Rotation-invariant recognition of shapes is a common challenge in computer vision. Recent approaches have significantly improved the accuracy of rotation-invariant recognition by encoding the rotational invariance of shapes as hand-crafted image features and introducing deep neural networks. However, the methods based on pixels have too much redundant information, and the critical geometric information is prone to early leakage, resulting in weak rotation-invariant recognition of fine-grained shapes. In this paper, we reconsider the shape recognition problem from the perspective of contour points rather than pixels. We propose an anti-noise rotation-invariant convolution module based on contour geometric aware for fine-grained shape recognition. The module divides the shape contour into multiple local geometric regions (LGA), where we implement finer-grained rotation-invariant coding in terms of point topological relations. We provide a deep network composed of five such cascaded modules for classification and retrieval experiments. The results show that our method exhibits excellent performance in rotation-invariant recognition of fine-grained shapes. In addition, we demonstrate that our method is robust to contour noise and the rotation centers.

With the continuous advancement of minimally invasive surgery, the incisions have become increasingly smaller, leading to a proportional convergence between the physical dimensions of surgical instruments and the operational space. This phenomenon has exacerbated the issue of visual obstruction caused by the overlapping of the instruments, which now stands as a significant technical impediment to the enhancement of precision in minimally invasive procedures. Vanilla approaches rely on deep learning-based image inpainting techniques to address this issue, but their results are unreliable for surgeons making decisions. This work rethinks hardware design, sacrificing resolution to increase the view of cameras, effectively filling the instrument occluded areas through multi-view technology. Subsequently, a super-resolution method is employed to restore the details of the inpainted images. This innovative approach transforms the uncertainty of deep learning from a large-range pixel inference problem into a more controllable pixel interpolation task, thereby significantly enhancing the reliability and accuracy of the repaired results. Taking spinal endoscopic surgery as an application scenario, we designed a miniature multi-view endoscope system tailored to the specific needs of this surgery. Phantom experiments are conducted to verify the feasibility of the proposed instrument transparency method. The results demonstrate the potential of the proposed method for improving minimally invasive surgery.