One of the most used statistical approaches in the
literature for computing texture information is the Gray Level Cooccurrence Matrix (GLCM). The use of GLCM and textures descriptors calculated from there, allows, among other applications, the detection of the main direction of the elements of texture. This is possible essentially because the GLCM uses different angles to compute the relationship between the gray
tones in the image. An alternative to computing this relationship is the Sum of Difference Histograms (SDH). In contrast to GLCM, which is a bidimensional structure, SDHs are onedimensional. Once there is a relationship between the data stored in both structures, the behavior of the descriptors is similar, and the SDH have a lower computational cost being, for this, more efficient in a number of applications. In this sense, this work leads to a series of investigations regarding the correspondence between
these two approaches. Moreover, it investigates which texture descriptors, calculated from the SDH, can collaborate in the process of identifying the predominant direction of a texture.

Determining the Direction of Images Using Sum and Difference Histograms and Texture Descriptors

Smart parking systems can manage real-time city resources, and geo-locally provides them to drivers. It involves on– and off–street parking management; that is why smart parking has been considered promising inception and a leading paradigm of a smart city. The design of a smart city ecosystem, especially on urban mobility, has a tremendous impact on resource status and city capacities. Although its high capacity to work for quality of experience and Business Intelligence, works about smart parking in the literature do not take advantage of this in its proposes. This work proposes the BEM framework that intends to take advantage of the car park Management and improve Quality Experience and Business Intelligence being an essential vector for the advancement of smart cities. A proof of concept was implemented, proving the solution viability.

Adriel Araujo

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SHORT BIO

Presentations

Determining the Direction of Images Using Sum and Difference Histograms and Texture Descriptors

BEM: A Framework based on Business Intelligence, Quality of Experience, and Car Park Management

Image Processing II

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