By Prof. Peter L. Stanchev, Kettering University, Flint, USA & Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria
There are many factors that contribute to the development of high-level concepts such as smart devices, smart cars, smart homes and even smart cities. In one hand it is inevitable that the advancements of handheld devices create the basis for realization of many smart technologies. This is due to the increase of computational power and minimization of electronics worldwide. On the other hand, the exponential increase of data according to different sources leads to a growing necessity for the development of adequate technologies that can clean, structure and analyze more and more data every day. This in turn leads to the creation of new smarter technologies, which have a profound impact on our everyday life.
In this talk, I will focus on the advancements of smart technologies that form the smart city ecosystem. More specifically I will put an emphasis on the automation of various processes based on the extraction and analysis of digital media, through voice, video and images.
The following applications using Smart Services through voice, video and images will be discussed:
a) Smart Services through emotional analysis.
b) Computer vision for image analysis in an autonomous car. The process of recognition and classifying objects of different road types.
c) Image retrieval using high-level semantic features based on extraction of low-level color, shape and texture characteristics and their conversion into high-level semantic features using fuzzy production rules, derived with the help of an image mining technique.
d) Image recognitions through searching a product through make a picture of an object, identifying the object, object discovering by image recognition, searching the network for objects, objects found in the network are display.
e) An efficient video stream filtering solution. The solution can be adopted for TV-Anytime, On Demand TV, Integrated Digital Television, Set-Top-Boxes, etc. uses a novel approach – Pivoted Stream. The solution exploits the properties of metric spaces, in order to reduce the computational load of the filtering receiver.
f) Machine learning, with regard to deep learning, helping to identify, classify, and quantify patterns in medical images.
Prof. Peter Stanchev is a professor at Kettering University, Flint, Michigan, USA. He also works at the Software Engineering and Information Systems Department at the Institute of Mathematics and Informatics, Bulgarian Academy of Sciences. He has forty years of professional experience in of multimedia systems, database systems, multimedia semantics, education, open access to scientific information and data and medical systems. He has M.Sc., Ph.D. and D.Sc. in Mathematics/Computer Science from Sofia University. He has published 2 books, more than 200 chapters in monographs, journal and conference peer-reviewed papers, more than 200 conference papers and seminars, and has had more than 2000 citations, h-index – 23, i10-index 38, impact factor – 77.03. Serving also on many database and multimedia conference program committees, he is currently editor-in-chief and member of the editorial boards of several journals. He is the Bulgarian representative in the EU OpenAIRE projects.