The boundaries of colonoscopy-acquired images are often blurred due to reflections and low contrast, and existing methods for colon polyp segmentation fail to effectively represent global contextual information and long-range dependencies, resulting in sub-optimal accuracy in segmenting polyp. To address this problem, we propose a novel approach which introduces uncertainty-guided cross-entropy loss into a Transformer model to achieve precise segmentation of colon polyps. Regarding the boundary blurred, we incorporate an uncertainty estimation module into the decoding process. This module assigns lower weights to pixels with higher boundary uncertainty so as to mitigate the influence of erroneous pixels, and a boundary attention module is employed between encoding and decoding to guide the network to capture polyp edges more effectively, thereby improving its ability for precise boundary localization. To enhance the contextual modeling capabilities of the model, we employ a Pyramid Vision Transformer v2 (PVTv2) encoder to extract semantic information and capture long-range dependencies in the lesion regions. Furthermore, we utilize a feature refinement module to capture local detailed information. Additionally, a low-level feature enhancement module is applied to highlight the region of interest (ROI) of polyps, thereby facilitating improved discrimination between normal tissues and polyps. Extensive experiments conducted on five publicly datasets demonstrate the superior accuracy and generalization performance of the proposed model. Furthermore, with minor refinements, this model can be extended to other tumor segmentation tasks.
Existed methods can't be used for recognizing simple polyhedron. In this paper, three problems are researched. First, a
method for recognizing triangle and quadrilateral is introduced based on geometry and angle constraint. Then Attribute
Relation Graph (ARG) is employed to describe simple polyhedron and line drawing. Last, a new method is presented to
recognize simple polyhedron from a line drawing. The method filters the candidate database before matching line
drawing and model, thus the recognition efficiency is improved greatly. We introduced the geometrical characteristics
and topological characteristics to describe each node of ARG, so the algorithm can not only recognize polyhedrons with
different shape but also distinguish between polyhedrons with the same shape but with different sizes and proportions.
Computer simulations demonstrate the effectiveness of the method preliminarily.
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