Geometric and Statistic Constraints in Dynamic Re-Orientation of On-Board Camera: Inherent Vanishing Points
ABSTRACT This paper investigates the stochastic projective properties of natural line segments in highway scenes for the purpose of dynamically re-orientating the on-board camera. We demonstrate there are geometric and statistic constraints on the projection of such line segments. A new concept of Inherent Vanishing Points associated with land vehicle boards was employed to relate to the observed line segments and rotation matrix. We show how these constraints greatly simplify the re-orientation of the on-board camera. We validate a dynamic re-orientation algorithm based on these constraints with a real image sequence. Compared with the techniques based on static calibration fields, the method based on our proposed statistical constraints is effective, stable, and easy to use.
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ABSTRACT: A novel approach to camera calibration by vanishing lines is proposed. Calibrated parameters include the orientation, position, and focal length of a camera. A hexagon is used as the calibration target to generate a vanishing line of the ground plane from its projected image. It is shown that the vanishing line includes useful geometric hints about the camera orientation parameters and the focal length, from which the orientation parameters can be solved easily and analytically. And the camera position parameters can be calibrated by the use of related geometric projective relationships. The simplicity of the target eliminates the complexity of the environment setup and simplifies the feature extraction in relevant image processing. The calibration formulas are also simple to compute. Experimental results show the feasibility of the proposed approachIEEE Transactions on Pattern Analysis and Machine Intelligence 05/1991; 13(4):370-376. · 4.80 Impact Factor
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ABSTRACT: We propose a method to locate three vanishing points on an image, corresponding to three orthogonal directions of the scene. This method is based on two cascaded Hough transforms. We show that, even in the case of synthetic images of high quality, a naive approach may fail, essentially because of the errors due to the limitation of the image size. We take into account these errors as well as errors due to detection inaccuracy of the image segments, and provide a method efficient, even in the case of real complex scenesIEEE Transactions on Pattern Analysis and Machine Intelligence 05/1994; · 4.80 Impact Factor
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ABSTRACT: This paper presents a new method for the detection of vanishing points based on sub-pixel line descriptions which recognizes the existence of errors in feature detection and which does not rely on supervision or the arbitrary specification of thresholds. Image processing and image analysis are integrated into a coherent scheme which extracts straight line structure from images, develops a measure of line quality for each line, estimates the number of vanishing points and their approximate orientations, and then computes optimal vanishing point estimates through combined clustering and numerical optimization. Both qualitative and quantitative evaluation of the algorithms performance is included in the presentationIEEE Transactions on Pattern Analysis and Machine Intelligence 12/1995; · 4.80 Impact Factor