Characterizing the performance of automatic road detection using error propagation

Abstract A methodology is introduced to predict the performance of automatic road detection using image examples of typical road types. In contrast to previous work on road detection, the focus is on characterizing the detection performance to achieve reliable performance measures of the detection. It is studied how noise, like road markings, shadows, trees and buildings, influences the detection of road. This noise is modeled using second-order statistics and its effects are calculated using error propagation on the detection equations. The method predicts the performance in terms of detection rate and gives the optimal parameter set that is needed for this detection. Experiments have been conducted on a set of images of typical roads in very high-resolution satellite images.

[1]  Ralph B. D'Agostino,et al.  Goodness-of-Fit-Techniques , 2020 .

[2]  Qingfen Lin,et al.  Efficient Detection of Second-Degree Variations in 2D and 3D Images , 2001, J. Vis. Commun. Image Represent..

[3]  Bernard Chalmond,et al.  Contextual performance prediction for low-level image analysis algorithms , 2001, IEEE Trans. Image Process..

[4]  Leo Dorst,et al.  First order error propagation of the Procrustes method for 3D attitude estimation , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Kevin W. Bowyer,et al.  Empirical evaluation techniques in computer vision , 1998 .

[6]  Carsten Steger,et al.  An Unbiased Detector of Curvilinear Structures , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Robert M. Haralick.  Propagating Covariance in Computer Vision , 1996, Int. J. Pattern Recognit. Artif. Intell..

[8]  Sidharta Gautama,et al.  Detecting change in road networks using continuous relaxation labeling , 2003 .

[9]  C. Steger,et al.  AUTOMATIC ROAD EXTRACTION BASED ON MULTI-SCALE, GROUPING, AND CONTEXT , 1999 .

[10]  Jacques Blanc-Talon,et al.  Imaging and vision systems: theory, assessment and applications , 2001 .

[11]  W. Philips,et al.  Image-Based Change Detection of Geographic Information Using Spatial Constraints , 2004 .

[12]  Sudeep Sarkar,et al.  Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Tony Lindeberg.  Edge Detection and Ridge Detection with Automatic Scale Selection , 2004, International Journal of Computer Vision.

[14]  N. A. Thacker,et al.  Performance Characterisation in Computer Vision: The Role of Statistics in Testing and Design , 2003 .

[15]  Henrik I. Christensen,et al.  EditorialPerformance characteristics of vision algorithms , 1997, Machine Vision and Applications.

[16]  Peter Rockett,et al.  Performance assessment of feature detection algorithms: a methodology and case study on corner detectors , 2003, IEEE Trans. Image Process..

[17]  R. Haralick,et al.  The Topographic Primal Sketch , 1983 .

[18]  Sudeep Sarkar,et al.  A Framework for Performance Characterization of Intermediate-Level Grouping Modules , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Peter Meer,et al.  Performance Assessment Through Bootstrap , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Donald Geman,et al.  An Active Testing Model for Tracking Roads in Satellite Images , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Juan B. Mena,et al.  State of the art on automatic road extraction for GIS update: a novel classification , 2003, Pattern Recognit. Lett..