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In this research the problem of the automatic detection and classification of rectangular road sign has been faced. The first step concerns the robust identification of the rectangular sign, through the search of gray level discontinuity on the image and Hough transform. Due to variety of rectangular road signs, we first recognize the guide sign and then we consider advertising the other rectangular signs. The classification is based on analysis of surface color and arrows direction of the sign. We have faced different problems, primarily: shape alterations of the sign owed to the perspective, shades, different light conditions, occlusion. The obtained results show the feasibility of the system.

In this research the problem of the detection and classification of road bridge sign has been faced, in particular, at butterfly bridge configuration. The first step concerns the robust identification of the rectangular sign, through the optical flow analysis. The second step concerns rectangular sign detection based on searching gray level discontinuity on the image and Hough transform. The classification is based on analysis of surface color on inner part, detection of shape and color of rectangular border around the sign, arrows direction analysis. Our approach can detect sign candidates in presence of complex background.

Individuals produce a large amount of freely available data by interacting, sharing, and consuming content through social media. Social Media Mining is a systematic analysis of information generated from social media. Therefore, it is possible to known media usage, online behaviors, sharing of content, connections between individuals, online buying behavior, etc. These patterns and trends are of interest to organizations, brand, businesses, marketers, sociologists etc. This chapter aims to introduces to the concepts of social media mining, processes and tools involved in mining and processing data from social media platforms, as well as the importance, privacy, challenges, and use cases.