Steganography on 3D Models Using a Spatial Subdivision Technique

This paper discusses an approach to automating CAD model acquisition by creating binary space partitioning (BSP) trees from laser range data. Distinct views of the object, each represented by a BSP tree, are then merged using set operations. BSP trees have proven their utility in 3D modeling, graphics and image processing, and their tree structure allows efficient algorithms to be developed that are compact and numerically robust. These properties are of primary importance when considering an intermediate representation between raw sensor data and existing CAD models.

Non-revisiting stochastic search revisited: Results, perspectives, and future directions

An image coding technique based on symmetry extraction and Binary Space Partitioning (BSP) tree representation for still pictures is presented. Axes of symmetry, detected through a principal axis of inertia approach and a coefficient of symmetry measure, are used to divide recursively an input image into a finite number of convex regions. This recursive partitioning results in the BSP tree representation of the image data. The iterative partition occurs whenever the current left/right node of the tree cannot be represented `symmetrically' by its counterpart, i.e., the right/left node. This splitting process may also end whenever the region associated with a given node has homogeneous characteristics or its size falls below a certain threshold. Given a BSP tree partition for a given input image, and the `seed' leaf nodes (i.e., those that cannot be generated by mirroring their counterparts), the remaining leaf nodes of the tree are reconstructed using a predictive scheme with respect to the `seed' leaf nodes.

We present a reactive data structure, that is, a spatial data structure with detail levels. The two properties, spatial organization and detail levels, are the basis for a geographic information system (GIS) with a multi-scale database. A reactive data structure is a novel type of data structure catering to multiple detail levels with rapid responses to spatial queries. It is presented here as a modification of the binary space partitioning tree that includes the levels of detail. This tree is one of the few spatial data structures that does not organize space in a rectangular manner. A prototype system has been implemented. An important result of this implementation is that it shows that binary space partitioning trees of real maps have O(n) storage space complexity in contrast to the theoretical worst case O(n2 ), with n the number of line segments in the map.

Collision detection is a critical problem in multi-axis NC machining with complex machining environments. Using a combination of machining environment culling and a two-phase collision detection strategy, a rapid and robust collision detection approach is implemented. With this approach, the machining environment is initially subdivided and organized hierarchically using a binary space partitioning (BSP) tree structure. Before carrying out precise collision detection, the machining environment is culled conservatively to remove most of the irrelevant geometries from further checking steps which are relatively time consuming. For the later stage of precise collision detection, which is the most complicated and time-consuming task in many existing approaches, a strategy of using two-phase collision detection is developed in order to make the approach not only efficient in computation and but also maintain a high detection precision at the same time. Another important benefit of this approach is that it takes advantage of the rapid performance of graphics hardware to generate visibility information very efficiently. We show how this information could be used in assisting collision detection. An example of five-axis machining in a complex environment is adopted as the case study to demonstrate the proposed approach. It has shown that the collision detection task can be achieved with a near real-time performance.

The research aims to address one of the complex problems that may be encountered when generating computer images for three-dimensional (3D) objects, which is the hidden surface detection and elimination. Several methods have been devised, through the last three decades, to solve this problem. However, the most popular methods used widely nowadays are depth or Z-Buffer (ZB) and Binary Space Partition Tree (BSPT). The first method (ZB) is very simple and more general but requires additional memory to store depth values in addition to intensity. On the other hand, (BSPT) method is more complex to implement but requires memory for pixel intensity only. Modelling graphical database in a binary tree make the dealing with parts of the database feasible. This is so important for clipping a part or more of the database when outside the viewing zone. The focus of this paper is to study these two methods, design an algorithm to implement each of them. After that a reasonable procedure is to found and applied for testing the performance of each of the two methods using exactly the same graphic load. A step by step increase of this load should be possible so that the behavior of the execution time at different complexity level and on average bases can be reported. The outcome results of the two methods should be compared and a recommendation to use which one of them is to be concluded [1]. Since one of the most popular graphics library which is being used during the last ten years and nowadays is the Open Graphic Library( OpenGL) from(Silicon Graphic Incorporation (SGI)[2], the algorithms of this work are implemented and the related software is carried out using this library with visual C platform.

Presents a fast shadow generation algorithm for a large number of convex polyhedra (objects) in a 3D scene based on the concept of characteristic views (CVs). The concept of CVs is that the infinite possible views of a 3D polyhedron can be grouped into a finite number of classes which have the same visible polygons from some reference points (i.e. viewpoints or point light sources). The space can be partitioned into subspaces in which all reference points have the same object ordering. Basically, object-precision hidden surface removal algorithms and object-precision shadow generation algorithms are the same except for the reference point. The shadow volume binary space partitioning (SVBSP) tree algorithm is known to be efficient in a static environment in which the point light source can be changed. However, if a scene consists of many objects and each object consists of many polygons, the time complexity for generating and traversing the SVBSP tree increases rapidly because the SVBSP tree algorithm deals with polygons which are components of objects. Furthermore, the SYBSP tree algorithm suffers from polygon-splitting problems resulting in a high cost as the number of polygons increases. Our approach is object-oriented in the sense that an object is used as the basic unit and polygons can be considered as components of the associated object. For all light source positions, the object ordering for shadow generation is determined at the preprocessing stage. At run-time, the shadow detection algorithm is executed and, if necessary, shadow fragments are generated.

We present a volume and complexity bounded solid simplification of models represented by Binary Space Partition (BSP). Depending on the compact and robust representation of a solid model in BSP-tree, the boundary surface of a simplified model is guaranteed to be watertight and self-intersection free. Two techniques are investigated in this paper. The volume bounded convex simplification can collapse parts with small volumes on the model into a simple convex volume enclosing the volumetric cells on the input model. The selection of which region to simplify is based on a volume-difference metric, with the help of which the volume difference between the given model and the simplified one is minimized. Another technique is a plane collapse method which reduces the depth of the BSP-tree. These two techniques are integrated into our solid simplification algorithm to give satisfactory results.

Generating piecewise linear approximations of digitized or “densely sampled” curves is an important problem in many areas. Here, we consider how to approximate an arbitrary digitized 3-D space curve, made of n+1 points, with m line segments. We present an O(n 3 log m) time, O(n 2 log m) space, dynamic programming algorithm which finds an optimal approximation. We then introduce an iterative heuristic algorithm, based upon the notions of curve length and spherical image, which quickly computes a good approximation of a space curve in O(N iter n) time and O(n) space. We apply this fast heuristic algorithm to display space curve segments and implicit surface patches, and to linearly approximate curved 3D objects, made by rotational sweeping, by binary space partitioning trees that are well-balanced.

Association rules are useful for determining correlations between attributes of a relation and have applications in the marketing, financial, and retail sectors. Furthermore, optimized association rules are an effective way to focus on the most interesting characteristics involving certain attributes. Optimized association rules are permitted to contain uninstantiated attributes and the problem is to determine instantiations such that either the support, confidence, or gain of the rule is maximized. In this paper, we generalize the optimized gain association rule problem by permitting rules to contain disjunctions over uninstantiated numeric attributes. Our generalized association rules enable us to extract more useful information about seasonal and local patterns involving the uninstantiated attribute. For rules containing a single numeric attribute, we present an algorithm with linear complexity for computing optimized gain rules. Furthermore, we propose a bucketing technique that can result in a significant reduction in input size by coalescing contiguous values without sacrificing optimality. We also present an approximation algorithm based on dynamic programming for two numeric attributes. Using recent results on binary space partitioning trees, we show that the approximations are within a constant factor of the optimal optimized gain rules. Our experimental results with synthetic data sets for a single numeric attribute demonstrate that our algorithm scales up linearly with the attribute's domain size as well as the number of disjunctions. In addition, we show that applying our optimized rule framework to a population survey real-life data set enables us to discover interesting underlying correlations among the attributes.

We show that there is always a binary space partition (BSP) of size O(n log k) and an autopartition of size O(nk) for n disjoint line segments in the plane, assuming that the segments have k distinct orientations. In particular, if k is a constant, these bounds imply that there is a linear-size BSP and autopartition. Our proof is constructive and can be turned into algorithms computing such a BSP or autopartition in O(n2 ) and O(n2k ) times.

A new and efficient ray-tracing method based on the combination of the image theory and the binary space partitioning (BSP) algorithm is presented. The efficiency of the method allows a full three-dimensional implementation of the geometrical optics and the uniform theory of diffraction (GO/UTD). The main feature of the method is the possibility of analyzing the radio channel in general enclosed spaces, including indoor, outdoor and mixed indoor/urban environments. This approach can be used for developing efficient engineering tools for the analysis of the radio channel in wireless and mobile communications systems. Several results that show the accuracy and efficiency of the method are presented.

We introduce a parallel approach to geometric modeling of complex objects and scenes, combining a dataflow streaming of BSP trees with a partition of the object space into independent portions, to be evaluated in parallel with minimal interprocess communication. Binary Space Partition (BSP) is a space index used in graphics for hidden-surface removal and animation. We use BSP trees with fuzzy leaves as a progressive representation of solid meshes. Our approach is implemented as a dataflow with processes that progress concurrently, where each refinement of the input to a process is mapped instantly to a refinement of the output, so that the result is also a stream of progressive refinements. This framework allows for progressive generation of complex geometric parts and large-scale assemblies. We have adapted several graphics techniques, including BSP, boundary polygons, CSG, splines and subdivision methods, to fit into our dataflow graph, where four types of processes produce, transform, combineor consume mesh cells. This approach is scalable over different kinds of HPC hardware and different number of computing nodes, by way of the decomposition of the object space and of the distribution of computational processes. Compiling a generative geometric expression into a dataflow graph is well suited to SMP machines, whereas a space decomposition into independent portions fits well with computing clusters and grids.

In the mining of communication relationships from high-speed frequency-hopping radio signals, existing clustering methods often suffer from low accuracy when dealing with uneven-density data and poor efficiency in processing large-scale datasets. To address these issues, this paper proposes an improved OPTICS algorithm integrating Stratified Sampling and Binary Tree Partition. First, representative small-sample data are extracted through stratified sampling, and a binary tree partition strategy is applied to adaptively divide the reachability plot, enabling precise clustering of non-uniform density data. Second, based on the small-sample clustering results, the K-Nearest Neighbors algorithm is utilized to rapidly classify the remaining data, thereby reducing computational complexity. Finally, by combining the clustering results with communication constraints, inter-cluster matching is performed to determine radio communication relationships. Simulation results demonstrate that the proposed method not only improves clustering accuracy for non-uniform density data but also optimizes computational efficiency, enabling accurate identification of communication relationships within the target area based on the clustering results.

The Wavelet Transform is a multi-resolution transform, that is, it allows a form of time–frequency analysis (or translation–scale in wavelet speak). When using the Fourier transform the result is a very precise analysis of the frequencies contained in the signal, but no information on when those frequencies occurred. The wavelet based image compression algorithms are used widely compared with other conventional compression algorithms. The wavelet coding based on the coefficient selection and sub band level. In this paper we have used two wavelets such as spherical and geometric wavelets. The spherical representation is a hierarchical description of how total energy gets distributed within each wavelet sub band.In the proposed method, we used fuzzy quantization technique for coefficient selection in the spherical wavelet. The other scheme introduces binary space partitioning scheme and geometric wavelet, where the existing pruning method of binary space partitioning is replaced by the genetic algorithm. We had another experiment with geometric wavelet with Artificial Bee Colony (ABC) algorithm. The experimental results for all the three methods are discussed in this paper. The advantages of these methods are the improved PSNR values at high and medium bit rates.