Thomas K. Peucker

Bio: Thomas K. Peucker is an academic researcher from Simon Fraser University. The author has contributed to research in topics: Triangulated irregular network & Logistic regression. The author has an hindex of 8, co-authored 12 publications receiving 4396 citations.

Algorithms for the reduction of the number of points required to represent a digitized line or its caricature

Abstract: All digitizing methods, as a general rule, record lines with far more data than is necessary for accurate graphic reproduction or for computer analysis. Two algorithms to reduce the number of points required to represent the line and, if desired, produce caricatures, are presented and compared with the most promising methods so far suggested. Line reduction will form a major part of automated generalization. Regle generale, les methodes numeriques enregistrent des lignes avec beaucoup plus de donnees qu'il n'est necessaire a la reproduction graphique precise ou a la recherche par ordinateur. L'auteur presente deux algorithmes pour reduire le nombre de points necessaires pour representer la ligne et produire des caricatures si desire, et les compare aux methodes les plus prometteuses suggerees jusqu'ici. La reduction de la ligne constituera une partie importante de la generalisation automatique.

Detection of Surface-Specific Points by Local Parallel Processing of Discrete Terrain Elevation Data

Abstract: This paper describes several methods designed to detect pits, peaks, passes, ridges, ravines, and breaks, given an array of sampled, quantized terrain elevations. The methods all use local analysis of the data; they are “parallel” in the sense that the results at each point do not depend on the results already obtained at other points.

The triangulated irregular network

Abstract: Our first motive for this research stems from problems with traditional terrain representations which were recognized as early as 1967 by Boehm. Topographic surfaces are non-stationary (Pike and R-ozema, 1975), i.e., the roughness of the terrain is not periodic but changes from one landtype to another. A regular grid therefore has to be adjusted to the roughest terrain in the mod'el and be highly redundant in smooth terrain. It is apparent that, if one is to model these non-stationary surfaces accurately and efficiently, one must use a method which adapts to this variation. The second principal motivation came from a realization

Digital Representation of Three-Dimensional Surfaces by Triangulated Irregular Networks (TIN). REVISED.

Abstract: : A Geographic Data Structure is developed which permits the digital acquisition, storage and application of data sets of three or more dimensions, in particular topographic terrains. The Structure consists of a Triangulated Irregular Network and higher-level structures. Comparisons with systems based on regular grids reveal that the Geographic Data Structure is advantageous in terms of storage space needed, computer time, and accuracy, in the acquisition, storage and application of digital terrain.

Statistical and structural approaches to texture

Abstract: In this survey we review the image processing literature on the various approaches and models investigators have used for texture. These include statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models. We discuss and generalize some structural approaches to texture based on more complex primitives than gray tone. We conclude with some structural-statistical generalizations which apply the statistical techniques to the structural primitives.

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

Abstract: 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. >

Computer Vision: Algorithms and Applications

Abstract: 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.

On the validation of models

Abstract: Traditional methods of evaluating geographic models by statistical comparisons between observed and simulated variates are criticized. In particular, it is suggested that the correlation coefficien...

Digital terrain modelling: A review of hydrological, geomorphological, and biological applications

Abstract: The topography of a catchment has a major impact on the hydrological, geomorphological. and biological processes active in the landscape. The spatial distribution of topographic attributes can often be used as an indirect measure of the spatial variability of these processes and allows them to be mapped using relatively simple techniques. Many geographic information systems are being developed that store topographic information as the primary data for analysing water resource and biological problems. Furthermore, topography can be used to develop more physically realistic structures for hydrologic and water quality models that directly account for the impact of topography on the hydrology. Digital elevation models are the primary data used in the analysis of catchment topography. We describe elevation data sources, digital elevation model structures, and the analysis of digital elevation data for hydrological, geomorphological, and biological applications. Some hydrologic models that make use of digital representations of topography are also considered.