Vol 44, No 4 (2018)

Stochastic ray tracing is used for rendering photorealistic images formed by augmented reality optical systems that combine the image generated by an optoelectronic device with the image of the environment. Methods for improving the efficiency of stochastic ray tracing that preserve the physical correctness of the simulation are proposed. Using a head-up display (HUD) as an example, it is shown that the forward stochastic ray tracing methods are sometimes more efficient than backward stochastic ray tracing methods for the visual simulation of augmented reality images. Approaches making it possible to combine the forward, backward, and bidirectional ray tracing in a unified simulation procedure are proposed. The results are illustrated by synthesized images produced by the optical system of head-up display.

Integration layer between digital content creation software (DCCS) and rendering software in a form of specialized database is proposed in this paper. In our approach, we focus on providing fast 3D-scene updates, ability to work with large digital assets (not fitting into memory), importing and exporting arbitrary parameters, serialization, convenient debugging tools and distributed rendering. Such database can be used as means to integrate different rendering engines with DCCS and also to transfer data between different DCCS.

Personalizing the three-dimensional atlas model (template) of an organ under analysis based on the data of patient’s three-dimensional examination is an important task for modern digital medicine. The template should be represented by a set of points Y and marked with the key points for the model (landmarks). The template is personalized by the non-rigid registration of the set Y with the set of points X that represents patient’s tomogram. Presently, the coherent point drift (CPD) is the most popular method for solving the problem of non-rigid alignment. In this paper, we propose and explore an approach that substantially improves the CPD result in the problem of automatic registration of cephalometric points (CPs). The proposed algorithm remains robust in the presence of significant skull deformations. Traditionally, 3D cephalometry uses the geometric descriptor of a CP, which refines the position of the point on the bone surface. However, the result of applying the descriptor depends significantly on the accuracy of its anatomical basis reconstruction. The proposed approach solves this problem by clarifying the basis of geometric descriptors, the supporting elements of which are orbital planes and the Frankfurt (orbital-ear) horizontal. For this purpose, additionally marked points of the orbitals (YO) are included into the template Y. Once Y and X are aligned by the CPD method, the plane positions of the orbitals are refined by solving a linear regression problem on the subsets of YO for the left and right orbitals. Cases of refinement with and without use of Tikhonov regularization (ridge-regression) are analyzed. The effect of the increase in the cardinality of the set X relative to Y on the registration accuracy is investigated. It is found that the condition |X| < |Y| has a negative effect on the accuracy, which increases when the cardinality of X relative to Y decreases. The refinement of CPs by the geometric descriptor is carried out on tomogram data in the region around CPs that is found by the CPD method. The dimensions of this region along three coordinates are determined by the anatomical domain of a particular CP descriptor. The quality of the algorithm is measured by the Euclidean distance between hand-marked and automatically found points. The template Y for the algorithm is built on a trauma-deformed skull. The algorithm is quantitatively verified by registering the CPs of orbitals and cheekbones from the data of four tomograms for the deformed skull. A key feature and source of high accuracy of the approach is the use of linear regression with Tikhonov regularization to refine it. As a result, 81.25% of the points found fall within a radius of 2 mm from the hand-marked points and 100% of the points fall within a radius of 4 mm.

Image registration problem often arises in microscopy when analyzing cell images. The most popular registration methods are rigid methods that use affine transformations. These methods are good enough for different types of images and image modalities, but they are very slow. This makes speed optimization techniques for these methods of particular importance. In this paper, we propose an algorithm for finding the optimal image downsampling coefficient to speedup image registration methods. The algorithm is tested for different rigid registration methods on HeLa cell images.

This paper is devoted to a unified approach to monitoring data generated by various electronic devices that are based on programmable microcontrollers. We suggest that communication between visualization systems and target devices be automatically tuned by retrieving the description of the input/output data structure from the firmware of the devices. For this purpose, we propose an ontology-based generator of firmware parsers. In our approach, the ontology that describes the syntax of input/output statements of different programming languages and the generator of firmware parsers become an essential part of the visualization system. Next, we propose to enrich the visualization pipeline with a data filtering stage. To make the filtering and rendering stages highly configurable, we use data flow diagrams (DFDs) that define data transformation. To enable the user to compose these diagrams, we develop a special high-level graphical editor. The description of DFD nodes is stored in the ontological knowledge base of the visualization system. To specify the nodes in ontological notation, we use ontologies of semantic filters, visual objects, and graphical scenes. We implement the proposed approach in the SciVi multiplatform client-server scientific visualization system and test its new capabilities by monitoring the orientation and light direction sensors.