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Loop closure detection is a key challenge in visual simultaneous localization and mapping (SLAM) systems, which has attracted significant research interest in recent years. It entails correctly determining whether a scene has previously been visited by a mobile robot and completely establishing the consistent maps of motion. There are many loop closure detection methods that have been proposed, but most of these algorithms are handcrafted features-based and perform weak robustness to illumination variations. In this paper, we investigate a Siamese Convolutional Neural Network (SCNN) to solve the task of loop closure detection in RGB-D SLAM. Firstly, we use a pre-trained SCNN model to extract features as image descriptors; then, the L2 norm distance is adopted as a similarity metric between descriptors. In terms of the learned features for matching, there are two key issues for discussion: (1) how to define an appropriate loss as supervision (utilizing the cross-entropy loss, the contrastive loss, or the combination of two); and (2) how to combine the appearance information in RGB images and position information in depth images (utilizing early fusion, mid-level fusion or late fusion). We compare our proposed method of different baseline by experiments carried out on two public datasets (New College and NYU), and our performance outperforms the state-of-the-art. [ABSTRACT FROM AUTHOR]

Objective. Low efficiency in medical image segmentation is a common issue that limits computer-aided diagnosis development. Due to the varying positions and sizes of nodules, it is not easy to accurately segment ultrasound images. This study aims to propose a segmentation model that maintains high efficiency while improving accuracy. Approach.  We propose a novel layer that integrates the advantages of dense connectivity, dilated convolution, and factorized filters to maintain excellent efficiency while improving accuracy. Dense connectivity optimizes feature reuse, dilated convolution redesigns layers, and factorized convolution improves efficiency. Moreover, we propose a loss function optimization method from a pixel perspective to increase the network's accuracy further. Main results. Experiments on the Thyroid dataset show that our method achieves 81.70% intersection-over-union (IoU), 90.50% true positive rate (TPR), and 0.25% false positive rate (FPR). In terms of accuracy, our method outperforms the state-of-the-art methods, with twice faster inference and nearly 400 times fewer parameters. Meanwhile, in a test on an External Thyroid dataset, our method achieves 77.03% IoU, 82.10% TPR, and 0.16% FPR, demonstrating our proposed model's robustness. Significance. We propose a real-time semantic segmentation architecture for thyroid nodule segmentation in ultrasound images called fully convolution dense dilated network (FCDDN). Our method runs fast with a few parameters and is suitable for medical devices requiring real-time segmentation., (© 2024 Institute of Physics and Engineering in Medicine.)