Due to the effects of quantization error and image noise, detecting ‘meaningful’ line segments from an image with high continuity is a challenging task. To pursue this goal, a novel line segment detector, called thecontrast-guided line segment detector(CGLSD), is proposed in this paper. Our basic idea is to integrate a low-level image attribute, i.e.,edge contrast, into the line segment detection process for improving line continuity. After applying an edge detector to the input image, the edge contrast is exploited to guide the growth of aline-support regionfor each line segment individually. This is achieved by evaluating edge pixels as well as those non-edge pixels that are nearby the edges. As a result, some of the non-edge pixels are re-considered as ‘edge’ pixels and included for establishing the support region. Reversely, certain edge pixels might be treated as ‘non-edge’ pixels instead and excluded from the region. Since each support region is supposed to yield onlyoneline segment, each formed support region needs to have arefinementby removing those edge pixels that do not belong to it. Lastly, the support region is required to pass through avalidationcheck that might lead to a complete discard of the line segment due to its low confidence. Extensive experiments are conducted and compared with multiple state-of-the-arts on two datasets, including the one from us with manually-annotated ground truth. The results have shown that the proposed CGLSD can deliver superior performance in nearly all test cases.

Deep learning is increasingly prevalent in the bearing fault diagnosis, while the deficiency of fault samples could diminish the diagnostic efficacy of data-driven models that depend on extensive training data. For that, a novel multiscale physics-informed network (MPINet) is proposed for bearing fault diagnosis with small samples. Our fundamental idea is incorporating physical knowledge into the training process for enabling the model could better learn the fault features. To pursue this goal, a physics-informed block (PIB) is developed to extract fault features, which is customized for each failure mode. By this process, multiple independently trained PIBs encode the physical knowledge of their corresponding failure mode into the model, and thus yield multiscale fault features. Finally, the diagnosis result is obtained by using a new classifier head to merge these multiscale features. Extensive experimental results show that our MPINet can obtain superior diagnosis performance with small samples.

Detecting ellipses poses a challenging low-level task indispensable to many image analysis applications. Existing ellipse detection methods commonly encounter two fundamental issues. First, inferior detection accuracy could be incurred on a small ellipse than that on a large one; this introduces the scale issue. Second, inferior detection accuracy could be yielded along the minor axis than along the major one of the same ellipse; this leads to the anisotropy issue. To address these issues simultaneously, a novel anisotropic scale-invariant (ASI) ellipse detection methodology is proposed. Our basic idea is to perform ellipse detection in a transformed image space referred to as the ellipse normalization (EN) space, in which the desired ellipse from the original image is 'normalized' to the unit circle. With the establishment of the EN-space, an analytical ellipse fitting scheme and a set of distance measures are developed. Theoretical justifications are then derived to prove that both our ellipse fitting scheme and distance measures are invariant to anisotropic scaling, and thus each ellipse can be detected with the same accuracy regardless of its size and ellipticity. By incorporating these components into two recent state-of-the-art algorithms, two ASI ellipse detectors are finally developed and exploited to verify the effectiveness of our proposed methodology.