KEYWORDS: Bone, 3D modeling, Minerals, Fractal analysis, 3D microstructuring, 3D image processing, Data modeling, Network architectures, Statistical analysis, 3D metrology
We develop and evaluate a novel 3D computational bone framework, which is capable of enabling quantitative
assessment of bone micro-architecture, bone mineral density and fracture risks. Our model for bone mineral is
developed and its parameters are estimated from imaging data obtained with dual energy x-ray absorptiometry
and x-ray imaging methods. Using these parameters, we propose a proper 3D microstructure bone model. The
research starts by developing a spatio-temporal 3D microstructure bone model using Voronoi tessellation. Then,
we simulate and analyze the architecture of human normal bone network and osteoporotic bone network with
edge pruning process in an appropriate ratio. Finally, we design several measurements to analyze Bone Mineral
Density (BMD) and bone strength based on our model. The validation results clearly demonstrate our 3D
Microstructure Bone Model is robust to reflect the properties of bone in the real world.
Effective and efficient approaches to monitor and manage maneuvering objects are of great importance in various applications, such as wide battlefields, traffics, and wireless communications. Modern airborne radar sensors can provide wide-area surveillance coverage of ground activities. The huge volume of radar data renders it impractical and inefficient to examine all the activities of individual moving object. Clustering moving objects and predicting motion tendencies of large groups are becoming a crucial issue for optimizing resource distribution and formulating sound decisions. However, most traditional clustering techniques are static-object-oriented and not effective at clustering maneuvering objects. In addition, the radar data intermittence and noise data, which are caused by extraneous objects and stationary clutter background, are major difficulties in clustering and predicting groups. In this paper, we present a dynamic-object-oriented clustering approach to detecting and predicting large group activities over time. We propose a "core member" concept to support dynamic-object-oriented clustering and to mitigate the effects of data intermittence and noise data. In general, some special targets always tend to remain in a constant group and stay near the center of that group. To a large extent, the movement of these targets represents the activity of the entire group. To exploit this characteristic, we consider these special targets to be core members of their own cluster. The movements of the core members can help us detect clusters and predict their future movements. The performance and results of the application of our approach to CASTFOREM data sets are also presented.
Many applications demand the capability of retrieval based on image content. A classification mechanism is needed to categorize images based on feature similarity. An effective classification of the images can support efficient retrieval of images. In this paper, we investigate a feature-based approach to image clustering and retrieval. Four different texture-based feature sets of images are extracted using Haar and Daubechies wavelet transforms. Using multi- resolution property of wavelets, we extract the features at different levels. The experimental results of our clustering approach on air photo images are reported.
In order to flexibly and efficiently store, manage, and present video data streams, continuous video data must be chopped into video objects and stored into database. This paper investigates systematic strategies for supporting continuous and synchronized presentation of video data streams in multimedia database systems. Compressed video data streams are segmented and stored as sets of video objects coupled with specified synchronization requirements. Strategies for efficiently scheduling and buffering video objects are presented which guarantee the hiccup-free presentations of video streams. Delay effects are considered in these strategies. We propose to extend the existing object-oriented database system (OODBS) techniques to include the proposed video presentation mechanisms. We are currently designing and implementing a multimedia presentation tool (termed MediaShow) on top of O2, a well-known OODBS, as a basis for our implementation. However, the design strategies can be generally used in any OODBS environments that support C++ interface.
Image compression techniques based on wavelet and fractal coding have been recognized significantly useful in image texture classification and discrimination. In fractal coding approach, each image is represented by a set of self-transformations through which an approximation of the original image can be reconstructed. These transformations of images can be utilized to distinguish images. The fractal coding technique can be extended to effectively determine the similarity between images. We introduce a joint fractal coding technique, applicable to pairs of images, which can be used to determine the degree of their similarity. Our experimental results demonstrate that fractal code approach is effective for content-based image retrieval. In wavelet transform approach, the wavelet transform decorrelates the image data into frequency domain. Feature vectors of images can be constructed from wavelet transformations, which can also be utilized to distinguish images through measuring distances between feature vectors. Our experiments indicate that this approach is also effective on content-based similarity comparison between images. More specifically, we observe that wavelets transform approach performs more effective on content- based similarity comparison on those images which contain strong texture features, where fractal coding approach performs relatively more uniformly well for various type of images.
This paper presents an approach to texture-based image retrieval which determines image similarity on the basis of the matching of fractal codes. Image fractal codes are generated via a fractal image compression technique that has been recently proposed as an effective image compression method. Each image is represented by a set of self-transformations through which an approximation of the original image can be reconstructed. These self-transformations, which are unique to each image and are semantically rich, are termed fractal codes. An image data model is proposed which constructs each image as a hierarchical structure. Each image is decomposed into block-based segments which are then assembled by a hierarchy on the basis of inclusion relationships. Each segment is then fractally encoded. The fractal codes of an iconic image are used as texture key and are matched with the fractal codes of images in a database by applying searching and matching algorithms to the hierarchies of the database images to locate the segments which best match the fractal codes of the iconic image. Retrievals of both exact and inexact matching of images are supported.
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