IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Refining edges detected by a LoG operator
Computer Vision, Graphics, and Image Processing
On Achievable Accuracy in Edge Localization
IEEE Transactions on Pattern Analysis and Machine Intelligence
A locally adaptive window for signal matching
International Journal of Computer Vision
Recognizing corners by fitting parametric models
International Journal of Computer Vision
Geometric invariance in computer vision
Geometric invariance in computer vision
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Lokalisierungseigenschaften direkter Ansätze zur Ermittlung von Grauwertecken
Mustererkennung 1993, Mustererkennung im Dienste der Gesundheit, 15. DAGM-Symposium
Über die Modellierung und Identifikation charakteristischer Grauwertverläufe in Realweltbildern
Mustererkennung 1990, 12. DAGM-Symposium,
Representing Edge Models via Local Principal Component Analysis
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Rotated Wedge Averaging Method for Junction Classification
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Behavior of the Laplacian of Gaussian Extrema
Journal of Mathematical Imaging and Vision
Performance evaluation of corner detectors using consistency and accuracy measures
Computer Vision and Image Understanding
Journal of Mathematical Imaging and Vision
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Performance evaluation of corner detectors using consistency and accuracy measures
Computer Vision and Image Understanding
Fundamental limits in 3d landmark localization
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
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Recently, in Rohr [13], we analyzed the systematiclocalization errors introduced by local operators for detectinggrey-value corners. These errors are inherently due to thedifferential structure of the operators and, in general, areenlarged by discretization and noise effects. Here, we take thestatistical point of view to analyze the localization errorscaused by noisy data. We consider a continuous image model thatrepresents the blur as well as noise introduced by an imagingsystem. In general, the systematic intensity variations arenonlinear functions of the location parameters. For this modelwe derive analytic results stating lower bounds for the locationuncertainty of image features. The lower bounds are evaluatedfor explicit edge and corner models. We show that the precisionof localization in general depends on the noise level, on thesize of the observation window, on the width of the intensitytransitions, as well as on other parameters describing thesystematic intensity variations. We also point out that theuncertainty lower bounds in localizing these image features canin principle be attained by fitting parametric models directlyto the image intensities. To give an impression of theachievable accuracy numerical examples are presented.