Digital storytelling platforms have proven to be a great way of bringing cultural heritage closer to people. What lacks is a deeper understanding of the user experience of such systems, especially in multi-platform digital storytelling. For the last three years, we have been developing a project called Spotlight Heritage Timisoara, which is at its core a digital storytelling platform for the city of Timisoara (Romania), soon to be European Capital of Culture in 2023. The project consists of a website, mobile applications, and interactive museographic and street exhibitions. This paper presents a multi-platform usability evaluation study which employed semi-structured interviews, observations, think-aloud protocol, SUS questionnaire, Net Promoter Score and Product Reaction Cards to gather insights from 105 participants and reveal usability problems in the Spotlight Heritage context. We found out that the four platforms, i.e., interactive touchscreen table, desktop/laptop, mobile and Augmented Reality, have very good usability scores, are considered accessible and useful, work seamlessly together, and create user satisfaction and loyalty, across demographic groups, having the potential to bring people closer to cultural heritage.

Computer Vision is a cross-research field with the main purpose of understanding the surrounding environment as closely as possible to human perception. The image processing systems is continuously growing and expanding into more complex systems, usually tailored to the certain needs or applications it may serve. To better serve this purpose, research on the architecture and design of such systems is also important. We present the End-to-End Computer Vision Framework, an open-source solution that aims to support researchers and teachers within the image processing vast field. The framework has incorporated Computer Vision features and Machine Learning models that researchers can use. In the continuous need to add new Computer Vision algorithms for a day-to-day research activity, our proposed framework has an advantage given by the configurable and scalar architecture. Even if the main focus of the framework is on the Computer Vision processing pipeline, the framework offers solutions to incorporate even more complex activities, such as training Machine Learning models. EECVF aims to become a useful tool for learning activities in the Computer Vision field, as it allows the learner and the teacher to handle only the topics at hand, and not the interconnection necessary for visual processing flow.

Computer Vision is a cross research field with the main purpose of understanding the surrounding environment as closely as possible to human perception. In addition to research on techniques and algorithms that enhance the capabilities of a Computer Vision system, research on the architecture and design of such systems is also important. In this paper we present an End-to-End Computer Vision Framework open-source solution, based on Python programming language, that aims to support researchers in this field. The main focus of the framework is the configurability and scalability of the system in the continuous need to add new Computer Vision algorithms or machine learning models for a day-to-day research activity.

For indexing an image databases there are used many features. In this paper are used features like histograms for the color components, stochastic moments, few parameters of the co-occurrences matrix and the Gabor filters decomposition. Using all of them we obtain good performance in the field of content-based image indexing and retrieval. There are some speed problems because are many features used in the search process. Here, I present an algorithm for indexing and retrieval, which use a supervised tree algorithm for speed increasing. The area of applications is very wide: multimedia documents, transaction systems, medical application.

This paper discusses the current developments in interactive video and its implications for online learning, and particularly to the new educational concept of MOOC - Massive Open Online Courses. The authors present an interactive video platform that can be easily used for generation of interactive videos. Interactivity is added to videos by temporal and spatial interactive multimedia annotations using media fragments, as well as decisions at the end of a video, via a special interface. The same interactive video platform allows the dynamic generation of a graph-type multiple path linear story, with information dependent on previous decisions and choices, similar to an educational experience where later courses depend on prerequisite information. Information depth is ensured via various types of annotations. The authors consider two main types of content annotations - basic annotations explaining fundamental concepts, and advanced annotations providing in-depth specialized information about a given topic. The dynamic of user interaction with these two types of annotations is used to determine the users level of knowledge about the course topic. All these aspects are then put in perspective against a MOOC environment, showing how interactive video can be used to enhance the learning process and to enhance the user experience. We also discuss the cost-effective means of adding value and interactivity to the video content, whether generated purposely to be interactive, or simply adding interaction to existing video content to enhance it. As a conclusion, the article proposes a theoretical and practical approach supported by existing examples, and draws future directions and guidelines for developing educational projects and applications using interactive video in a MOOC context.