A systemic approach to automatic metadata extraction from multimedia content

There is a need for automatic processing and extracting of meaningful metadata from multimedia information, especially in the audiovisual industry. This higher level information is used in a variety of practices, such as enriching multimedia content with external links, clickable objects and useful related information in general. This paper presents a system for efficient multimedia content analysis and automatic annotation within a multimedia processing and publishing framework. This system is comprised of three modules: the first provides detection of faces and recognition of known persons; the second provides generic object detection, based on a deep convolutional neural network topology; the third provides automated location estimation and landmark recognition based on state-of-the-art technologies. The results are exported in meaningful metadata that can be utilized in various ways. The system has been successfully tested in the framework of the EC Horizon 2020 Mecanex project, targeting advertising and production markets

a-shapes for local feature detection

Local image features are routinely used in state-of-the-art methods to solve many computer vision problems like image retrieval, classification, or 3D registration. As the applications become more complex, the research for better visual features is still active. In this paper we present a feature detector that exploits the inherent geometry of sampled image edges using α-shapes. We propose a novel edge sampling scheme that exploits local shape and investigate different triangulations of sampled points. We also introduce a novel approach to represent the anisotropy in a triangulation along with different feature selection methods. Our detector provides a small number of distinctive features that is ideal for large scale applications, while achieving competitive performance in a series of matching and retrieval experiments

Detailed design and implementation of the kernel of a real-time distributed multiprocessor operating system

This thesis presents the detailed design and implementation of the kernel of a real-time, distributed operating system for a microcomputer based multiprocessor system. Process oriented structure, segmented address spaces and a synchronization mechanism based on eventcounts and sequencers comprise the central concepts around which this operating system is built. the operating system is hierarchically structured, layered in three loop levels of abstraction and fundamentally configuration independent. This design permits the logical distribution of the kernel functions in the address space of each process and the physical distribution of system code and data among the microcomputers. This physical distribution in turn, in a multimicroprocessor configuration will help to minimize system bus contention. The system particularly supports applications where processing is partitioned into a set of multiple interacting asynchronous processes. One such application is that of smart sensor image processing for which this system has been specifically developed. The implementation was developed for the INTEL 86/12A single-board computer using the 8086 processor chip.http://archive.org/details/detaileddesignim00rapaLieutenant, Hellenic NavyApproved for public release; distribution is unlimited