Block cosparsity overcomplete learning transform image segmentation algorithm based on burr model

Han, Ling; Li, Shujuan; Ren, Pengxin; Xue, Dingdan

doi:10.1049/iet-ipr.2019.1212

Cited by 1 publication

(1 citation statement)

References 25 publications

(19 reference statements)

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“…Combined with other burr models [28, 29], one new model of the burr is proposed [30, 31]:

b u r r = h_{b} \sqrt[n_{s}]{1 - {(||, \frac{r_{f} + b_{g} + r_{d}}{b_{r}})}^{n_{s}}}, r_{d} \in [0, b_{r}]

where

h_{b}

expresses the height of the burr,

r_{f}

describes the burr root radius, burr thickness is

b_{g}

b_{r}

represents the root thickness of the burr,

r_{d}

is the relative deviation, the shape exponent of the burr is

n_{s}

, the effective exit surface angle is u and v f represents feed direction of the workpiece (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

Image denoising based on BCOLTA: Dataset and study

Han

Liu

2021

IET Image Processing

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Robot deburring is an effective method for improving the surface quality of the high‐voltage copper contact. The first step of robot deburring is to acquire the burr images. We propose a new burr mathematical model and build a real burr image dataset for burr image denoising. In order to improve burr image denoising effects of the high‐voltage copper contact, this study proposes an online burr image denoising algorithm, that is, block cosparsity overcomplete learning transform algorithm (BCOLTA). The penalty term and the condition number are affected by the burr parameter. The clustering and transform alternate minimisation algorithms are adopted to achieve lower computational cost and better denoising effect. In addition, BCOLTA also has a good adaptibility to inherent noise images, especially in Gaussian noise. Compared with other traditional and deep learning algorithms by no reference and full reference image quality assessment methods, BCOLTA has state‐of‐the‐art denoising effects and computational complexity on dealing with burr images. This research will play an important role in the intelligent manufacturing field.

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“…Combined with other burr models [28, 29], one new model of the burr is proposed [30, 31]: