Voxel-based Modeling with Multi-resolution Wavelet Transform for Layered Manufacturing

A voxel-based modeling system with multi-resolution for layered manufacturing is presented in this paper. When dealing with discretized data input, voxel-based modeling shows its clear advantages over the conventional geometric modeling methods. To increase the efficiency of voxel data due to its large storage space requirement, multi-resolution method with wavelet transform technique is implemented. Combining with iso-surface generation and lossless polygon reduction, this voxel-based modeling method can easily work with layered manufacturing. To demonstrate these concepts, components with different resolutions are built using Layered Manufacturing and presented in the paper.Mechanical Engineerin

Research issues in data modeling for scientific visualization

This article summarizes some topics of modeling as they impinge on the future development of scientific data visualization. The benefits from visualization techniques in analyzing data are well established, but to build on these pioneering efforts, one must recognize modeling as a distinct structural component in the larger context of visualization and problem-solving systems. Volume modeling is the entry way to this arena of future development, and model-based rendering describes how scientists will view the results. Important side developments such as multiresolution modeling and model-based segmentation will contribute structural capability to these systems. All of these components ultimately depend on the mathematical foundations of scattered data modeling and on model validation and standards to incorporate this modeling methodology into effective tools for scientific inquiry.Postprint (published version

Fast Random Access to Wavelet Compressed Volumetric Data Using Hashing

We present a new approach to lossy storage of the coefficients of wavelet transformed data. While it is common to store the coefficients of largest magnitude (and let all other coefficients be zero), we allow a slightly different set of coefficients to be stored. This brings into play a recently proposed hashing technique that allows space efficient storage and very efficient retrieval of coefficients. Our approach is applied to compression of volumetric data sets. For the ``Visible Man'' volume we obtain up to 80% improvement in compression ratio over previously suggested schemes. Further, the time for accessing a random voxel is quite competitive

Error-driven adaptive resolutions for large scientific data sets

The process of making observations and drawing conclusions from large data sets is an essential part of modern scientific research. However, the size of these data sets can easily exceed the available resources of a typical workstation, making visualization and analysis a formidable challenge. Many solutions, including multiresolution and adaptive resolution representations, have been proposed and implemented to address these problems. This thesis describes an error model for calculating and representing localized error from data reduction and a process for constructing error-driven adaptive resolutions from this data, allowing fully-renderable error driven adaptive resolutions to be constructed from a single, high-resolution data set. We evaluated the performance of these adaptive resolutions generated with various parameters compared to the original data set. We found that adaptive resolutions generated with reasonable subdomain sizes and error tolerances show improved performance daring visualization

Volumetric Medical Images Visualization on Mobile Devices

Volumetric medical images visualization is an important tool in the diagnosis and treatment of diseases. Through history, one of the most dificult tasks for Medicine Specialists has been the accurate location of broken bones and of the damaged tissues during Chemotherapy treatment, among other applications; like techniques used in Neurological Studies. Thus these situations enhance the need of visualization in Medicine. New technologies, the improvement and development of new hardware as well as software and the updating of old ones for graphic applications have resulted in specialized systems for medical visualization. However the use of these techniques in mobile devices has been poor due to its low performance. In our work, we propose a client-server scheme, where the model is compressed in the server side and is reconstructed in a nal thin-client device. The technique restricts the natural density values to achieve good bone visualization in medical models, transforming the rest of the data to zero. Our proposal uses a tridimensional Haar Wavelet Function locally applied inside units blocks of 16x16x16, similar to the Wavelet Based 3D Compression Scheme for Interactive Visualization of Very Large Volume Data approach. We also implement a quantization algorithm which handles error coeficients according to the frequency distributions of these coe cients. Finally, we made an evaluation of the volume visualization; on current mobile devices .We present the speci cations for the implementation of our technique in the Nokia n900 Mobile Phone

Visualization-specific compression of large volume data

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Feature-preserving Reduction and Visualization of Industrial CT data using GLCM texture analysis and Mass-spring Model Deformation

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 8. 신영길.본 논문에서는 3D 볼륨 데이터에서 중요한 영역을 보존하면서 크기를 줄이는 방법을 제안한다. 볼륨 데이터에서 어느 부분이 중요한 영역인지를 결정하기 위해 질감 분석 방법 중 하나인 GLCM 균일도를 이용한 중요도 측정 모델을 제안하고, 이를 기반으로 한 MSM 기반의 볼륨 변형을 수행한다. 중요도가 반영된 볼륨 변형 과정을 통해, 중요한 영역은 상대적으로 크기가 확장되는 반면, 덜 중요한 영역은 줄어들게 된다. 이로 인해, 일반적으로 손실률이 높은 균일 다운샘플링을 이용한 압축 후에도 작은 크기의 중요한 특징점들이 손실되지 않고 보존될 수 있다. 실측 산업 영상 데이터를 이용한 실험을 통해, 그냥 균일 다운샘플링을 이용한 압축 결과에서는 사라진 작은 기공이나 수축 균열 형태의 결함 영역이 제안 방법에서는 보존되는 것을 확인할 수 있었다. 이 변형 볼륨을 원래 형태로 가시화하기 위해선 역변형 과정을 추가로 수행해야 하지만, 이 계산은 가시화 과정에 간단하게 추가할 수 있으며, 결과를 얻기 위한 소요시간에 유의미한 영향을 미치지 않는다.Non-destructive testing is a method which examines the internal structures of industrial components such as various machine parts without dissecting them. Recently, 3D CT based analysis enables more accurate inspection than traditional X-ray based tests. However, manipulating volumetric data acquired by CT is still challenging due to its huge size of the volume data. This dissertation proposes a novel method that reduces the size of 3D volume data while preserving important features in the data. Our method quantifies the importance of features in the 3D data based on gray level co-occurrence matrix (GLCM) texture analysis and represents the volume data using a simple mass-spring model. According to the measured importance value, blocks containing important features expand while other blocks shrink. After deformation, small features are exaggerated on deformed volume space, and more likely to survive during the uniform volume reduction. Experimental results showed that our method well preserved the small features of the original volume data during the reduction without any artifact comparing with the previous methods. Although additional inverse deformation process was required for the rendering of the deformed volume data, the rendering speed of the deformed volume data was much faster than that of the original volume data.초록 i 목차 iii 그림 목차 vi 표 목차 x 1장 서론 1 1.1 볼륨 렌더링 1 1.2 비파괴검사 2 1.3 연구 내용 4 1.4 논문의 구성 6 2장 관련 연구 7 2.1 볼륨 렌더링 알고리즘 7 2.1.1 볼륨 데이터의 특성 7 2.1.2 표면 추출 기법 8 2.1.3 직접 볼륨 렌더링 10 2.2 압축 볼륨 렌더링 17 2.2.1 벡터 양자화 18 2.2.2 변환 부호화 19 2.2.3 다중-해상도 기반 기법 23 2.2.4 볼륨 변형 기반 방법 25 2.3 질량-스프링 기반 볼륨 변형 모델 27 2.4 산업용 CT 영상의 중요 특징점 측량 방법 30 3장 중요도 측정 기법 32 3.1 명암도 동시발생 행렬 32 3.2 GLCM 균일도 기반 중요도 모델 36 3.3 공기 영역 제거 44 4장 볼륨 변형, 축소 및 가시화 47 4.1 질량-스프링 모델 기반 볼륨 변형 47 4.2 볼륨 축소 54 4.3 역변형 및 렌더링 55 5장 실험 및 결과 58 5.1 화질 평가 60 5.2 속도 평가 65 5.3 파라미터 연구 69 6장 결론 74 6.1 요약 74 6.2 향후 연구 75 참고문헌 77 Abstract 83Docto