Image Rectification Using Affine Epipolar Geometric Constraint

Sui, Liansheng; Zhang, Jiulong; Du-wu, Cui

doi:10.1109/iscsct.2008.76

Cited by 9 publications

(8 citation statements)

References 6 publications

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“…A normalized stereo pair is characterized by both of the normalized images have a zero parallax in y-axis direction [9,15]. Hence, conjugate pixel locations on the normalized images have an equal row value (i.e.…”

Section: Results and Analysismentioning

confidence: 99%

Precise Wide Baseline Stereo Image Matching for Compact Digital Cameras

Tjahjadi¹,

Handoko²

2017

EECSI

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Numerous image matching methods for wide range of applications have been invented in the last decade. When high precision and reliability of the object space point coordinates is highly demanding, a stereo image matching method which can produce conjugate point of images and a standard deviation of the matched point is examined. In this approach, image gradients are used locally to seek a conjugate patch. The normalized cross correlation is first utilized to estimate an approximate location of the conjugate patch between two normalized images. Then the location of conjugate patch is further refined by using Gaussian-Newton least squares image matching. Both radiometric and geometric parameters of least squares models are used selectively in seeking the best possible accuracy. Iterative computation is conducted to incrementally refine the geometric location of the conjugate point. After a matched patch has been found, a variant-covariant matrix of the parameter is analyzed to inform the precision of the conjugate points both on images and object space. This method can compute high precision object space points and some examples demonstrate the insight of the approach.

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Section: Results and Analysismentioning

confidence: 99%

Precise Wide Baseline Stereo Image Matching for Compact Digital Cameras

Tjahjadi¹,

Handoko²

2017

EECSI

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“…The epipolar constraint is used to filter out matching points that are not on the epipolar line, and motion estimation is performed by the FBM [18], [19]. These methods prevent the moving object from being included in the background.…”

Section: A Related Workmentioning

confidence: 99%

“…The MAD is the mean absolute frame difference, which represents the accuracy of the global motion compensation. The MAD is defined as (19) where t background is a set of background pixels in the th t frame, and ) ( Fig. 9 shows the MADs from various target detection methods.…”

Section: ) Quantitative Evaluationmentioning

confidence: 99%

A Disparity-Based Adaptive Multihomography Method for Moving Target Detection Based on Global Motion Compensation

Kim

Yang

Park

2016

IEEE Trans. Circuits Syst. Video Technol.

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Moving target detection is an important technique in visual surveillance systems. If a camera is freely moving, it becomes more difficult to detect a moving target, especially in the environment of a wide-range background. To compensate for the global motion of a wide-range background, a disparity-based adaptive multi-homography method is proposed. The proposed method comprises four steps; feature point extraction, generation of adaptive multi-homography matrices using motion grouping, generation of background model and detection of background, and target detection. Experiment results show that the proposed method can robustly detect moving targets in sequences taken by a freely moving camera.

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“…But it required that all involving matrix must be positive in the rectify process, and would be disturbed easily by noise. Sui Liansheng et al [6] used projective transformation to make all corresponding epipolar lines parallel to each other, and then moved the corresponding epipolar lines to the corresponding horizontal lines by offset-value which was obtained from the optimization of minimizing the vertical difference. Although the positive of matrix was not required, the complex computation of the epipolar lines was inevitable yet.…”

Section: Introductionmentioning

confidence: 99%

Stereo rectification of calibrated image pairs based on geometric transformation

Su¹,

He²

2011

IJMECS

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The objective of stereo rectification is to make the corresponding epipolar lines of image pairs be parallel to the horizontal direction, so that the efficiency of stereo matching is improved as the corresponding points stay in the same horizontal lines of both images. In this paper，a simple and convenient rectification method of calibrated image pairs based on geometric transformation is proposed, which can avoid the complicated calculation of many previous algorithms such as based on epipolar lines, based on fundamental matrix or directly depend on corresponding points. This method is divided into two steps including coordinate system transformation and re-projection of image points. Firstly, we establish two virtual cameras with parallel optical axis by coordinate system transformation based on the pose relationship of the two cameras from calibration result. Secondly, we re-project the points of the original image onto new image planes of the virtual cameras through geometrical method, and then realized the stereo rectification. Experiments of real stereo image pairs show that the proposed method is able to realize the rectification of stereo image pairs accurately and efficiently.

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Image Rectification Using Affine Epipolar Geometric Constraint

Cited by 9 publications

References 6 publications

Precise Wide Baseline Stereo Image Matching for Compact Digital Cameras

Precise Wide Baseline Stereo Image Matching for Compact Digital Cameras

A Disparity-Based Adaptive Multihomography Method for Moving Target Detection Based on Global Motion Compensation

Stereo rectification of calibrated image pairs based on geometric transformation

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