers can use the different aspects of mise-en-scène metaphorically in order to express abstract notions like evil, importance, control, relationship or confusion. Such visual manifestations, as also occurs with their verbal equivalents, frequently go unnoticed or have been used so often that they have become clichés. But the important thing to bear in mind is that their origin lies in a primary metaphor and due to this origin these kinds of film-making strategies have been so expressively successful.

Parallel Computational Intelligence-Based Multi-Camera Surveillance System

In this work, we present a multi-camera surveillance system based on the use of self-organizing neural networks to represent events on video. The system processes several tasks in parallel using GPUs (graphic processor units). It addresses multiple vision tasks at various levels, such as segmentation, representation or characterization, analysis and monitoring of the movement. These features allow the construction of a robust representation of the environment and interpret the behavior of mobile agents in the scene. It is also necessary to integrate the vision module into a global system that operates in a complex environment by receiving images from multiple acquisition devices at video frequency. Offering relevant information to higher level systems, monitoring and making decisions in real time, it must accomplish a set of requirements, such as: time constraints, high availability, robustness, high processing speed and re-configurability. We have built a system able to represent and analyze the motion in video acquired by a multi-camera network and to process multi-source data in parallel on a multi-GPU architecture.