Search Results (86)

The payload module of a high-throughput satellite involves the complex assembly of various components, which plays a vital role in maintaining the satellite’s structural and functional integrity. To support this, inspections during the assembly process are essential for minimizing human error, reducing inspection time, and ensuring adherence to design specifications. However, the current inspection process is entirely manual. It requires substantial manpower and time and is prone to errors such as missed or false detections, which compromise the overall effectiveness of the inspection process. To enhance the inspection efficiency and accuracy of the payload module in high-throughput satellites, this paper proposes a framework for text detection and recognition targeting diamond labels, R-hole labels, and interface labels within payload module images. Detecting and recognizing text labels on products in the high-throughput satellite payload module provides a means to determine the individual products’ assembly states and the correctness of their connection relationships with the waveguides/cables. The framework consists of two key components: a copy-and-paste data augmentation method, which generates synthetic images by overlaying foreground images onto background images, together with a text detection and recognition model incorporating a dual decoder. The detection accuracy on the simulated payload module data reached 87.42%, while the operational efficiency improved significantly by reducing the inspection time from 5 days to just 1 day. Full article

Automatic segmentation of nuclei on breast cancer histology images is a basic and important step for diagnosis in a computer-aided diagnostic approach and helps pathologists discover cancer early. Nuclei segmentation remains a challenging problem due to cancer biology and the variability of tissue characteristics; thus, their detection in an image is a very tedious and time-consuming task. In this context, overlapping nuclei objects present difficulties in separating them by conventional segmentation methods; thus, active contours can be employed in image segmentation. A major limitation of the active contours method is its inability to resolve image boundaries/edges of intersecting objects and segment multiple overlapping objects as a single object. Therefore, we present a hybrid active contour (connected component + active contours) method to segment cancerous lesions in unsupervised human breast histology images. Initially, this approach prepares and pre-processes data through various augmentation methods to increase the dataset size. Then, a stain normalization technique is applied to these augmented images to isolate nuclei features from tissue structures. Secondly, morphology operation techniques, namely erosion, dilation, opening, and distance transform, are used to highlight foreground and background pixels while removing overlapping regions from the highlighted nuclei objects on the image. Consequently, the connected components method groups these highlighted pixel components with similar intensity values and assigns them to their relevant labeled component to form a binary mask. Once all binary-masked groups have been determined, a deep-learning recurrent neural network (RNN) model from the Keras architecture uses this information to automatically segment nuclei objects having cancerous lesions on the image via the active contours method. This approach, therefore, uses the capabilities of connected components analysis to solve the limitations of the active contour method. This segmentation method is evaluated on an unsupervised, augmented human breast cancer histology dataset of 15,179 images. This proposed method produced a significant evaluation result of $98.71\%$ accuracy score. Full article

This paper introduces a new memory-efficient algorithm for connected-components labeling in binary images, which is based on run-length encoding. Unlike conventional pixel-based methods that scan and label individual pixels using global buffers or disjoint-set structures, our approach encodes rows as linked segments and merges them using a union-by-size strategy. We accelerate run detection by using a precomputed 16-bit cache of binary patterns, allowing for fast decoding without relying on bitwise CPU instructions. When compared against other run-length encoded algorithms, such as the Scan-Based Labeling Algorithm or Run-Based Two-Scan, our method achieves up to 35% faster on most real-world datasets. While other binary-optimized algorithms, such as Bit-Run Two-Scan and Bit-Merge Run Scan, are up to 45% faster than our algorithm, they require much higher memory usage. Compared to them, our method tends to reduce memory consumption on some large document datasets by up to 80%. Full article

Soft and flexible capacitive tactile sensors are vital in prosthetics, wearable health monitoring, and soft robotics applications. However, achieving accurate real-time force detection and spatial localization remains a significant challenge, especially in dynamic, non-rigid environments like prosthetic liners. This study presents a real-time force point detection and tracking system using a custom-fabricated soft elastomeric capacitive sensor array in conjunction with image processing and machine learning techniques. The system integrates Otsu’s thresholding, Connected Component Labeling, and a tailored cluster-tracking algorithm for anomaly detection, enabling real-time localization within 1 ms. A $6\times 6$ Dragon Skin-based sensor array was fabricated, embedded with copper yarn electrodes, and evaluated using a UR3e robotic arm and a Schunk force-torque sensor to generate controlled stimuli. The fabricated tactile sensor measures the applied force from 1 to 3 N. Sensor output was captured via a MUCA breakout board and Arduino Nano 33 IoT, transmitting the Ratio of Mutual Capacitance data for further analysis. A Python-based processing pipeline filters and visualizes the data with real-time clustering and adaptive thresholding. Machine learning models such as linear regression, Support Vector Machine, decision tree, and Gaussian Process Regression were evaluated to correlate force with capacitance values. Decision Tree Regression achieved the highest performance ($R^2=0.9996, RMSE=0.0446$), providing an effective correlation factor of $51.76$ for force estimation. The system offers robust performance in complex interactions and a scalable solution for soft robotics and prosthetic force mapping, supporting health monitoring, safe automation, and medical diagnostics. Full article

The early detection of cancerous lesions is a challenging task given the cancer biology and the variability in tissue characteristics, thus rendering medical image analysis tedious and time-inefficient. In the past, conventional computer-aided diagnosis (CAD) and detection methods have heavily relied on the visual inspection of medical images, which is ineffective, particularly for large and visible cancerous lesions in such images. Additionally, conventional methods face challenges in analyzing objects in large images due to overlapping/intersecting objects and the inability to resolve their image boundaries/edges. Nevertheless, the early detection of breast cancer lesions is a key determinant for diagnosis and treatment. In this study, we present a deep learning-based technique for breast cancer lesion detection, namely blob detection, which automatically detects hidden and inaccessible cancerous lesions in unsupervised human breast histology images. Initially, this approach prepares and pre-processes data through various augmentation methods to increase the dataset size. Secondly, a stain normalization technique is applied to the augmented images to separate nucleus features from tissue structures. Thirdly, morphology operation techniques, namely erosion, dilation, opening, and a distance transform, are used to enhance the images by highlighting foreground and background pixels while removing overlapping regions from the highlighted nucleus objects in the image. Subsequently, image segmentation is handled via the connected components method, which groups highlighted pixel components with similar intensity values and assigns them to their relevant labeled components (binary masks). These binary masks are then used in the active contours method for further segmentation by highlighting the boundaries/edges of ROIs. Finally, a deep learning recurrent neural network (RNN) model automatically detects and extracts cancerous lesions and their edges from the histology images via the blob detection method. This proposed approach utilizes the capabilities of both the connected components method and the active contours method to resolve the limitations of blob detection. This detection method is evaluated on 27,249 unsupervised, augmented human breast cancer histology dataset images, and it shows a significant evaluation result in the form of a $98.82\%$ F1 accuracy score. Full article

Fraud detection in large-scale graphs presents significant challenges, especially in heterophilic graphs where linked nodes often belong to dissimilar classes or exhibit contrasting attributes. These asymmetric interactions, combined with class imbalance and limited labeled data, make it difficult to fully leverage node labels in semi-supervised learning frameworks. This study aims to address these challenges by proposing a novel framework, Spectrum-Constrained and Skip-Enhanced Graph Fraud Detection (SCSE-GFD), designed specifically for fraud detection in heterophilic graphs. The primary objective is to enhance fraud detection performance while maintaining computational efficiency. SCSE-GFD integrates several key components to improve performance. It employs adaptive polynomial convolution to capture multi-frequency signals and utilizes relation-specific spectral filtering to accommodate both homophilic and heterophilic structures. Additionally, a relation-aware mechanism is incorporated to differentiate between edge types, which enhances feature propagation across diverse graph connections. To address the issue of over-smoothing, skip connections are used to preserve both low- and high-level node representations. Furthermore, supervised edge classification is used to improve the structural understanding of the graph. Extensive experiments on real-world datasets, including Amazon and YelpChi, demonstrate SCSE-GFD’s effectiveness. The framework achieved state-of-the-art AUC scores of 96.21% on Amazon and 90.58% on YelpChi, significantly outperforming existing models. These results validate SCSE-GFD’s ability to improve fraud detection accuracy while maintaining efficiency. Full article

The coal mining industry has accumulated a vast amount of knowledge on shearer accident analysis and handling during its development. Accurately identifying and extracting entity information related to shearer maintenance is crucial for advancing downstream tasks in intelligent shearer operations and maintenance. Currently, named entity recognition in the field of shearer maintenance primarily relies on fine-tuning-based methods; however, a gap exists between pretraining and downstream tasks. In this paper, we introduce prompt learning and large language models (LLMs), proposing a named entity recognition method for shearer maintenance based on multi-level clue-guided prompt learning (MCP). This method consists of three key components: (1) the prompt learning layer, which encapsulates the information to be identified and forms multi-level sub-clues into structured prompts based on a predefined format; (2) the LLM layer, which employs a decoder-only architecture-based large language model to deeply process the connection between the structured prompts and the information to be identified through multiple stacked decoder layers; and (3) the answer layer, which maps the output of the LLM layer to a structured label space via a parser to obtain the recognition results of structured named entities in the shearer maintenance domain. By designing multi-level sub-clues, MCP enables the model to extract and learn trigger words related to entity recognition from the prompts, acquiring context-aware prompt tokens. This allows the model to make accurate predictions, bridging the gap between fine-tuning and pretraining while eliminating the reliance on labeled data for fine-tuning. Validation was conducted on a self-constructed knowledge corpus in the shearer maintenance domain. Experimental results demonstrate that the proposed method outperforms mainstream baseline models in the field of shearer maintenance. Full article

Atmospheric jets are pivotal components of atmospheric circulation, profoundly influencing surface weather patterns and the development of extreme weather events such as storms and cold waves. Accurate detection of the jet stream axis is indispensable for enhancing weather forecasting, monitoring climate change, and mitigating disasters. However, traditional methods for delineating atmospheric jets are plagued by inefficiency, substantial errors, and pronounced subjectivity, limiting their applicability in complex atmospheric scenarios. Current research on semi-supervised methods for extracting atmospheric jets remains scarce, with most approaches dependent on traditional techniques that struggle with stability and generalization. To address these limitations, this study proposes a semi-supervised jet stream axis extraction method leveraging an enhanced U-Net++ model. The approach incorporates improved residual blocks and enhanced attention gate mechanisms, seamlessly integrating these enhanced attention gates into the dense skip connections of U-Net++. Furthermore, it optimizes the consistency learning phase within semi-supervised frameworks, effectively addressing data scarcity challenges while significantly enhancing the precision of jet stream axis detection. Experimental results reveal the following: (1) With only 30% of labeled data, the proposed method achieves a precision exceeding 80% on the test set, surpassing state-of-the-art (SOTA) baselines. Compared to fully supervised U-Net and U-Net++ methods, the precision improves by 17.02% and 9.91%. (2) With labeled data proportions of 10%, 20%, and 30%, the proposed method outperforms the MT semi-supervised method, achieving precision gains of 9.44%, 15.58%, and 19.50%, while surpassing the DCT semi-supervised method with improvements of 10.24%, 16.64%, and 14.15%, respectively. Ablation studies further validate the effectiveness of the proposed method in accurately identifying the jet stream axis. The proposed method exhibits remarkable consistency, stability, and generalization capabilities, producing jet stream axis extractions closely aligned with wind field data. Full article

Objective: This study explores a semi-supervised learning (SSL), pseudo-labeled strategy using diverse datasets such as head and neck cancer (HNCa) to enhance lung cancer (LCa) survival outcome predictions, analyzing handcrafted and deep radiomic features (HRF/DRF) from PET/CT scans with hybrid machine learning systems (HMLSs). Methods: We collected 199 LCa patients with both PET and CT images, obtained from TCIA and our local database, alongside 408 HNCa PET/CT images from TCIA. We extracted 215 HRFs and 1024 DRFs by PySERA and a 3D autoencoder, respectively, within the ViSERA 1.0.0 software, from segmented primary tumors. The supervised strategy (SL) employed an HMLS–PCA connected with six classifiers on both HRFs and DRFs. The SSL strategy expanded the datasets by adding 408 pseudo-labeled HNCa cases (labeled by the Random Forest algorithm) to 199 LCa cases, using the same HMLS techniques. Furthermore, principal component analysis (PCA) linked with four survival prediction algorithms were utilized in the survival hazard ratio analysis. Results: The SSL strategy outperformed the SL method (p << 0.001), achieving an average accuracy of 0.85 ± 0.05 with DRFs from PET and PCA + Multi-Layer Perceptron (MLP), compared to 0.69 ± 0.06 for the SL strategy using DRFs from CT and PCA + Light Gradient Boosting (LGB). Additionally, PCA linked with Component-wise Gradient Boosting Survival Analysis on both HRFs and DRFs, as extracted from CT, had an average C-index of 0.80, with a log rank p-value << 0.001, confirmed by external testing. Conclusions: Shifting from HRFs and SL to DRFs and SSL strategies, particularly in contexts with limited data points, enabling CT or PET alone, can significantly achieve high predictive performance. Full article

Extracting building outlines from 3D models poses significant challenges stemming from the intricate diversity of structures and the complexity of urban scenes. Current techniques heavily rely on human expertise and involve repetitive, labor-intensive manual operations. To address these limitations, this paper presents an innovative automatic technique for accurately extracting building footprints, particularly those with gable and hip roofs, directly from 3D data. Our methodology encompasses several key steps: firstly, we construct a triangulated irregular network (TIN) to capture the intricate geometry of the buildings. Subsequently, we employ 2D indexing and counting grids for efficient data processing and utilize a sophisticated connected component labeling algorithm to precisely identify the extents of the roofs. A single seed point is manually specified to initiate the process, from which we select the triangular facets representing the outer walls of the buildings. Utilizing the projection histogram method, these facets are grouped and processed to extract regular building footprints. Extensive experiments conducted on datasets from Nanjing and Wuhan demonstrate the remarkable accuracy of our approach. With mean intersection over union (mIOU) values of 99.2% and 99.4%, respectively, and F1 scores of 94.3% and 96.7%, our method proves to be both effective and robust in mapping building footprints from 3D real-scene data. This work represents a significant advancement in automating the extraction of building footprints from complex 3D scenes, with potential applications in urban planning, disaster response, and environmental monitoring. Full article

Background: The combination of macroporous cryogels with synthetic peptide factors represents a promising but poorly explored strategy for the development of extracellular matrix (ECM)-mimicking scaffolds for peripheral nerve (PN) repair. Methods: In this study, IKVAV peptide was functionalized with terminal lysine residues to allow its in situ cross-linking with gelatin macromer, resulting in the formation of IKVAV-containing proteinaceous cryogels. The controllable inclusion and distribution of the peptide molecules within the scaffold was verified using a fluorescently labelled peptide counterpart. The optimized cryogel scaffold was combined with polycaprolactone (PCL)-based shell tube to form a suturable nerve conduit (NC) to be implanted into sciatic nerve diastasis in rats. Results: The NC constituents did not impair the viability of primary skin fibroblasts. Concentration-dependent effects of the peptide component on interrelated viscoelastic and swelling properties of the cryogels as well as on proliferation and morphological differentiation of neurogenic PC-12 cells were established, also indicating the existence of an optimal-density range of the introduced peptide. The in vivo implanted NC sustained the connection of the nerve stumps with partial degradation of the PCL tube over eight weeks, whereas the core-filling cryogel profoundly improved local electromyographic recovery and morphological repair of the nerve tissues, confirming the regenerative activity of the developed scaffold. Conclusions: These results provide proof-of-concept for the development of a newly designed PN conduit prototype based on IKVAV-activated cryogel, and they can be exploited to create other ECM-mimicking scaffolds. Full article

The detection of distant tiny objects in aerial imagery plays a pivotal role in early warning, localization, and recognition tasks. However, due to the scarcity of appearance information, minimal pixel representation, susceptibility to blending with the background, and the incompatibility of conventional metrics, the rapid and accurate detection of tiny objects poses significant challenges. To address these issues, a single-stage tiny object detector tailored for aerial imagery is proposed, comprising two primary components. Firstly, we introduce a light backbone-heavy neck architecture, named the Global Context Self-Attention and Dense Nested Connection Feature Extraction Network (GC-DN Network), which efficiently extracts and fuses multi-scale features of the target. Secondly, we propose a novel metric, MMPW, to replace the Intersection over Union (IoU) in label assignment strategies, Non-Maximum Suppression (NMS), and regression loss functions. Specifically, MMPW models bounding boxes as 2D Gaussian distributions and utilizes the Mixed Minimum Point-Wasserstein Distance to quantify the similarity between boxes. Experiments conducted on the latest aerial image tiny object datasets, AI-TOD and VisDrone-19, demonstrate that our method improves AP50 performance by 9.4% and 5%, respectively, and AP performance by 4.3% and 3.6%. This validates the efficacy of our approach for detecting tiny objects in aerial imagery. Full article

Segmentation of nuclei in histology images is key in analyzing and quantifying morphology changes of nuclei features and tissue structures. Conventional diagnosis, segmenting, and detection methods have relied heavily on the manual-visual inspection of histology images. These methods are only effective on clearly visible cancerous lesions on histology images thus limited in their performance due to the complexity of tissue structures in histology images. Hence, early detection of breast cancer is key for treatment and profits from Computer-Aided-Diagnostic (CAD) systems introduced to efficiently and automatically segment and detect nuclei cells in pathology. This paper proposes, an automatic watershed segmentation method of cancerous lesions in unsupervised human breast histology images. Firstly, this approach pre-processes data through various augmentation methods to increase the size of dataset images, then a stain normalization technique is applied to these augmented images to isolate nuclei features from tissue structures. Secondly, data enhancement techniques namely; erosion, dilation, and distance transform are used to highlight foreground and background pixels while removing unwanted regions from the highlighted nuclei objects on the image. Consequently, the connected components method groups these highlighted pixel components with similar intensity values and, assigns them to their relevant labeled component binary mask. Once all binary masked groups have been determined, a deep-learning recurrent neural network from the Keras architecture uses this information to automatically segment nuclei objects with cancerous lesions and their edges on the image via watershed filling. This segmentation method is evaluated on an unsupervised, augmented human breast cancer histology dataset of 11,151 images. This proposed method produced a significant evaluation result of $98\%$ F1-accuracy score. Full article

The integration of satellite data with deep learning has revolutionized various tasks in remote sensing, including classification, object detection, and semantic segmentation. Cloud segmentation in high-resolution satellite imagery is a critical application within this domain, yet progress in developing advanced algorithms for this task has been hindered by the scarcity of specialized datasets and annotation tools. This study addresses this challenge by introducing CloudLabel, a semi-automatic annotation technique leveraging region growing and morphological algorithms including flood fill, connected components, and guided filter. CloudLabel v1.0 streamlines the annotation process for high-resolution satellite images, thereby addressing the limitations of existing annotation platforms which are not specifically adapted to cloud segmentation, and enabling the efficient creation of high-quality cloud segmentation datasets. Notably, we have curated the Annotated Dataset for Training Cloud Segmentation (ADTCS) comprising 32,065 images (512 × 512) for cloud segmentation based on CloudLabel. The ADTCS dataset facilitates algorithmic advancement in cloud segmentation, characterized by uniform cloud coverage distribution and high image entropy (mainly 5–7). These features enable deep learning models to capture comprehensive cloud characteristics, enhancing recognition accuracy and reducing ground object misclassification. This contribution significantly advances remote sensing applications and cloud segmentation algorithms. Full article

Data augmentation is crucial for enhancing the performance of text classification models when labelled training data are scarce. For natural language processing (NLP) tasks, large language models (LLMs) are able to generate high-quality augmented data. But a fundamental understanding of the reasons for their effectiveness remains limited. This paper presents a geometric and topological perspective on textual data augmentation using LLMs. We compare the augmentation data generated by GPT-J with those generated through cosine similarity from Word2Vec and GloVe embeddings. Topological data analysis reveals that GPT-J generated data maintains label coherence. Convex hull analysis of such data represented by their two principal components shows that they lie within the spatial boundaries of the original training data. Delaunay triangulation reveals that increasing the number of augmented data points that are connected within these boundaries correlates with improved classification accuracy. These findings provide insights into the superior performance of LLMs in data augmentation. A framework for predicting the usefulness of augmentation data based on geometric properties could be formed based on these techniques. Full article