From conceptual models to schemata: An object-process-based data warehouse construction method

Data warehouse modeling is a complex task, which involves knowledge of business processes of the domain of discourse, understanding the structural and behavioral system's conceptual model, and familiarity with data warehouse technologies. The suitability of current data warehouse modeling methods for large-scale systems is questionable, as they require multiple manual actions to discover measures and relevant dimensional entities and they tend to disregard the system's dynamic aspects. We present an Object-process-based Data Warehouse Construction (ODWC) method that overcomes these limitations of existing methods by utilizing the operational system conceptual model to construct a corresponding data warehouse schema. We specify the ODWC method, apply it on a case study, evaluate it, and compare it to existing methods.     

Calibration of Cameras with Radially Symmetric Distortion

We present algorithms for plane-based calibration of general radially distorted cameras. By this, we understand cameras that have a distortion center and an optical axis such that the projection rays of pixels lying on a circle centered on the distortion center form a right viewing cone centered on the optical axis. The camera is said to have a single viewpoint (SVP) if all such viewing cones have the same apex (the optical center); otherwise, we speak of NSVP cases. This model encompasses the classical radial distortion model [5], fisheyes, and most central or noncentral catadioptric cameras. Calibration consists in the estimation of the distortion center, the opening angles of all viewing cones, and their optical centers. We present two approaches of computing a full calibration from dense correspondences of a single or multiple planes with known euclidean structure. The first one is based on a geometric constraint linking viewing cones and their intersections with the calibration plane (conic sections). The second approach is a homography-based method. Experiments using simulated and a broad variety of real cameras show great stability. Furthermore, we provide a comparison with Hartley-Kang's algorithm [12], which, however, cannot handle such a broad variety of camera configurations, showing similar performance.     

Multi-Perspective Modelling, Rendering and Imaging

A perspective image represents the spatial relationships of objects in a scene as they appear from a single viewpoint. In contrast, a multi‐perspective image combines what is seen from several viewpoints into a single image. Despite their incongruity of view, effective multi‐perspective images are able to preserve spatial coherence and can depict, within a single context, details of a scene that are simultaneously inaccessible from a single view, yet easily interpretable by a viewer. In computer vision, multi‐perspective images have been used for analysing structure revealed via motion and generating panoramic images with a wide field‐of‐view using mirrors. In this STAR, we provide a practical guide on topics in multi‐perspective modelling and rendering methods and multi‐perspective imaging systems. We start with a brief review of multi‐perspective image techniques frequently employed by artists such as the visual paradoxes of Escher, the Cubism of Picasso and Braque and multi‐perspective panoramas in cel animations. We then characterize existing multi‐perspective camera models, with an emphasis on their underlying geometry and image properties. We demonstrate how to use these camera models for creating specific multi‐perspective rendering effects. Furthermore, we show that many of these cameras satisfy the multi‐perspective stereo constraints and we demonstrate several multi‐perspective imaging systems for extracting 3D geometry for computer vision. The participants learn about topics in multi‐perspective modelling and rendering for generating compelling pictures for computer graphics and in multi‐perspective imaging for extracting 3D geometry for computer vision. We hope to provide enough fundamentals to satisfy the technical specialist without intimidating curious digital artists interested in multi‐perspective images. The intended audience includes digital artists, photographers and computer graphics and computer vision researchers using or building multi‐perspective cameras. They will learn about multi‐perspective modelling and rendering, along with many real world multi‐perspective imaging systems.     

Classification accuracy is not enough

We argue that an evaluation of system behavior at the level of the music is required to usefully address the fundamental problems of music genre recognition (MGR), and indeed other tasks of music information retrieval, such as autotagging. A recent review of works in MGR since 1995 shows that most (82 %) measure the capacity of a system to recognize genre by its classification accuracy. After reviewing evaluation in MGR, we show that neither classification accuracy, nor recall and precision, nor confusion tables, necessarily reflect the capacity of a system to recognize genre in musical signals. Hence, such figures of merit cannot be used to reliably rank, promote or discount the genre recognition performance of MGR systems if genre recognition (rather than identification by irrelevant confounding factors) is the objective. This motivates the development of a richer experimental toolbox for evaluating any system designed to intelligently extract information from music signals.     

Joint Estimation of Shape and Reflectance using Multiple Images with Known Illumination Conditions

We propose a generative model based method for recovering both the shape and the reflectance of the surface(s) of a scene from multiple images, assuming that illumination conditions and cameras calibration are known in advance. Based on a variational framework and via gradient descents, the algorithm minimizes simultaneously and consistently a global cost functional with respect to both shape and reflectance. The motivations for our approach are threefold. (1) Contrary to previous works which mainly consider specific individual scenarios, our method applies indiscriminately to a number of classical scenarios; in particular it works for classical stereovision, multiview photometric stereo and multiview shape from shading. It works with changing as well as static illumination. (2) Our approach naturally combines stereo, silhouette and shading cues in a single framework. (3) Moreover, unlike most previous methods dealing with only Lambertian surfaces, the proposed method considers general dichromatic surfaces. We verify the method using various synthetic and real data sets.     

Comprehensibility of UML-based software product line specifications

Software Product Line Engineering (SPLE) deals with developing artifacts that capture the common and variable aspects of software product families. Domain models are one kind of such artifacts. Being developed in early stages, domain models need to specify commonality and variability and guide the reuse of the artifacts in particular software products. Although different modeling methods have been proposed to manage and support these activities, the assessment of these methods is still in an inceptive stage. In this work, we examined the comprehensibility of domain models specified in ADOM, a UML-based SPLE method. In particular, we conducted a controlled experiment in which 116 undergraduate students were required to answer comprehension questions regarding a domain model that was equipped with explicit reuse guidance and/or variability specification. We found that explicit specification of reuse guidance within the domain model helped understand the model, whereas explicit specification of variability increased comprehensibility only to a limited extent. Explicit specification of both reuse guidance and variability often provided intermediate results, namely, results that were better than specification of variability without reuse guidance, but worse than specification of reuse guidance without variability. All these results were perceived in different UML diagram types, namely, use case, class, and sequence diagrams and for different commonality-, variability-, and reuse-related aspects.     

On Camera Calibration with Linear Programming and Loop Constraint Linearization

A technique for calibrating a network of perspective cameras based on their graph of trifocal tensors is presented. After estimating a set of reliable epipolar geometries, a parameterization of the graph of trifocal tensors is proposed in which each trifocal tensor is linearly encoded by a 4-vector. The strength of this parameterization is that the homographies relating two adjacent trifocal tensors, as well as the projection matrices depend linearly on the parameters. Two methods for estimating these parameters in a global way taking into account loops in the graph are developed. Both methods are based on sequential linear programming: the first relies on a locally linear approximation of the polynomials involved in the loop constraints whereas the second uses alternating minimization. Both methods have the advantage of being non-incremental and of uniformly distributing the error across all the cameras. Experiments carried out on several real data sets demonstrate the accuracy of the proposed approach and its efficiency in distributing errors over the whole set of cameras.     

From embedded sensors to sensorial materials—The road to function scale integration

Ubiquitous computing is about to become part of our everyday lives by integrating hundreds of “invisible” to us computing devices in our environment, so that they can unobtrusively and constantly assist us. This will imply more and smaller “invisible” sensors, homogeneously distributed and at the same time densely packed in host materials, responding to various stimuli and immediately delivering information. In order to reach this aim, the embedded sensors should be integrated within the host material, heading towards sensorial materials. The first step is to omit all parts that are not needed for the sensorial task and to find new solutions for a gentle integration. This is what we call function scale integration. The paper discusses sensor embedding in the human hand as an example of integration in nature, new technological applications and main challenges associated with this approach.     

Preliminary results for SeaWiFS vicarious calibration coefficients in the Baltic Sea

The technique of vicarious calibration is used in connection with an atmospheric correction to improve the Sea viewing Wide Field of view Sensor (SeaWiFS) normalized water-leaving radiance by the first determination of mean vicarious calibration coefficients from in situ measurements in the Baltic Sea. A necessary adjustment of the SeaWiFS pre-flight calibration slope was found to be +3.5%, +0.3%, ?1.7%, ?0.4%, +0.8% and ?1.3% for the first six SeaWiFS channels. The derived mean vicarious calibration coefficients are higher than the coefficients in the standard SeaWiFS Data Analysis System (SeaDAS) software but with similar shape and good agreement with other research results. The coefficients were used to obtain better normalized water-leaving radiance from SeaWiFS measurements in the Baltic Sea. The deviations of calculated to measured radiances in the open Baltic Sea are between 3% and 47% in the channels 412 to 670?nm, with the trend of higher deviations in the blue channels. The objective of radiance determination in all SeaWiFS channels with a maximum uncertainty of 5% in clear water regions is probably not reachable in the turbid water of the Baltic Sea.