The internal parameter calibration based on sub-pixel threshold segment algorithm

Guo, Chao; Yang, Ruifeng; Zhao, Luobin

doi:10.3233/jcm-170726

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…In the RGB space, because the background of apple images are complex, and the conventional segmentation algorithm with stationary threshold is not ideal, such as segmentation algorithm based on threshold or improved threshold [32], [33], K-means clustering [34]- [36], Otsu method [37], [38], etc.. This study attempts to construct an adaptive threshold segmentation method based on the graph segmentation algorithm [39]- [43], achieve efficient and adaptive segmentation through predicate constraints.…”

Section: Target Region Fitting Based On Optimized Graphmentioning

confidence: 99%

Fast Recognition and Location of Target Fruit Based on Depth Information

Duan

Luo

et al. 2019

IEEE Access

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In order to realize the recognition and localization of the target fruit more accurately and efficiently, an optimized graph-based recognition algorithm based on image depth information is proposed. Firstly, the gradient information of the depth image is obtained from the acquired depth apple image, the gradient vector is reflected from the 3D space to the 2D space, and then rotated 90 degrees clockwise, and the regular region forms the center of the vortex, which is the target fruit center to determine target fruit location. In the RGB space, the graph-based segmentation algorithm is introduced. By constructing the adaptive threshold, the mapping relationship between the number of centers and the parameters of k and min is established to find the target fruit region. Finally, scan the maximum radius of the super pixel area where the center of the circle is located, and fit the target fruit to realize the recognition and localization of the apple. The experimental results show that the target recognition rate is 96.61%, and the recognition time has been reduced significantly.

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Section: Target Region Fitting Based On Optimized Graphmentioning

confidence: 99%

Fast Recognition and Location of Target Fruit Based on Depth Information

Duan

Luo

et al. 2019

IEEE Access

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Weld pool image procession based on the Fourier-DNA low-pass filtering

Ding¹

2021

JCM

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In order to extract the character of weld pool, a new method for weld pool image procession based on the Fourier-DNA low-pass filtering is proposed. Firstly, the TIG welding technique is chosen to be the researched object. The welding experimental system is setup, which is used to capture weld pool images in real time. On this basis, Fourier transformation is performed to weld pool image, and changed the space domain to frequency domain. Then the DNA low-pass filtering is constructed. The DNA intelligent algorithm is used to obtain the optimum low-pass cut-off frequency, and the low-pass filtering can be obtained. The binaryzation operation by the optimum low-pass filtering method and the traditional otsu method have been done. The results show that the method proposed in the paper has a better binaryzation performance. In the end, canny edge extract operation are performed. So the weld pool image edge can be obtained, which has made some basis for the extraction of weld pool character latter on.

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The internal parameter calibration based on sub-pixel threshold segment algorithm

Cited by 2 publications

References 7 publications

Fast Recognition and Location of Target Fruit Based on Depth Information

Fast Recognition and Location of Target Fruit Based on Depth Information

Weld pool image procession based on the Fourier-DNA low-pass filtering

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