Pub Date : 2024-09-16DOI: 10.1109/LSP.2024.3461654
Zhuoran Zheng;Chen Wu;Yeying Jin;Xiuyi Jia
Recently, large models (Segment Anything model) came on the scene to provide a new baseline for polyp segmentation tasks. This demonstrates that large models with a sufficient image level prior can achieve promising performance on a given task. In this paper, we unfold a new perspective on polyp segmentation modeling by leveraging the Depth Anything Model (DAM) to provide depth prior to polyp segmentation models. Specifically, the input polyp image is first passed through a frozen DAM to generate a depth map. The depth map and the input polyp images are then concatenated and fed into a convolutional neural network with multiscale to generate segmented images. Extensive experimental results demonstrate the effectiveness of our method, and in addition, we observe that our method still performs well on images of polyps with noise.
最近,大型模型(Segment Anything model)的出现为息肉分割任务提供了新的基准。这表明,具有足够图像级先验的大型模型可以在给定任务中取得可喜的性能。在本文中,我们利用深度任意模型(DAM)为息肉分割模型提供深度先验,从而为息肉分割建模提供了一个新的视角。具体来说,输入的息肉图像首先通过冻结的 DAM 生成深度图。然后将深度图和输入的息肉图像连接起来,并输入具有多尺度的卷积神经网络,生成分割图像。广泛的实验结果证明了我们方法的有效性,此外,我们还观察到我们的方法在有噪声的息肉图像上仍然表现良好。
{"title":"Polyp-DAM: Polyp Segmentation via Depth Anything Model","authors":"Zhuoran Zheng;Chen Wu;Yeying Jin;Xiuyi Jia","doi":"10.1109/LSP.2024.3461654","DOIUrl":"https://doi.org/10.1109/LSP.2024.3461654","url":null,"abstract":"Recently, large models (Segment Anything model) came on the scene to provide a new baseline for polyp segmentation tasks. This demonstrates that large models with a sufficient image level prior can achieve promising performance on a given task. In this paper, we unfold a new perspective on polyp segmentation modeling by leveraging the Depth Anything Model (DAM) to provide depth prior to polyp segmentation models. Specifically, the input polyp image is first passed through a frozen DAM to generate a depth map. The depth map and the input polyp images are then concatenated and fed into a convolutional neural network with multiscale to generate segmented images. Extensive experimental results demonstrate the effectiveness of our method, and in addition, we observe that our method still performs well on images of polyps with noise.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Radar signal deinterleaving is an essential step in perceiving the battlefield situation and mastering military initiative in the information battlefield. Complex radar systems are rapidly updated and iterated, which exacerbates the possibility of “increasing batch” and “mistaken batch” during radar signal deinterleaving. In this letter, a novel method based on complex networks and Laplacian graph clustering is proposed to improve the accuracy of deinterleaving. First, a complex network is constructed to mine the spatial correlation relationships of the same radar signals. Then, based on the graph characteristics of the Laplacian matrix, the number of cluster centers is solved. Finally, this letter employs Laplacian spectral clustering based on graph segmentation to accomplish radar signal deinterleaving. The results of the experimental simulation demonstrate that the method is capable of effectively tackling the “increasing batch” and “mistaken batch” problems of radar signal deinterleaving, and could reach 99.88% deinterleaving accuracy with high robustness.
{"title":"A Radar Signal Deinterleaving Method Based on Complex Network and Laplacian Graph Clustering","authors":"Qiang Guo;Shuai Huang;Liangang Qi;Daren Li;Mykola Kaliuzhnyi","doi":"10.1109/LSP.2024.3461656","DOIUrl":"https://doi.org/10.1109/LSP.2024.3461656","url":null,"abstract":"Radar signal deinterleaving is an essential step in perceiving the battlefield situation and mastering military initiative in the information battlefield. Complex radar systems are rapidly updated and iterated, which exacerbates the possibility of “increasing batch” and “mistaken batch” during radar signal deinterleaving. In this letter, a novel method based on complex networks and Laplacian graph clustering is proposed to improve the accuracy of deinterleaving. First, a complex network is constructed to mine the spatial correlation relationships of the same radar signals. Then, based on the graph characteristics of the Laplacian matrix, the number of cluster centers is solved. Finally, this letter employs Laplacian spectral clustering based on graph segmentation to accomplish radar signal deinterleaving. The results of the experimental simulation demonstrate that the method is capable of effectively tackling the “increasing batch” and “mistaken batch” problems of radar signal deinterleaving, and could reach 99.88% deinterleaving accuracy with high robustness.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-16DOI: 10.1109/LSP.2024.3459811
Natsuki Akaishi;Koki Yamada;Kohei Yatabe
Harmonic/percussive source separation (HPSS) is an important tool for analyzing and processing audio signals. The standard approach to HPSS takes advantage of the structural difference of sinusoidal and percussive components, called anisotropic smoothness