Transparency of AI-XR Systems:Insights from Experts

Recent advancements in the field of Extended Reality (XR) have increasingly integrated AI (Artificial Intelligence) technologies to enhance user experience and interactions. These are now crucial in XR applications in tasks like real-time object recognition, responsive environment generation, and adaptive intelligent behavior creation. Nevertheless, AI-XR systems need to adhere to social, legal, and ethical standards, norms, and values to ensure that their development, deployment, and use is done in a responsible and trustworthy manner. This assures building trust, preventing misusing sensitive information, and safeguarding user privacy and autonomy. To this end, transparency of AI-XR systems is fundamental for building and assuring trust. Accordingly, this research aims to understand and reflect on the perspectives that experts have in relation to building transparent XI-XR systems. To do so, a workshop with 14 field experts is conducted and the findings serve as a design framework with a rich palette of technical, socio-technical, and human-centered instruments that could be of use to researchers and practitioners in this domain.</p

Determining Car Driver Interaction Intent through Analysis of Behavior Patterns

Part 5: Human InteractionInternational audienceThe aim of the article is to present preliminary results obtained by analysis of the behavior patterns of various driver subjects, in the context of an intelligent assistive driving system. We determined the parameters which are involved in determining the car driver’s interaction intent, and extracted features of interest from various measured parameters of the driver, car, and the environment. We discuss how threshold values can be obtained for the extracted features that can be part of rules to decide on specific interaction intents. The results obtained in this paper will be incorporated in a knowledge base to define the rules of an rule-based expert system that will predict in real-time the driver’s interaction intent, in order to enhance the safe driving experience

Visual Image Information Mining for ESA Sentinel‐2

This paper describes a human-centered interactive technique that discovers the optimum combination of three spectral bands optimizing visualization of learned classes and objects in large satellite scenes. The method implements the minimum-redundancy-maximum-relevance mRMR information-based feature selector. The algorithm automatically ranks the ESA Sentinel-2 spectral bands according to the amount of information contained about a learned class or object. The top three features with maximum information are automatically fed to the R-G-B channels of the display. The tool presents the capability to optimize in real time maybe the most important problem in the computer-assisted work of the human operator: visualization of areas of interest. The evaluation of results is performed in terms of both quality (expert-driven visual analysis) and quantity (color metrics) and concludes that this approach can become an important tool in support of image information mining operations. Results of experiments performed on ESA Sentinel-2 will be presented

An Inverse Problem Approach for the Segmentation of the Snow Cover in Satellite Images

A new model for the correction of topographic effects in satellite images of rough terrain is described. The model simulates a synthetic image of the scene using a computer graphics approach which combines ray-tracing techniques with radiosity methods. Computation is structured on three levels: a macro level in which the image is described by the Digital Elevation Model and the light source, a meso-scale in which the model simulates the integration effect of the imaging sensor and a micro-scale which is characterized by the reflectance of the snow cover (specular and diffuse). The parameters of the model are tuned with a gradient search to fit real images acquired by the Landsat-TM sensor. The results show a better accuracy than the classical &quot;cosine of incidence&quot; and Minnaert models. Additionally a new technique based on maximum entropy estimation is used to determine the reflectance function of snow and compare it with the one predicted by our model

Automatic feature extraction applied to EUSC data

A data set distributed by EUSC has undergone feature extraction

Interactive Spectral Band Discovery for Exploratory Visual Analysis of Satellite Images

This paper advocates an automatic technique to discover the optimum combination of three spectral features of a multispectral satellite image that enhance visualization of learned targets/objects. The method is an application-free, single-click user effort, spectrally and spatially balanced, fast-response, low-cost, information-based feature selector that comes to optimize maybe the most important problem in the computer-assisted work of the human operator: visualization of target areas

Knowledge acquisition and semantic rules discovery for CORINE LAND COVER mapping Latent Dirichlet Allocation

Latent Dirichlet Allocation offers the chance to discover semantic rules for land cover mapping