infonet-economy

We present texture operators encoding class-specific local organizations of image directions (LOIDs) in a rotation-invariant fashion. The LOIDs are key for visual understanding, and are at the origin of the success of the popular approaches, such as local binary patterns (LBPs) and the scale-invariant feature transform (SIFT). Whereas, LBPs and SIFT yield hand-crafted image...

The increasing interdependence of firms and individuals throughout the world facilitates the development of the crowdlending market. Crowdlending is an emerging source of financing involving open calls to the public, generally via internet, to finance with loans individuals or companies (Meyer, 2007). The major role of crowdlending activities has been to bring new energy to a global...

This chapter aims to provide an overview of the foundations of texture processing for biomedical image analysis. Its purpose is to define precisely what biomedical texture is, how is it different from general texture information considered in computer vision, and what is the general problem formulation to translate 2Dand 3Dtextured patterns from biomedical images to visually and...

Research in the health-care domain requires the collection of important and exhaustive datasets, in order to validate a scientific hypothesis, or to assess the effectiveness of a treatment, technology, medicine, or procedure. The data acquisition phase for this type of work requires an often under-estimated amount of time and effort, while needing to keep high quality standards for...

This chapter clarifies the important aspects of biomedical texture analysis under the general framework introduced in Chapter 1. It was proposed that any approach can be character-ized as the combination of local texture operators and regional aggregation functions. The type of scale and directional information that can or cannot be modeled by categories of texture processing methods...

Content–based multimedia retrieval has been an active research domain since the mid 1990s. In the medical domain visual retrieval started later and has mostly remained a research instrument and less a clinical tool, even though a few tools for retrieval are employed in clinical work. The limited size of data sets due to privacy constraints is often mentioned as a reason for these...

Electronic Data Capture (EDC) software solutions are progressively being adopted for conducting clinical trials and studies, carried out by biomedi-cal, pharmaceutical and health-care research teams. In this paper we present the MedRed Ontology, whose goal is to represent the metadata of these studies, using well-established standards, and reusing related vocabularies to describe...

Training deep convolutional neural network for classification in medical tasks is often difficult due to the lack of annotated data sam-ples. Deep convolutional networks (CNN) has been successfully used as an automatic detection tool to support the grading of diabetic retinopa-thy and macular edema. Nevertheless, the manual annotation of exu-dates in eye fundus images used to classify...