189 research outputs found
A Parallel Thinning Algorithm for Grayscale Images
International audienceGrayscale skeletonization offers an interesting alternative to traditional skeletonization following a binarization. It is well known that parallel algorithms for skeletonization outperform sequential ones in terms of quality of results, yet no general and well defined framework has been proposed until now for parallel grayscale thinning. We introduce in this paper a parallel thinning algorithm for grayscale images, and prove its topological soundness based on properties of the critical kernels framework. The algorithm and its proof, given here in the 2D case, are also valid in 3D. Some applications are sketched in conclusion
Robust and flexible multi-scale medial axis computation
The principle of the multi-scale medial axis (MMA) is important in that any object is detected at a blurring scale proportional to the size of the object. Thus it provides a sound balance between noise removal and preserving detail. The robustness of the MMA has been reflected in many existing applications in object segmentation, recognition, description and registration. This thesis aims to improve the computational aspects of the MMA. The MMA is obtained by computing ridges in a “medialness” scale-space derived from an image. In computing the medialness scale-space, we propose an edge-free medialness algorithm, the Concordance-based Medial Axis Transform (CMAT). It not only depends on the symmetry of the positions of boundaries, but also is related to the symmetry of the intensity contrasts at boundaries. Therefore it excludes spurious MMA branches arising from isolated boundaries. In addition, the localisation accuracy for the position and width of an object, as well as the robustness under noisy conditions, is preserved in the CMAT. In computing ridges in the medialness space, we propose the sliding window algorithm for extracting locally optimal scale ridges. It is simple and efficient in that it can readily separate the scale dimension from the search space but avoids the difficult task of constructing surfaces of connected maxima. It can extract a complete set of MMA for interfering objects in scale-space, e.g. embedded or adjacent objects. These algorithms are evaluated using a quantitative study of their performance for 1-D signals and qualitative testing on 2-D images
Automatic extraction of bronchus and centerline determination from CT images for three dimensional virtual bronchoscopy.
Law Tsui Ying.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 64-70).Abstracts in English and Chinese.Acknowledgments --- p.iiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Structure of Bronchus --- p.3Chapter 1.2 --- Existing Systems --- p.4Chapter 1.2.1 --- Virtual Endoscope System (VES) --- p.4Chapter 1.2.2 --- Virtual Reality Surgical Simulator --- p.4Chapter 1.2.3 --- Automated Virtual Colonoscopy (AVC) --- p.5Chapter 1.2.4 --- QUICKSEE --- p.5Chapter 1.3 --- Organization of Thesis --- p.6Chapter 2 --- Three Dimensional Visualization in Medicine --- p.7Chapter 2.1 --- Acquisition --- p.8Chapter 2.1.1 --- Computed Tomography --- p.8Chapter 2.2 --- Resampling --- p.9Chapter 2.3 --- Segmentation and Classification --- p.9Chapter 2.3.1 --- Segmentation by Thresholding --- p.10Chapter 2.3.2 --- Segmentation by Texture Analysis --- p.10Chapter 2.3.3 --- Segmentation by Region Growing --- p.10Chapter 2.3.4 --- Segmentation by Edge Detection --- p.11Chapter 2.4 --- Rendering --- p.12Chapter 2.5 --- Display --- p.13Chapter 2.6 --- Hazards of Visualization --- p.13Chapter 2.6.1 --- Adding Visual Richness and Obscuring Important Detail --- p.14Chapter 2.6.2 --- Enhancing Details Incorrectly --- p.14Chapter 2.6.3 --- The Picture is not the Patient --- p.14Chapter 2.6.4 --- Pictures-'R'-Us --- p.14Chapter 3 --- Overview of Advanced Segmentation Methodologies --- p.15Chapter 3.1 --- Mathematical Morphology --- p.15Chapter 3.2 --- Recursive Region Search --- p.16Chapter 3.3 --- Active Region Models --- p.17Chapter 4 --- Overview of Centerline Methodologies --- p.18Chapter 4.1 --- Thinning Approach --- p.18Chapter 4.2 --- Volume Growing Approach --- p.21Chapter 4.3 --- Combination of Mathematical Morphology and Region Growing Schemes --- p.22Chapter 4.4 --- Simultaneous Borders Identification Approach --- p.23Chapter 4.5 --- Tracking Approach --- p.24Chapter 4.6 --- Distance Transform Approach --- p.25Chapter 5 --- Automated Extraction of Bronchus Area --- p.27Chapter 5.1 --- Basic Idea --- p.27Chapter 5.2 --- Outline of the Automated Extraction Algorithm --- p.28Chapter 5.2.1 --- Selection of a Start Point --- p.28Chapter 5.2.2 --- Three Dimensional Region Growing Method --- p.29Chapter 5.2.3 --- Optimization of the Threshold Value --- p.29Chapter 5.3 --- Retrieval of Start Point Algorithm Using Genetic Algorithm --- p.29Chapter 5.3.1 --- Introduction to Genetic Algorithm --- p.30Chapter 5.3.2 --- Problem Modeling --- p.31Chapter 5.3.3 --- Algorithm for Determining a Start Point --- p.33Chapter 5.3.4 --- Genetic Operators --- p.33Chapter 5.4 --- Three Dimensional Painting Algorithm --- p.34Chapter 5.4.1 --- Outline of the Three Dimensional Painting Algorithm --- p.34Chapter 5.5 --- Optimization of the Threshold Value --- p.36Chapter 6 --- Automatic Centerline Determination Algorithm --- p.38Chapter 6.1 --- Distance Transformations --- p.38Chapter 6.2 --- End Points Retrieval --- p.41Chapter 6.3 --- Graph Based Centerline Algorithm --- p.44Chapter 7 --- Experiments and Discussion --- p.48Chapter 7.1 --- Experiment of Automated Determination of Bronchus Algorithm --- p.48Chapter 7.2 --- Experiment of Automatic Centerline Determination Algorithm --- p.54Chapter 8 --- Conclusion --- p.62Bibliography --- p.6
Automatic lineament analysis techniques for remotely sensed imagery
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ASKI: full-sky lensing map making algorithms
Within the context of upcoming full-sky lensing surveys, the edge-preserving
non- linear algorithm Aski is presented. Using the framework of Maximum A
Posteriori inversion, it aims at recovering the full-sky convergence map from
surveys with masks. It proceeds in two steps: CCD images of crowded galactic
fields are deblurred using automated edge-preserving deconvolution; once the
reduced shear is estimated, the convergence map is also inverted via an edge-
preserving method. For the deblurring, it is found that when the observed field
is crowded, this gain can be quite significant for realistic ground-based
surveys when both positivity and edge-preserving penalties are imposed during
the iterative deconvolution. For the convergence inversion, the quality of the
reconstruction is investigated on noisy maps derived from the horizon N-body
simulation, with and without Galactic cuts, and quantified using one-point
statistics, power spectra, cluster counts, peak patches and the skeleton. It is
found that the reconstruction is able to interpolate and extrapolate within the
Galactic cuts/non-uniform noise; its sharpness-preserving penalization avoids
strong biasing near the clusters of the map; it reconstructs well the shape of
the PDF as traced by its skewness and kurtosis; the geometry and topology of
the reconstructed map is close to the initial map as traced by the peak patch
distribution and the skeleton's differential length; the two-points statistics
of the recovered map is consistent with the corresponding smoothed version of
the initial map; the distribution of point sources is also consistent with the
corresponding smoothing, with a significant improvement when edge preserving
prior is applied. The contamination of B-modes when realistic Galactic cuts are
present is also investigated. Leakage mainly occurs on large scales.Comment: 24 pages, 21 figures accepted for publication to MNRAS
3D Reconstruction of vegetation from orthophotos
[CATALÀ] Mitjançant tècniques de Visió per Computador som capaços de detectar i classificar la flora present en una ortofoto. A partir d'aquesta informació, i d'un conjunt de models de plantes en 3D que generem, elaborem una reproducció 3D aproximada, però factible, de la vegetació present en l'ortofoto.[ANGLÈS] Using Computer Vision techniques, we are able to detect and classify the flora present in an orthphoto. With this information, and with a self-produced base set of 3D plants, we generate an approximated, but feasible, 3D reproduction of the vegetation that appears in the orthophoto
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