6,583 research outputs found
Advanced Algorithms for 3D Medical Image Data Fusion in Specific Medical Problems
FĂșze obrazu je dnes jednou z nejbÄĆŸnÄjĆĄĂch avĆĄak stĂĄle velmi diskutovanou oblastĂ v lĂ©kaĆskĂ©m zobrazovĂĄnĂ a hraje dĆŻleĆŸitou roli ve vĆĄech oblastech lĂ©kaĆskĂ© pĂ©Äe jako je diagnĂłza, lĂ©Äba a chirurgie. V tĂ©to dizertaÄnĂ prĂĄci jsou pĆedstaveny tĆi projekty, kterĂ© jsou velmi Ășzce spojeny s oblastĂ fĂșze medicĂnskĂœch dat. PrvnĂ projekt pojednĂĄvĂĄ o 3D CT subtrakÄnĂ angiografii dolnĂch konÄetin. V prĂĄci je vyuĆŸito kombinace kontrastnĂch a nekontrastnĂch dat pro zĂskĂĄnĂ kompletnĂho cĂ©vnĂho stromu. DruhĂœ projekt se zabĂœvĂĄ fĂșzĂ DTI a T1 vĂĄhovanĂœch MRI dat mozku. CĂlem tohoto projektu je zkombinovat stukturĂĄlnĂ a funkÄnĂ informace, kterĂ© umoĆŸĆujĂ zlepĆĄit znalosti konektivity v mozkovĂ© tkĂĄni. TĆetĂ projekt se zabĂœvĂĄ metastĂĄzemi v CT ÄasovĂœch datech pĂĄteĆe. Tento projekt je zamÄĆen na studium vĂœvoje metastĂĄz uvnitĆ obratlĆŻ ve fĂșzovanĂ© ÄasovĂ© ĆadÄ snĂmkĆŻ. Tato dizertaÄnĂ prĂĄce pĆedstavuje novou metodologii pro klasifikaci tÄchto metastĂĄz. VĆĄechny projekty zmĂnÄnĂ© v tĂ©to dizertaÄnĂ prĂĄci byly ĆeĆĄeny v rĂĄmci pracovnĂ skupiny zabĂœvajĂcĂ se analĂœzou lĂ©kaĆskĂœch dat, kterou vedl pan Prof. JiĆĂ Jan. Tato dizertaÄnĂ prĂĄce obsahuje registraÄnĂ ÄĂĄst prvnĂho a klasifikaÄnĂ ÄĂĄst tĆetĂho projektu. DruhĂœ projekt je pĆedstaven kompletnÄ. DalĆĄĂ ÄĂĄst prvnĂho a tĆetĂho projektu, obsahujĂcĂ specifickĂ© pĆedzpracovĂĄnĂ dat, jsou obsaĆŸeny v disertaÄnĂ prĂĄci mĂ©ho kolegy Ing. Romana Petera.Image fusion is one of todayÂŽs most common and still challenging tasks in medical imaging and it plays crucial role in all areas of medical care such as diagnosis, treatment and surgery. Three projects crucially dependent on image fusion are introduced in this thesis. The first project deals with the 3D CT subtraction angiography of lower limbs. It combines pre-contrast and contrast enhanced data to extract the blood vessel tree. The second project fuses the DTI and T1-weighted MRI brain data. The aim of this project is to combine the brain structural and functional information that purvey improved knowledge about intrinsic brain connectivity. The third project deals with the time series of CT spine data where the metastases occur. In this project the progression of metastases within the vertebrae is studied based on fusion of the successive elements of the image series. This thesis introduces new methodology of classifying metastatic tissue. All the projects mentioned in this thesis have been solved by the medical image analysis group led by Prof. JiĆĂ Jan. This dissertation concerns primarily the registration part of the first project and the classification part of the third project. The second project is described completely. The other parts of the first and third project, including the specific preprocessing of the data, are introduced in detail in the dissertation thesis of my colleague Roman Peter, M.Sc.
Multi-Modal Enhancement Techniques for Visibility Improvement of Digital Images
Image enhancement techniques for visibility improvement of 8-bit color digital images based on spatial domain, wavelet transform domain, and multiple image fusion approaches are investigated in this dissertation research.
In the category of spatial domain approach, two enhancement algorithms are developed to deal with problems associated with images captured from scenes with high dynamic ranges. The first technique is based on an illuminance-reflectance (I-R) model of the scene irradiance. The dynamic range compression of the input image is achieved by a nonlinear transformation of the estimated illuminance based on a windowed inverse sigmoid transfer function. A single-scale neighborhood dependent contrast enhancement process is proposed to enhance the high frequency components of the illuminance, which compensates for the contrast degradation of the mid-tone frequency components caused by dynamic range compression. The intensity image obtained by integrating the enhanced illuminance and the extracted reflectance is then converted to a RGB color image through linear color restoration utilizing the color components of the original image. The second technique, named AINDANE, is a two step approach comprised of adaptive luminance enhancement and adaptive contrast enhancement. An image dependent nonlinear transfer function is designed for dynamic range compression and a multiscale image dependent neighborhood approach is developed for contrast enhancement. Real time processing of video streams is realized with the I-R model based technique due to its high speed processing capability while AINDANE produces higher quality enhanced images due to its multi-scale contrast enhancement property. Both the algorithms exhibit balanced luminance, contrast enhancement, higher robustness, and better color consistency when compared with conventional techniques.
In the transform domain approach, wavelet transform based image denoising and contrast enhancement algorithms are developed. The denoising is treated as a maximum a posteriori (MAP) estimator problem; a Bivariate probability density function model is introduced to explore the interlevel dependency among the wavelet coefficients. In addition, an approximate solution to the MAP estimation problem is proposed to avoid the use of complex iterative computations to find a numerical solution. This relatively low complexity image denoising algorithm implemented with dual-tree complex wavelet transform (DT-CWT) produces high quality denoised images
Analysis domain model for shared virtual environments
The field of shared virtual environments, which also
encompasses online games and social 3D environments, has a
system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model
Hierarchical Variance Reduction Techniques for Monte Carlo Rendering
Ever since the first three-dimensional computer graphics appeared half a century ago, the goal has been to model and simulate how light interacts with materials and objects to form an image. The ultimate goal is photorealistic rendering, where the created images reach a level of accuracy that makes them indistinguishable from photographs of the real world. There are many applications ñ visualization of products and architectural designs yet to be built, special effects, computer-generated films, virtual reality, and video games, to name a few. However, the problem has proven tremendously complex; the illumination at any point is described by a recursive integral to which a closed-form solution seldom exists. Instead, computer simulation and Monte Carlo methods are commonly used to statistically estimate the result. This introduces undesirable noise, or variance, and a large body of research has been devoted to finding ways to reduce the variance. I continue along this line of research, and present several novel techniques for variance reduction in Monte Carlo rendering, as well as a few related tools. The research in this dissertation focuses on using importance sampling to pick a small set of well-distributed point samples. As the primary contribution, I have developed the first methods to explicitly draw samples from the product of distant high-frequency lighting and complex reflectance functions. By sampling the product, low noise results can be achieved using a very small number of samples, which is important to minimize the rendering times. Several different hierarchical representations are explored to allow efficient product sampling. In the first publication, the key idea is to work in a compressed wavelet basis, which allows fast evaluation of the product. Many of the initial restrictions of this technique were removed in follow-up work, allowing higher-resolution uncompressed lighting and avoiding precomputation of reflectance functions. My second main contribution is to present one of the first techniques to take the triple product of lighting, visibility and reflectance into account to further reduce the variance in Monte Carlo rendering. For this purpose, control variates are combined with importance sampling to solve the problem in a novel way. A large part of the technique also focuses on analysis and approximation of the visibility function. To further refine the above techniques, several useful tools are introduced. These include a fast, low-distortion map to represent (hemi)spherical functions, a method to create high-quality quasi-random points, and an optimizing compiler for analyzing shaders using interval arithmetic. The latter automatically extracts bounds for importance sampling of arbitrary shaders, as opposed to using a priori known reflectance functions. In summary, the work presented here takes the field of computer graphics one step further towards making photorealistic rendering practical for a wide range of uses. By introducing several novel Monte Carlo methods, more sophisticated lighting and materials can be used without increasing the computation times. The research is aimed at domain-specific solutions to the rendering problem, but I believe that much of the new theory is applicable in other parts of computer graphics, as well as in other fields
Design of a High-Speed Architecture for Stabilization of Video Captured Under Non-Uniform Lighting Conditions
Video captured in shaky conditions may lead to vibrations. A robust algorithm to immobilize the video by compensating for the vibrations from physical settings of the camera is presented in this dissertation. A very high performance hardware architecture on Field Programmable Gate Array (FPGA) technology is also developed for the implementation of the stabilization system. Stabilization of video sequences captured under non-uniform lighting conditions begins with a nonlinear enhancement process. This improves the visibility of the scene captured from physical sensing devices which have limited dynamic range. This physical limitation causes the saturated region of the image to shadow out the rest of the scene. It is therefore desirable to bring back a more uniform scene which eliminates the shadows to a certain extent. Stabilization of video requires the estimation of global motion parameters. By obtaining reliable background motion, the video can be spatially transformed to the reference sequence thereby eliminating the unintended motion of the camera.
A reflectance-illuminance model for video enhancement is used in this research work to improve the visibility and quality of the scene. With fast color space conversion, the computational complexity is reduced to a minimum. The basic video stabilization model is formulated and configured for hardware implementation. Such a model involves evaluation of reliable features for tracking, motion estimation, and affine transformation to map the display coordinates of a stabilized sequence. The multiplications, divisions and exponentiations are replaced by simple arithmetic and logic operations using improved log-domain computations in the hardware modules. On Xilinx\u27s Virtex II 2V8000-5 FPGA platform, the prototype system consumes 59% logic slices, 30% flip-flops, 34% lookup tables, 35% embedded RAMs and two ZBT frame buffers. The system is capable of rendering 180.9 million pixels per second (mpps) and consumes approximately 30.6 watts of power at 1.5 volts. With a 1024Ă1024 frame, the throughput is equivalent to 172 frames per second (fps).
Future work will optimize the performance-resource trade-off to meet the specific needs of the applications. It further extends the model for extraction and tracking of moving objects as our model inherently encapsulates the attributes of spatial distortion and motion prediction to reduce complexity. With these parameters to narrow down the processing range, it is possible to achieve a minimum of 20 fps on desktop computers with Intel Core 2 Duo or Quad Core CPUs and 2GB DDR2 memory without a dedicated hardware
Neural Radiance Fields: Past, Present, and Future
The various aspects like modeling and interpreting 3D environments and
surroundings have enticed humans to progress their research in 3D Computer
Vision, Computer Graphics, and Machine Learning. An attempt made by Mildenhall
et al in their paper about NeRFs (Neural Radiance Fields) led to a boom in
Computer Graphics, Robotics, Computer Vision, and the possible scope of
High-Resolution Low Storage Augmented Reality and Virtual Reality-based 3D
models have gained traction from res with more than 1000 preprints related to
NeRFs published. This paper serves as a bridge for people starting to study
these fields by building on the basics of Mathematics, Geometry, Computer
Vision, and Computer Graphics to the difficulties encountered in Implicit
Representations at the intersection of all these disciplines. This survey
provides the history of rendering, Implicit Learning, and NeRFs, the
progression of research on NeRFs, and the potential applications and
implications of NeRFs in today's world. In doing so, this survey categorizes
all the NeRF-related research in terms of the datasets used, objective
functions, applications solved, and evaluation criteria for these applications.Comment: 413 pages, 9 figures, 277 citation
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