A fast and flexible algorithm for computing watersheds in digital gray-scale images is introduced. A review of watersheds and related motion is first presented, and the major methods to determine watersheds are discussed. The algorithm is based on an immersion process analogy, in which the flooding of the water in the picture is efficiently simulated using of queue of pixel. It is described in detail provided in a pseudo C language. The accuracy of this algorithm is proven to be superior to that of the existing implementations, and it is shown that its adaptation to any kind of digital grid and its generalization to n-dimensional images (and even to graphs) are straightforward. The algorithm is reported to be faster than any other watershed algorithm. Applications of this algorithm with regard to picture segmentation are presented for magnetic resonance (MR) imagery and for digital elevation models. An example of 3-D watershed is also provided. >

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Watersheds in digital spaces: an efficient algorithm based on immersion simulations

The Hausdorff distance measures the extent to which each point of a model set lies near some point of an image set and vice versa. Thus, this distance can be used to determine the degree of resemblance between two objects that are superimposed on one another. Efficient algorithms for computing the Hausdorff distance between all possible relative positions of a binary image and a model are presented. The focus is primarily on the case in which the model is only allowed to translate with respect to the image. The techniques are extended to rigid motion. The Hausdorff distance computation differs from many other shape comparison methods in that no correspondence between the model and the image is derived. The method is quite tolerant of small position errors such as those that occur with edge detectors and other feature extraction methods. It is shown that the method extends naturally to the problem of comparing a portion of a model against an image. >

Comparing images using the Hausdorff distance

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

/pdf/computer-vision-algorithms-and-applications-25dn6wu83j.pdf

Computer Vision: Algorithms and Applications

Foundations of Confocal Scanned Imaging in Light Microscopy -- Fundamental Limits in Confocal Microscopy -- Special Optical Elements -- Points, Pixels, and Gray Levels: Digitizing Image Data -- Laser Sources for Confocal Microscopy -- Non-Laser Light Sources for Three-Dimensional Microscopy -- Objective Lenses for Confocal Microscopy -- The Contrast Formation in Optical Microscopy -- The Intermediate Optical System of Laser-Scanning Confocal Microscopes -- Disk-Scanning Confocal Microscopy -- Measuring the Real Point Spread Function of High Numerical Aperture Microscope Objective Lenses -- Photon Detectors for Confocal Microscopy -- Structured Illumination Methods -- Visualization Systems for Multi-Dimensional Microscopy Images -- Automated Three-Dimensional Image Analysis Methods for Confocal Microscopy -- Fluorophores for Confocal Microscopy: Photophysics and Photochemistry -- Practical Considerations in the Selection and Application of Fluorescent Probes -- Guiding Principles of Specimen Preservation for Confocal Fluorescence Microscopy -- Confocal Microscopy of Living Cells -- Aberrations in Confocal and Multi-Photon Fluorescence Microscopy Induced by Refractive Index Mismatch -- Interaction of Light with Botanical Specimens -- Signal-to-Noise Ratio in Confocal Microscopes -- Comparison of Widefield/Deconvolution and Confocal Microscopy for Three-Dimensional Imaging -- Blind Deconvolution -- Image Enhancement by Deconvolution -- Fiber-Optics in Scanning Optical Microscopy -- Fluorescence Lifetime Imaging in Scanning Microscopy -- Multi-Photon Molecular Excitation in Laser-Scanning Microscopy -- Multifocal Multi-Photon Microscopy -- 4Pi Microscopy -- Nanoscale Resolution with Focused Light: Stimulated Emission Depletion and Other Reversible Saturable Optical Fluorescence Transitions Microscopy Concepts -- Mass Storage, Display, and Hard Copy -- Coherent Anti-Stokes Raman Scattering Microscopy -- Related Methods for Three-Dimensional Imaging -- Tutorial on Practical Confocal Microscopy and Use of the Confocal Test Specimen -- Practical Confocal Microscopy -- Selective Plane Illumination Microscopy -- Cell Damage During Multi-Photon Microscopy -- Photobleaching -- Nonlinear (Harmonic Generation) Optical Microscopy -- Imaging Brain Slices -- Fluorescent Ion Measurement -- Confocal and Multi-Photon Imaging of Living Embryos -- Imaging Plant Cells -- Practical Fluorescence Resonance Energy Transfer or Molecular Nanobioscopy of Living Cells -- Automated Confocal Imaging and High-Content Screening for Cytomics -- Automated Interpretation of Subcellular Location Patterns from Three-Dimensional Confocal Microscopy -- Display and Presentation Software -- When Light Microscope Resolution Is Not Enough:Correlational Light Microscopy and Electron Microscopy -- Databases for Two- and Three-Dimensional Microscopical Images in Biology -- Confocal Microscopy of Biofilms — Spatiotemporal Approaches -- Bibliography of Confocal Microscopy.

Handbook of biological confocal microscopy

Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science (2nd edition)

Evaluation of methods for shaded surface display of CT volumes.

http://liu.diva-portal.org/smash/get/diva2:241538/FULLTEXT01.pdf

Efficient Detection of Second-Degree Variations in 2D and 3D Images

This paper reports an ongoing feasibility study in industrial X-ray tomography. Full volume scanning of logs may have a large potential in terms of increased sales value. On the other hand, there is no technology commercially available today which can meet the requirements for speed. In conventional medical 3D-tomography, the source-detector system rotates around a slowly translating patient. We do not believe such an arrangement is able to reach our speed requirements. Instead, we have been simulating an arrangement with two fixed source-detector systems rotated 90° relative to each other. Each system consists of an X-ray source and a 2D detector. The logs are to be translated through this arrangement at relatively high speed (2–3 m/s) lengthwise on a conveyor belt, while cone-beam projections are acquired by each of the source-detector systems. The 2D-detectors are composed of a number of 1D-detectors side by side. For cost reasons we will try to limit the number of rows to nine, forming a 2D-array of 256×9 detector elements. Due to the unconventional scanning geometry, we have developed a new reconstruction algorithm, which also takes special care to avoid many artifacts due to sparse and/or missing data. Experiments (still simulated), performed on voxelized CT data, indicate that the knots are reconstructed with sufficient accuracy to allow for quantitative optimization. Due to the missing data, however, heartwood can barely be distinguished from sapwood, but his fact does not seem to compromise the subsequent knot detection. Note that this paper only describes the reconstruction part of the simulated experiments. To be complete, the feasibility study should also include segmentation, which is outside the scope of this paper.

Scanning of logs with linear cone-beam tomography

This paper addresses the problem on how to evaluate various operators used for estimation of derivatives in images. Such operators are extremely commonly used, for instance to detect edges. For bandlimited correctly sampled signals ideal derivative operators are easy to define. For 2D signals the first derivative operators take the form of a rotation invariant pair. Rotation invariance is also a natural requirement for the non-ideal practically implementable operators of which the Sobel operator is one example. Second degree derivators do not form a set of rotation invariant operators by itself. Instead, certain linear combinations of these derivators display this property. The Fourier domain is used extensively in the analysis, partly because the angular variation in the form of circular harmonics is preserved over the Fourier transform. Circular harmonics expansion is used for evaluation of practically implementable operator kernels. A quantity called total harmonic distortion (THD) is defined to capture the overall deviation from rotation invariance. In a comparison with two other simple kernels it seems that the Sobel operator does fairly well. Errors in magnitude and orientation estimation follow the THD-values quite closely. For all pairs of operators (convolution kernels), which are to be employed for orientation estimation, the paper presents a method to evaluate their quality. Besides, the paper brings about new insights in the interconnected concepts of edge detectors, line detectors, derivative operators, rotation invariance, and rotational symmetry.

Rotation invariance in gradient and higher order derivative detectors

Recently, several computed tomography (CT) machines with multirow detectors have been introduced on the market. Although the projections are obtained from a cone beam rather than a fan beam, multis ...

Per-Erik Danielsson

Papers

Evaluation of methods for shaded surface display of CT volumes.

Efficient Detection of Second-Degree Variations in 2D and 3D Images

Scanning of logs with linear cone-beam tomography

Rotation invariance in gradient and higher order derivative detectors

Helical cone-beam tomography