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Unmanned Aerial Vehicle (UAV) has seen a dramatic rise in popularity for remote-sensing image acquisition and analysis in recent years. It has brought promising results in low-altitude monitoring tasks that require detailed visual inspections. Semantic segmentation is one of the hot topics in UAV remote sensing image analysis, as its capability to mine contextual semantic information from UAV images is crucial for achieving a fine-grained understanding of scenes. However, in the remote sensing field, recent reviews have not focused on combining "UAV remote sensing" and "semantic segmentation" to summarize the advanced works and future trends. In this study, we focus primarily on describing various recent semantic segmentation methods applied in UAV remote sensing images and summarizing their advantages and limitations. According to the distinction in modeling contextual semantic information, we have categorized and outlined the methods based on graph-based contextual models and deep-learning-based models. Publicly available UAV-based image datasets are also gathered to encourage systematic research on advanced semantic segmentation methods. We provide quantitative results of representative methods on two high-resolution UAV-based image datasets for fair comparisons and discussions in terms of semantic segmentation accuracy and model inference efficiency. Besides, this paper concludes some remaining challenges and future directions in semantic segmentation for UAV remote sensing images and points out that methods based on deep learning will become the future research trend. • Summarize semantic segmentation methods and datasets for UAV remote sensing images. • Provide fair comparisons of representative methods in accuracy and efficiency. • Identify challenges and future directions for UAV image semantic segmentation. • Semantic segmentation methods based on deep learning will be the mainstream. [ABSTRACT FROM AUTHOR]

Colorectal polyps are known to be potential precursors to colorectal cancer. Effective polyp segmentation during colonoscopy examinations can help the clinicians accurately locate potential polyp areas, and reduce misdiagnosis and missed diagnosis. Although existing approaches have achieved significant breakthroughs in medical image segmentation, polyp segmentation is still far from being solved. This is mainly due to the following reasons: (1) most of them tend to ignore the feature misalignment issues during the feature aggregation process; and (2) few algorithms explicitly consider the impact of boundary information on the performance of polyp segmentation. To solve the above issues, we formulate a novel neural network for polyp segmentation in endoscopy images. Different from existing approaches, we explore a new paradigm to enhance multi-level feature fusion by introducing the Feature Fusion Module, which leverages the semantic offset field learned to align the multi-level feature maps for resolving the feature misalignment issue. In addition, we design an auxiliary boundary branch to focus on boundary-aware information, and thus boost the performance of boundary prediction. Specifically, the reference boundary map learned through end-to-end optimization can be considered as a complementary feature of the high-level semantics representation, and then integrated into the main branch via the Boundary Embedding Module, which in turn promotes further refinement of the prediction, especially the boundary. Extensive experiments demonstrate that our approach achieves state-of-the-art performance in polyp segmentation. [ABSTRACT FROM AUTHOR]

In this paper, we propose an improved rotation invariant uniform local binary pattern (RIU-LBP) operator for segmenting high-resolution sensing image which can effectively describe the texture features of a high-resolution remote sensing image. The improved RIU-LBP is based on RIU-LBP. It introduces a threshold in binarization of region pixels. The new LBP operator can better tolerate small texture variation and better distinguish the plain and rough texture than the original RIU-LBP does. Then, a merging criterion of texture regions is proposed, which is based on regional LBP value distribution and Bhattacharyya distance. Finally, the texture merging criterion and spectral merging criterion are combined in the statistical region merging (SRM)-based remote sensing image segmentation method to improve segmentation results, taking full advantage of rich spectral and texture information in high-resolution remote sensing images. This algorithm can be adjusted to the number of segmented regions, and experiments indicate better segmentation results than ENVI 5.0 and the SRM method. [ABSTRACT FROM AUTHOR]

• A method for extracting weld seam characteristics of dissimilar welding is proposed. • The weld seam characteristics are obtained automatically. • The segmentation effects of different methods are compared. • The extracted values are in good agreement with experimentally measured values. Dissimilar materials welding has been widely used in the components with the requirements of high strength and low weight in manufacturing industry. The geometric characteristics of weld are highly related to the quality of welded joints. Therefore, this paper proposes a method to extract the weld seam characteristics for laser welding of dissimilar materials of the low carbon steel (Q235) and stainless steel (316L) by image segmentation. The region of interest (ROI) of metallographic image of fusion region is extracted and converted as the grayscale image for filtering process. The multi-thresholding image segmentation method using genetic algorithm (GA), seeded region growing (SRG) method with automatic seed selection and RGB image segmentation method based on Euclidean distance are adopted to segment the image. The segmentation effects of different methods are compared in details and segmented image with the best quality is selected for extracting weld seam characteristics including weld area (AW), right-front width (WFR), left-front width (WFL) and weld penetration (WP). The relative errors between the extracted values and the experimentally measured values are less than 4%. The results show that the proposed method is reasonable and feasible to obtain the weld seam characteristics for quantitative evaluation of laser welding quality of dissimilar materials. [ABSTRACT FROM AUTHOR]

The highly ambiguous nature of boundaries and similar objects is difficult to address in some ultrasound image segmentation tasks, such as neck muscle segmentation, leading to unsatisfactory performance. Thus, this paper proposes a two-stage network called SCCNet (self-correction context network) using a self-correction boundary preservation module and class-context filter to alleviate these problems. The proposed self-correction boundary preservation module uses a dynamic key boundary point (KBP) map to increase the capability of iteratively discriminating ambiguous boundary points segments, and the predicted segmentation map from one stage is used to obtain a dynamic class prior filter to improve the segmentation performance at Stage 2. Finally, three datasets, Neck Muscle, CAMUS and Thyroid, are used to demonstrate that our proposed SCCNet outperforms other state-of-the art methods, such as BPBnet, DSNnet, and RAGCnet. Our proposed network shows at least a 1.2–3.7% improvement on the three datasets, Neck Muscle, Thyroid, and CAMUS. The source code is available at https://github.com/lijixing0425/SCCNet. • First, we proposed a new Self-Correction boundary preservation module, named DBPB, which is an iterative training strategy proved to have effect on decreasing ambiguous boundary problem for Ultrasound image segmentation with high-variability. • Second, we propose a new Dynamic Class Prior Filter(DCPF) module that it incorporates a class-level context prior to construct the dynamic multiscale filter. The proposed DCPF outperforms DCM (Dynamic Convolutional Module), and other popular context modules for ultrasound image segmentation with high-variability,proved by our ablation experiments. • Third, we demonstrate that the proposed SCCNet, considering boundary and context modules outperforms the-state-of-the-art networks such as BPBnet, RAGCnet which only considering one of context and boundary modules. Meanwhile, SCCNet outperforms LFB-Neton CAMUS dataset, further demonstrating that our method consistently outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]