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OSSS-EA’06 Conference

OSSS-EA’06 Conference. Representor Zhu-lin LI. 3. 3. 1. 3. Report Content. Introduction of Myself. About SCILAB. 2. Introduction of the Paper. Introduction of Myself. My name is ZhulinLI, I come from Shaanxi province, I am a senior lecturer of Yan ’ an University. .

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OSSS-EA’06 Conference

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  1. OSSS-EA’06 Conference Representor Zhu-lin LI

  2. 3 3 1 3 Report Content Introduction of Myself About SCILAB 2 Introduction of the Paper

  3. Introduction of Myself My name is ZhulinLI, I come from Shaanxi province, I am a senior lecturer of Yan’an University. I am a graduate of the Second Artillery Engineering College, to study Image Processing and Stereo Vision.

  4. About SCILAB • We learn Scilab by Scilab website. • SIP is the image processing and computer vision package for SciLab, SIP reads/writes images in formats like JPEG, PNG, GIF, FITS, TIFF and BMP. It does filtering, histogram, thresholding, segmentation, edge detection, morphology, and shape analysis,color image preocessing, curvature, fractal dimension, distance transforms, multiscale skeletons, and more. SIP is meant to be a complete, useful, and FREE digital image processing toolbox for Scilab.

  5. Introduction of the Paper SUBJECT A Stereo Matching Method for Image Uncalibrated Camera Parameter ABSTRACT In this paper, a stereo matching implementation method for image uncalibrated camera based on Scilab is proposed. The master idea is to find correspondences of feature points between left image and right image, which are measured by gradient invariance under some constraints and Mahalanobis distance, and false matched points are rejected by topology property. The results show the method expands its application and improves matching accuracy, right ratio is 83.1%.

  6. Experiment Results and Analysis • Content of the Paper Invariant Feature Detection Similarity Metric Rejecting False Matched Point

  7. s [2/3～3/2], the interest points have invariant character for affine transformation. (Mikolajczyk K, Tuytelaars T, Schmid C, et. A Comparison of Affine Region Detectors, IJCV, 2005). SUSAN detector Feature Points Gradient of image can be adapted toillumination changes when photometric changes ∆ L ∈ [-15～+39] Invariant Detection Geometric Invariant Detection Photometric Invariant Detection Gradient of Feature Point

  8. IC Gradient Correlative data of intensity changeand gradient

  9. A different intensity image is selected, its gradient can be adapted to illumination changes when photometric changes [-26～+58]. The result show the gradient is invariant under other photometric changes interval.

  10. Similarity Metric Algorithm Mahalanobis distance similarity metric algorithm of searching correspondences is follow. Step1. Compute the gradient amplitudes and directions of points for two-view images. if the difference of gradient amplitude and directions less than threshold1 and threshold2 respectively , then go to step2. Otherwise, select other point, go to step1. Step2. Compute Mahalanobis distance d of pair-point, if d<m (given threshold), then the pair-point is considered a pair of corresponding points. Step3. Select other point , go to step1.

  11. Rejecting Falsely Matched Points The triple interest points of the left image are not in line, their correspondences are of the right image. Their topological relation is (5) if lie on the left side of the directed line , then , Otherwise, . The same side equation is (6) In initial matched points, if points are not satisfied with the function(6) , then reject these points. We compute the Fundamental matrix F using 8 points algorithm, then detect the points not satisfied with epipolar geometry, that is formular(7), to get robust correspondences. （7） Where u and u’ are correspondences of two-view images.

  12. Experiment Environment • SCILAB 4.0 • Pentium(R)4,2.53GHz • 350×250 two-view images

  13. Input images Points extracted Similaritymetric Rejecting Falsely Matched Output result Experiment results Matching flow

  14. Capability Analysis we have done initial matching, rejected majority false matched points using topology property. Results see this table.

  15. SUSAN interest points and gradient of these points are satisfied with geometric and photometric constraints, this allows to reject falsely matched correspondences at an early matching stage. • Matching accuracy is more robust because of rejecting false correspondences by using topological relation of triple points not in line. • Experiment results based on Scilab workshop is good. Conclusions

  16. Thanks

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