As musculoskeletal illnesses continue to increase, practical computerised muscle modelling is crucial. This paper addresses this concern by proposing a mathematical model for a dynamic 3D geometrical surface representation of muscles using a Radial Basis Function (RBF) approximation technique. The objective is to obtain a smoother surface while minimising data use, contrasting it from classical polygonal (e.g. triangular) surface mesh models or volumetric (e.g. tetrahedral) mesh models. The paper uses RBF implicit surface description to describe static surface generation and dynamic surface deformations based on its spatial curvature preservation during the deformation. The novel method is tested on multiple data sets, and the experiments show promising results according to the introduced metrics.

A new approach is proposed for lossless raster image compression employing interpolative coding. A new multifunction prediction scheme is presented first. Then, interpolative coding, which has not been applied frequently for image compression, is explained briefly. Its simplification is introduced in regard to the original approach. It is determined that the JPEG LS predictor reduces the information entropy slightly better than the multi-functional approach. Furthermore, the interpolative coding was moderately more efficient than the most frequently used arithmetic coding. Finally, our compression pipeline is compared against JPEG LS, JPEG 2000 in the lossless mode, and PNG using 24 standard grayscale benchmark images. JPEG LS turned out to be the most efficient, followed by JPEG 2000, while our approach using simplified interpolative coding was moderately better than PNG. The implementation of the proposed encoder is extremely simple and can be performed in less than 60 lines of programming code for the coder and 60 lines for the decoder, which is demonstrated in the given pseudocodes.

Outside the main mountain ranges and high North and South regions, individual isolated very small glaciers are the only glacier remnants and exceptional high-mountain active geomorphosites, which can be used to represent climate change consequences first hand to the local general public. The isolated, very small Triglav glacier in Slovenia was used to represent 3D glacier area changes for the period 1829–2016, together with long-term meteorological changes. Spatio-temporal changes of the glacier were derived mainly from old images and postcards with the help of interactive orientation (monoplotting), which enables the acquisition of a 3D glacier boundary from a single image by using a modern detailed digital elevation model. Very intuitive 3D visualisation was prepared, which shows the spatio-temporal changes of the glacier area, together with changes in average annual temperature and maximum annual snow depth. The last two are presented by colour palettes, where red colours represent stages when temperatures or maximum snow depths deviate from long-term averages in a negative way, meaning accelerating the glacier area reduction. Blue colours are used for stages when these parameters deviate from long-term averages in a positive way, meaning preserving the glacier area. From this 3D visualisation, one can easily recognise which meteorological parameter is the most important for the Triglav glacier preservation; this is the maximum annual snow depth. Such kind of 3D visualisation has a great potential for promotion of other active or evolving passive geomorphosites too.

Most 3D point cloud watermarking techniques apply Principal Component Analysis (PCA) to protect the watermark against affine transformation attacks. Unfortunately, they fail in the case of cropping and random point removal attacks. In this work, an alternative approach is proposed that solves these issues efficiently. A point cloud registration technique is developed, based on a 3D convex hull. The scale and the initial rigid affine transformation between the watermarked and the original point cloud can be estimated in this way to obtain a coarse point cloud registration. An iterative closest point algorithm is performed after that to align the attacked watermarked point cloud to the original one completely. The watermark can then be extracted from the watermarked point cloud easily. The extensive experiments confirmed that the proposed approach resists the affine transformation, cropping, random point removal, and various combinations of these attacks. The most dangerous is an attack with noise that can be handled only to some extent. However, this issue is common to the other state-of-the-art approaches.

This paper presents a novel robust approach developed specially for watermarking airborne LiDAR data, which consist of a large cloud of geo-referenced points and has some unique characteristics. The approach consists of the following steps: (1) Defining the marker circular areas, in which the watermark bit will be embedded; (2) Dividing the marker circular areas uniformly into smaller circular areas by applying the sunflower seed distribution algorithm; (3) Using the points in these smaller circular areas to construct the input values for the Discrete Cosine Transformation (DCT); (4) Changing the last DCT coefficient; (5) Perform Inverse Discrete Cosine Transformation (IDCT), and perturbing the points within smaller circular areas according to the output values from this inverse transformation. Applying our approach, the watermark was dispersed into a set of points within the marker circular areas. The watermark bits are embedded multiple times in different marker circular areas. Thus, the robustness of the watermark was increased against various attacks. The watermark extraction process is practically the same, except in the final step, in which only the sign of each last DCT coefficient is checked, and decisions are made about the value of the watermark bits. Several experiments were performed to analyse the robustness of our watermarking schema against the most probable attacks.

This letter considers a new approach for the lossless progressive compression of light detection and ranging (LiDAR) data stored within a LAS file (public file format for the interchange of three-dimensional point cloud data), which is used for storing the results of LiDAR scanning. The presented method builds a hierarchical data model for arranging LAS points into different levels in one pass. The higher levels are compressed using variable length and arithmetic coding, whilst the lower levels apply a prediction model of the non-progressive compression schema. The order of the points, as captured by the LiDAR scanner, has to be preserved within each level as better compression ratios are achieved in this way.

A new method is introduced for the lossy compression of a LAS file. LAS files store the results from LiDAR scanning, and contain a huge amount of points with associated scalar values. The proposed method consists of four steps: eliminating those points within the over-sampled regions, moving the position of the remaining points within the user-specified limits, variable-length coding, and arithmetic compression. This method was compared with the only known lossy LAS files compression method, as owned by LizardTech (TM). As shown by experimentation, the proposed method is considerably better than the referenced method.

This paper considers a new method for reconstructing deliberately-corrupted pixels in raster images. Firstly, a faster approach for reconstructing corrupted pixels is proposed by applying a processing-circle instead of a processing-square. It is shown that the obtained quality of the reconstructed image is no worse because of this. The quality of the reconstruction is further improved by controlling the pixel corrupting process within the input image. It is shown that a combination of the processing-circle approach and data-dependent corruption reduces the reconstruction time, and the mistakes of the reconstructed pixels.

Various blending functions can be applied in the volumetric modelling of clouds. This paper analyzes the impact of five different blending functions (linear, cubic, Wyvill-1, Wyvill-2, and cosine) on cloud shape, structure, and area. We have found that there are only negligible differences among them. Therefore, we can conclude that the simple linear blending function satisfies all the desired properties for generating cloud images by volumetric modelling.

Current implicit blending techniques are mostly designed for use in surface modelling, where only boundaries of the object defined by the implicit primitives are important. In contrast, in volumetric implicit modelling the interior of the object is also significant, which requires different and more suitable techniques for combining implicit primitives. In this paper, we first discuss irregularities that occur using the current techniques. Then, a new technique for blending implicit primitives, especially appropriate in volumetric modelling (e.g., cloud modelling), is introduced. It overcomes these abnormalities and gives us better results than current techniques.

Realistic modeling and animating clouds are ones of the hardest tasks in computer animation. There are different techniques used in computer graphics for representing clouds. Our work is based on volume-rendered implicit junction, for volumetric cloud modeling. Basically, implicit primitives are controlled by a modified particle system. We propose genetic algorithms for initialization of the particle system. They provide an alternative to traditional optimization techniques and are also useful in the key-frame animation. Additionally, we introduce some new ideas in volumetric clouds animation: advanced techniques for controlling a large collection of individual elements - flocks and autonomous behavior. Some properties of these techniques are also applicable in the cloud animation.