Abstract

The paper considers semi-global stereo matching in the context of vision-based driver assistance systems. The need for real-time performance in this field requires a design change of the originally proposed method to run on current hardware. This paper proposes such a new design; the novel strategy first generates a disparity map from half-resolution input images. The result is then used as prior to restrict the disparity search space for full-resolution computation. This approach is compared to an SGM strategy as employed currently in a state-of-the-art real-time FPGA solution. Furthermore, trinocular stereo evaluation is performed on ten real-world traffic sequences with a total of 4,000 trinocular frames. An extension to the original evaluation methodology is proposed to resolve ambiguities and to incorporate disparity density in a statistically meaningful way. Evaluation results indicate that the novel SGM method is up to 40% faster when compared to the previous strategy. It returns denser disparity maps, and is also more accurate on evaluated traffic scenes. Document type: Part of book or chapter of book

Abstract

The proposed paper describes a compression test analysis of JBIG standard algorithm. The aim of such work is to proof the effectiveness of this standard for images acquired through scanners and processed into a printer pipeline. The main issue of printer pipelines is the necessity to use a memory buffer to store scanned images for multiple prints. This work demonstrates that for very large scales the buffer can be fixed using medium compression case, using multiple scans in case of uncommon random patterns. Document type: Part of book or chapter of book

Abstract

Since the introduction of mental disorders as networks of causally interacting symptoms, this novel framework has received considerable attention. The past years have resulted in over 40 scientific publications and numerous conference symposia and workshops. Now is an excellent moment to take stock of the network approach: What are its most fundamental challenges, and what are potential ways forward in addressing them? After a brief conceptual introduction, we first discuss challenges to network theory: (1) What is the validity of the network approach beyond some commonly investigated disorders such as major depression? (2) How do we best define psychopathological networks and their constituent elements? And (3) how can we gain a better understanding of the causal nature and real-life underpinnings of associations among symptoms? Next, after a short technical introduction to network modeling, we discuss challenges to network methodology: (4) heterogeneity of samples studied with network analytic models, and (5) a lurking replicability crisis in this strongly data-driven and exploratory field. Addressing these challenges may propel the network approach from its adolescence into adulthood and promises advances in understanding psychopathology both at the nomothetic and idiographic level. Keywords: clinical psychology; dynamic systems; mental disorders; networks; personalized medicine; psychiatry; reproducibility Document type: Part of book or chapter of book

Abstract

Currently, large physics simulations produce 3D discretized field data whose individual isosurfaces, after conventional extraction processes, contain upwards of hundreds of millions of triangles. Detailed interactive viewing of these surfaces requires (a) powerful compression to minimize storage, and (b) fast view-dependent optimization of display triangulations to most effectively utilize high-performance graphics hardware. In this work, we introduce the first end-to-end multiresolution dataflow strategy that can effectively combine the top performing subdivision-surface wavelet compression and view-dependent optimization methods, thus increasing efficiency by several orders of magnitude over conventional processing pipelines. In addition to the general development and analysis of the dataflow, we present new algorithms at two steps in the pipeline that provide the “glue” that makes an integrated large-scale data visualization approach possible. A shrink-wrapping step converts highly detailed unstructured surfaces of arbitrary topology to the semi-structured meshes needed for wavelet compression. Remapping to triangle bintrees minimizes disturbing “pops” during realtime displaytriangulation optimization and provides effective selective-transmission compression for out-of-core and remote access to extremely large surfaces. Overall, this is the first effort to exploit semi-structured surface representations for a complete large-data visualization pipeline. Document type: Part of book or chapter of book

Abstract

Utility networks (pipelines and cables) are usually modelled by lines and nodes. The systems used for maintenance of utilities are typically two-dimensional, allowing for creating vertical profiles in limited key places. Since the position is visualized on 2D maps, it is rather difficult to distinguish between cables and pipelines with the same x,y coordinates. Such visualization might be misunderstood by both field workers (maintaining the utilities) and specialists. Therefore the interest in 3D visualization is rapidly increasing. Document type: Part of book or chapter of book