Propagation, structural similarity and image quality

Póster presentado en SPIE Photonics Europe, Brussels, 16-19 April 2012.

Open business intelligence: on the importance of data quality awareness in user-friendly data mining

Citizens demand more and more data for making decisions in their daily life. Therefore, mechanisms that allow citizens to understand and analyze linked open data (LOD) in a user-friendly manner are highly required. To this aim, the concept of Open Business Intelligence (OpenBI) is introduced in this position paper. OpenBI facilitates non-expert users to (i) analyze and visualize LOD, thus generating actionable information by means of reporting, OLAP analysis, dashboards or data mining; and to (ii) share the new acquired information as LOD to be reused by anyone. One of the most challenging issues of OpenBI is related to data mining, since non-experts (as citizens) need guidance during preprocessing and application of mining algorithms due to the complexity of the mining process and the low quality of the data sources. This is even worst when dealing with LOD, not only because of the different kind of links among data, but also because of its high dimensionality. As a consequence, in this position paper we advocate that data mining for OpenBI requires data quality-aware mechanisms for guiding non-expert users in obtaining and sharing the most reliable knowledge from the available LOD.

Retinal image quality assessment through a visual similarity index

Retinal image quality is commonly analyzed through parameters inherited from instrumental optics. These parameters are defined for ‘good optics’ so they are hard to translate into visual quality metrics. Instead of using point or artificial functions, we propose a quality index that takes into account properties of natural images. These images usually show strong local correlations that help to interpret the image. Our aim is to derive an objective index that quantifies the quality of vision by taking into account the local structure of the scene, instead of focusing on a particular aberration. As we show, this index highly correlates with visual acuity and allows inter-comparison of natural images around the retina. The usefulness of the index is proven through the analysis of real eyes before and after undergoing corneal surgery, which usually are hard to analyze with standard metrics.