Automatic chessboard corner detection method

Liu, Yu; Liu, Shuping; Cao, Yang; Wang, Zengfu

doi:10.1049/iet-ipr.2015.0126

Cited by 22 publications

(16 citation statements)

References 18 publications

(22 reference statements)

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“…where λ 1 and λ 2 are the eigenvalues, and σ is a scaling parameter for tuning the elements A and B. At the pixel location (x, y), we have three conditions to determine whether a region is corner, edge, or no feature of interest [26], [28], [29]: (1) if both eigenvalues are large, then this location is at a corner;…”

Section: B Image Enhancement Using Convolution Operationsmentioning

confidence: 99%

“…In the present study, a Harris corner detector [26], [28], [29] is chosen for image enhancement using a 2D convolution process. The proposed corner detector features a 2 × 2 mask matrix and performs the local convolution operation, which uses the Gaussian function to enhance the original image by convolving in the horizontal and vertical directions and remove speckle noise and aliasing artifacts in the ROI.…”

Section: Introductionmentioning

confidence: 99%

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Breast Benign and Malignant Tumors Rapidly Screening by ARFI-VTI Elastography and Random Decision Forests Based Classifier

Chen

Lin

et al. 2020

IEEE Access

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Breast cancer is the most common cancer among women in Taiwan, and the number of breast cancer cases reported annually continues to increase. In 2018, breast cancer ranked fourth in terms of mortality. Early stages (stages 0-2) of malignant breast lesions can be diagnosed during regular screening, and early treatment via advanced medical therapies increases survival rates. Ultrasound imaging, including acoustic radiation force impulse (ARFI) imaging, is the first-line examination technique used to locate breast lesion tissue, which can then be quantitated by virtual touch tissue imaging (VTI). ARFI-VTI elastography is a breast imaging modality that creates two-dimensional (2D) images to visualize the texture details, elasticity, and morphological features of a region of interest (ROI). The 2D Harris corner convolution is applied during digital imaging to remove speckle noise and enhance the ARFI-VTI images for extrapolation of lesion tissue in a ROI. Then, 2D Harris corner convolution, maximum pooling, and random decision forests (RDF) are integrated into a machine vision classifier to screen subjects with benign or malignant tumors. A total of 320 ARFI-VTI images were collected for experiments. In training stages, 122 images were randomly selected to train the RDF-based classifiers and the remaining images were randomly selected for performance evaluation via cross-validation in recalling stages. In a 10-fold cross-validation, promising results with mean sensitivity, mean specificity, and mean accuracy of 86.02%, 87.63%, and 86.97%, respectively, are achieved for quantifying the performance of the proposed classifier. Breast tumors visualized on ARFI-VTI images can be used for rapid screening of malignant or benign lesions by using the proposed machine vision classifier. INDEX TERMS Acoustic radiation force impulse, virtual touch tissue imaging, elastography, harris corner convolution, random decision forest.

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Section: B Image Enhancement Using Convolution Operationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Breast Benign and Malignant Tumors Rapidly Screening by ARFI-VTI Elastography and Random Decision Forests Based Classifier

Chen

Lin

et al. 2020

IEEE Access

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“…Several corner points could be detected using the above method. 33 Figure 5 is characteristic points of the valve core detected by changing the corner detection threshold. The results show that two boundary points of the core contour could be detected at the top of the area of the image.…”

Section: Vibration Detection Of Core Based On Corner Detectionmentioning

confidence: 99%

Image recognition and diagnosis for vibration characteristics of cone valve core

Yan

Zhao

et al. 2020

Advances in Mechanical Engineering

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Research on vibration characteristics of hydraulic valves is helpful to improve the performance of the hydraulic valve. In this article, a visualization experimental method is designed to capture the cone valve core vibration, and the image sequence of the vibration is obtained. And the least-squares contour fitting of the valve core is proposed to analyze the vibration characteristics of the valve core, which could eliminate noise in the vibration signal and improve accuracy. The difference of the least-squares variance between adjacent data points obtained by least-squares contour fitting is one order of magnitude smaller than corner detection method, while the signal-to-noise ratio is more than twice the corner detection. Furthermore, the frequency spectrum and amplitude of the valve core vibration are also discussed. The deviation of the valve core from the coordinate origin will increase with increasing of the pressure difference and decreasing of the pre-compression of the spring and the amplitude of vibration increases too. The vibration signal of the valve core under different conditions has similar frequency spectrum, which is mainly composed of the vibration signal spectrum caused by the fluctuation of oil pressure and turbulence of the liquid system.

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“…Documents [19,20] detect the corner points of checkerboard patterns by quadrilateral join rules. In documents [21,22], the initial corner set is obtained by the improved Hessian corner detector, and the initial concentration of false points is eliminated by the intensity and geometrical features of the chessboard pattern. But the above two methods are mainly designed for the chessboard pattern used in camera calibration, which do not meet the requirement of calibration pattern used in scene calibration.…”

Section: Introductionmentioning

confidence: 99%

Ring Fusion of Fisheye Images Based on Corner Detection Algorithm for Around View Monitoring System of Intelligent Driving

Zhao

Gao

Zhang

et al. 2018

Journal of Robotics

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In order to improve the visual effect of the around view monitor (AVM), we propose a novel ring fusion method to reduce the brightness difference among fisheye images and achieve a smooth transition around stitching seam. Firstly, an integrated corner detection is proposed to automatically detect corner points for image registration. Then, we use equalization processing to reduce the brightness among images. And we match the color of images according to the ring fusion method. Finally, we use distance weight to blend images around stitching seam. Through this algorithm, we have made a Matlab toolbox for image blending. 100% of the required corner is accurately and fully automatically detected. The transition around the stitching seam is very smooth, with no obvious stitching trace.

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Automatic chessboard corner detection method

Cited by 22 publications

References 18 publications

Breast Benign and Malignant Tumors Rapidly Screening by ARFI-VTI Elastography and Random Decision Forests Based Classifier

Breast Benign and Malignant Tumors Rapidly Screening by ARFI-VTI Elastography and Random Decision Forests Based Classifier

Image recognition and diagnosis for vibration characteristics of cone valve core

Ring Fusion of Fisheye Images Based on Corner Detection Algorithm for Around View Monitoring System of Intelligent Driving

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