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UNIVERSIDAD DE MURCIA LÍNEA DE INVESTIGACIÓN DE PERCEPCIÓN ARTIFICIAL Y RECONOCIMIENTO DE PATRONES - GRUPO DE COMPUTACIÓN CIENTÍFICA A CAMERA CALIBRATION.

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Presentation on theme: "UNIVERSIDAD DE MURCIA LÍNEA DE INVESTIGACIÓN DE PERCEPCIÓN ARTIFICIAL Y RECONOCIMIENTO DE PATRONES - GRUPO DE COMPUTACIÓN CIENTÍFICA A CAMERA CALIBRATION."— Presentation transcript:

1 UNIVERSIDAD DE MURCIA LÍNEA DE INVESTIGACIÓN DE PERCEPCIÓN ARTIFICIAL Y RECONOCIMIENTO DE PATRONES - GRUPO DE COMPUTACIÓN CIENTÍFICA A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos Dept. de Informática y Sistemas Universidad de Murcia - España

2 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT INTRODUCTION Camera calibration: estimation of the unknown values in a camera model. –Intrinsic parameters. –Extrinsic parameters. Calibration target: object of known geometry, easy to detect and locate, used in calibration.

3 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT INTRODUCTION The whole procedure of camera calibration [Heikkilä et al. 97]: –Determinate a camera model. –Control point location in the images. –Camera model fitting. –Image correction for distortion. –Estimate the errors of the previous stages.

4 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT INTRODUCTION Much research has been devoted to model fitting. Control point location: –Design physical target structure. –Design an algorithm for target detection and location. –Goals: accuracy, robustness, efficiency, simplicity.

5 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT TARGET DESIGN Previous work: square features. Typical methods use: –Edge, segment, corner detection. –Line intersections. –Contour following.

6 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT TARGET DESIGN Previous work: dot features. Point features (less than 5 pixels radius). Centroid calculation. Used in photogrametry.

7 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT TARGET DESIGN Circular features. Key ideas: –Circles (ellipses) are mapped to ellipses (using perspective projection). –Ellipses are the most simple shape to describe, detect and locate.

8 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT TARGET DESIGN Previous work based on centroid. Problem of perspective bias: ellipse centroid is not necessarily the projected centroid of the circle.

9 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT TARGET DETECTION/LOCATION Process for detection and location of the target. Main steps: –Detection and location of ellipses. –Extraction of invariant points. –Matching with known points of the target. Then model fitting (DLT) is applied.

10 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT TARGET DETECTION/LOCATION Ellipse detection and location: –Image binarization. Threshold: median value of partial histogram. –Connected component grouping. –Gaussian component description. For each region:, and number of points.

11 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT ELLIPSE DETECTION/LOCATION Binarization Connected compo- nent grouping Acquired image Gaussian description

12 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT ELLIPTICAL SHAPE TEST Gaussian parameters:,. Ellipse mayor and minor radius: a, b Ellipse area: S R = ab Radius from gaussian parameters:

13 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT TARGET DETECTION/LOCATION Ellipse location is insufficient: invariant points should be extracted. Feature points in a target of circles. –Ellipse centroid is not an invariant feature point. –Invariant feature points can be obtained using relations between coplanar circles.

14 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT TARGET DETECTION/LOCATION Tangent invariance: supposing perspective projection common tangent property remains invariant. Perspective projection

15 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT TARGET DETECTION/LOCATION Some conclusions dont held when radial distortion is considered. Dealing with distortion: –Iterative method: parameter calculation/image correction. –Independent estimation (and correction) of distortion.

16 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT EXPERIMENTAL RESULTS Tests are centered on the target detection/location procedure. –Accuracy: feature point location. –Robustness: defocusing and noise. –Efficiency: computation time. Acquisition: low-cost videoconference camera QuickCam Pro (Logitech). Computer: off-the-self PC, with K6 at 350Mhz.

17 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT EXPERIMENTAL RESULTS Target used in the experiments. 320x240 pixels 256 gray levels Manual measure to determine ground-truth positions.

18 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT EXPERIMENTAL RESULTS Location error vs. ellipse size in images

19 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT EXPERIMENTAL RESULTS Manual measure is insufficient. Accuracy of the method (using ideal images): 0.05 pixels mean, 0.03 pixels standard deviation. The target was detected in 97% of the images.

20 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT EXPERIMENTAL RESULTS Robustness to defocusing and noise. Location error vs gaussian smoothing Location error vs. random noise

21 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT EXPERIMENTAL RESULTS Efficiency: –The main process is a connected component labeling algorithm. –This requires a single scanning of the image, with a constant cost per pixel. –The whole process can be made at approx. 10 Hz.

22 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT CONCLUSIONS A technique for camera calibration is proposed based in the use of circles as target features. This contribution is centered in target detection/location. Process of detection and location: –Gaussian description of connected component. –Feature point calculation and matching.

23 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT CONCLUSIONS The method is simple and low- level, which implies efficiency and robustness. Subpixel accuracy is clearly reached. High robustness to noise and defocusing. The technique is suited for automated systems.

24 A CAMERA CALIBRATION TECHNIQUE USING TARGETS OF CIRCULAR FEATURES Ginés García Mateos SIARP2000 LISBOA SEPT LAST This work has been supported by CICYT project TIC Línea PARP web page: Muito obrigado


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