Abstract
Applications and products currently available for the broadcasting market are vertically integrated or proprietary They are based on components requiring specific and costly development to interoperate and typically rely on a single manufacturer or system integrator Hence, they are not fully compliant with broadcasters' requirements. Architectural Solutions for Services Enhancing digital Television (ASSET) is a European-funded project. Its main goal is to overcome the limitations of custom-specific implementations in a digital system for TV content creation. These limitations are generally due to the misfit of interfaces between software layers, proprietary application program interfaces (APIs) of equipment from different vendors, and the lack of a generalized middleware for multimedia content management with openly defined interfaces. Besides presenting the ASSET-proposed architecture and concepts, this paper describes the prototype under development to test and demonstrate the project proposals.

Abstract
The challenge of managing large scale media assets has led to the development of metadata schemas that are expected to enable efficient search and retrieval of multimedia content. Those approaches propose schemas that can range from simple keyword based descriptions to complex hierarchical organization of information. However, effective media asset management requires more than content searching and retrieval: underlying infrastructures are usually complex, require the use of a number of different equipment and management decisions have to be done based on information available from the multimedia metadata layer as well as on data describing system resources and capabilities. In this paper we propose a new ontology that aggregates information from different sources and enables a top level business oriented view of multimedia archives.

Abstract
The evolution of Television towards de digital domain is opening new opportunities but also new challenges both to users and system managers. Audiovisual television archives will be an essential component of the whole digital television operators systems, as archived information needs to be available to a wide range of users. This paper presents the work developed at INESC Porto within the VIDION project and the experiments on merging television, computer and telecommunications concepts and technologies by the use of software agents and CORBA to assist in solving problems of information and system configuration and management in a TV archive. Aspects such as definition of the problem, architecture proposed and current state of the work will be the focus of the paper.

Abstract
The widespread use of smartphones and other low-cost equipment as recording devices, the massive growth in bandwidth, and the ever-growing demand for new applications with enhanced capabilities, made visual data a must in several scenarios, including surveillance, sports, retail, entertainment, and intelligent vehicles. Despite significant advances in analyzing and extracting data from images and video, there is a lack of solutions able to analyze and semantically describe the information in the visual scene so that it can be efficiently used and repurposed. Scientific contributions have focused on individual aspects or addressing specific problems and application areas, and no cross-domain solution is available to implement a complete system that enables information passing between cross-cutting algorithms. This paper analyses the problem from an end-to-end perspective, i.e., from the visual scene analysis to the representation of information in a virtual environment, including how the extracted data can be described and stored. A simple processing pipeline is introduced to set up a structure for discussing challenges and opportunities in different steps of the entire process, allowing to identify current gaps in the literature. The work reviews various technologies specifically from the perspective of their applicability to an end-to-end pipeline for scene analysis and synthesis, along with an extensive analysis of datasets for relevant tasks.

Abstract
When compared with traditional local shops where the customer has a personalised service, in large retail departments, the client has to make his purchase decisions independently, mostly supported by the information available in the package. Additionally, people are becoming more aware of the importance of the food ingredients and demanding about the type of products they buy and the information provided in the package, despite it often being hard to interpret. Big shops such as supermarkets have also introduced important challenges for the retailer due to the large number of different products in the store, heterogeneous affluence and the daily needs of item repositioning. In this scenario, the automatic detection and recognition of products on the shelves or off the shelves has gained increased interest as the application of these technologies may improve the shopping experience through self-assisted shopping apps and autonomous shopping, or even benefit stock management with real-time inventory, automatic shelf monitoring and product tracking. These solutions can also have an important impact on customers with visual impairments. Despite recent developments in computer vision, automatic grocery product recognition is still very challenging, with most works focusing on the detection or recognition of a small number of products, often under controlled conditions. This paper discusses the challenges related to this problem and presents a review of proposed methods for retail product label processing, with a special focus on assisted analysis for customer support, including for the visually impaired. Moreover, it details the public datasets used in this topic and identifies their limitations, and discusses future research directions of related fields.