Systems for data processing, anaylsis and representation

Allam, Mosaad; Plunkett, Gordon

Bibliographic data

Monograph

Persistent identifier:: 1067490280

Title:: Systems for data processing, anaylsis and representation

Sub title:: ISPRS Commission II Symposium : June 6 - 10, Ottawa, Canada

Scope:: 1 Online-Ressource (XX, 530 Seiten)

Year of publication:: 1994

Place of publication:: Ottawa

Publisher of the original:: The Surveys, Mapping and Remote Sensing, Natural Resources Canada

Identifier (digital):: 1067490280

Illustration:: Illustrationen

Signature of the source:: ZS 312(30,2)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist aus dem Copyrightjahr ermittelt.

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Allam, Mosaad; Plunkett, Gordon

Corporations:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Adapter:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Founder of work:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Other corporate:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: [Wednesday, June 8, 1994]

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: [Session F-1 WG II/1 - Real-Time Mapping Technologies - Automatic Orientation of Sensors]

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: THE EXTERIOR ORIENTATION OF DIGITAL IMAGES BY ROAD MATCHING FAYEZ SHAHIN & KURT NOVAK

Document type:: Monograph

Structure type:: Chapter

points. The splines considered here are the well-known B- splines [Barnhill and Riesen- feld 1974]. Piecewise cubic polynomials in parametric form C;(t) = (z(t), y(2), z(t)) de- fine a curve as a function of the parameter t such that Ci(t) = 3 ViBi() 1=0 where V; = (=;,¥;,2;) are the coefficients of the polynomial and B;(t) are the basis functions. For the curve to appear contin- iously smooth, it must have positional, first derivative, and second derivative continuity at the break points, also known as knots. The coefficients of the piecewise polynomi- als (B-splines) physically define the vertices of a polygon that guides the splines to trace a smooth curve (guiding polygon). These ver- tices are also called control points (see Fig. 1). The control points of B-splines are invari- ant under affine, and projective transforma- tions. In addition, the errors incurred for a linear feature when assuming the invariance property of the B-spline under a perspective transformation are small, particularly in the case of small scale images [Cohen and Wang, 1994]. Cubic polynomials are most frequently used for splines since they are the lowest order in which curvature can change sign. Using the knot points P;, which can be derived using the critical point detection algorithm mentioned earlier, the piecewise polynomial coefficients, which are also the polygon vertices V;, are ob- tained by solving a set of simultaneous equa- tions Vir | 2W | Va 3 6 + ran isism. Since there are two fewer equations than un- knowns, two conditions are added for the open curve case to ensure that the curvature is zero at both ends of the curve. By adding these conditions, the number of equations is equal to the number of unknowns, and the equations can be solved. The iterative algorithm of Yamaguchi [Yam- aguchi 1988] is used to compute the vertices of the guiding polygon. This algorithm is based on the idea that the set of equations satisfy the convergence condition of the Gauss-Seidel method, and that there is a special relation- ship among the unknowns (the coefficient ma- irix is circulant). 3. THE IMAGE MODULE The goal of this module is to extract road seg- ments automatically from digital images and to represent these segments by cubic B-splines to serve as a model of the roads in image space. Usually a road segment in an image contrasts sufficiently with its background, has a uniform width, and has sufficient length. The Duda road operator is employed to ex- tract pixels that most likely belong to road segments. This low level step is followed by thinning and road tracking to group con- nected pixels as road segments. Finally, seg- ments that belong together are grouped, and the gaps between them are filled meaningfully. 3.1 The Duda Road Operator The Duda road operator (DRO) is used to make explicit the locations which are assigned the highest likelihood of road presence in the image. DRO masks are applied in four prin- cipal directions: horizontal, vertical, right di- agonal, and left diagonal. For more details about DRO the reader is referred to [Duda 1973]. Once the image is convolved by the four masks, every nonborder pixel has four evalua- tion scores associated with it. The maximum score for every pixel is retained, and the final scores are thresholded to keep only the best, which are then interpreted as producing pos- itive responses. This procedure is followed by thinning and road tracking. The road track- 176 ing aims ments. Fi as a list of is used to 3.2 Segm The roads tions are I of noise, p there 1s a ments tha "between t] ment inte, main feati pairs of se; ment / is € ment gq, if other, as g nearest en is that the (relative t not deviat segment q van 1988]. segment a1 gether as « the gaps I filled by th 4. TH This modu between tl ages and t object spa object spa ric cubic I property c tive transf of these sj matching ; match is c ods. 4.1 Primi The coeffic

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