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Boundary contour determination during seedling image segmentation is critical for accurate object detection and morphological characterization in agricultural machine vision systems. The traditional manual annotation for segmentation is labor-intensive, time-consuming, and prone to errors, especially in controlled environments with complex backgrounds. These errors can affect the accuracy of detecting phenotypic traits, like shape, size, and width. To address these issues, this study introduced a method that integrated image features and a support vector machine (SVM) to improve boundary contour determination during segmentation, enabling real-time detection and monitoring. Seedling images (pepper, tomato, cucumber, and watermelon) were captured under various lighting conditions to enhance object–background differentiation. Histogram equalization and noise reduction filters (median and Gaussian) were applied to minimize the illumination effects. The peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) were used to select the clip limit for histogram equalization. The images were analyzed across 18 different color spaces to extract the color features, and six texture features were derived using the gray-level co-occurrence matrix (GLCM) method. To reduce feature overlap, sequential feature selection (SFS) was applied, and the SVM was used for object segmentation. The SVM model achieved 73% segmentation accuracy without SFS and 98% with SFS. Segmentation accuracy for the different seedlings ranged from 81% to 98%, with a low boundary misclassification rate between 0.011 and 0.019. The correlation between the actual and segmented contour areas was strong, with an R2 up to 0.9887. The segmented boundary contour files were converted into annotation files to train a YOLOv8 model, which achieved a precision ranging from 96% to 98.5% and a recall ranging from 96% to 98%. This approach enhanced the segmentation accuracy, reduced manual annotation, and improved the agricultural monitoring systems for plant health management. The future direction involves integrating this system with advanced methods to address overlapping image segmentation challenges, further enhancing the real-time seedling monitoring and optimizing crop management and productivity. [ABSTRACT FROM AUTHOR]

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After annotating a medical imaging modality that is relatively straightforward to label, doctors often expect automatic annotations for images from other modalities of the same region, even though these modalities differ in contrast and structure. This study focuses on creating automatic lymph node annotation in MRI images using available CT annotations via deep-learning models. Training such models typically requires partial MRI labels for semi-supervision. However, annotating lymph nodes in MRI images is particularly challenging due to their small size and the high cost of MRI scans. These factors make it difficult to create labeled MRI datasets for deep learning model training. Moreover, existing cross-modal annotation methods primarily focus on large tumors and require large datasets, making them unsuitable for small lymph nodes with less training data. We address these challenges using cross-modal supervision through image registration. Our algorithm reduces the burden of manual annotation and the reliance on large labeled datasets and eliminates the need for any MRI ground truth. The algorithm has three steps: 1) unsupervised deformable image translation-based registration of MRI to CT image, producing registered MRI; 2) annotating lymph nodes in registered MRI with the available CT labels; and 3) deregistration of registered annotated MRI back to the original shape of MRI. The translation-based registration model for the algorithm’s first and third steps uses a discriminator-free StyleGAN2 translation network and a deformable image registration network with a U-Net-inspired architecture. This registration network includes local and global feature extraction modules, a local-global spatial correlation module, and a superresolution loss function. Our approach eliminates the need for MRI labels by registering MRI with CT images. Experiments show 2.19% and 4.08% MSE reductions, 5.40% and 3.28% SSIM improvements, 29.85% and 3.82% NCC increases for cross-modality and mono-modality registration, respectively, along with a 36.7% training speedup over state-of-the-art translation-based registration models. The lymph node annotation method achieves an average of 74.3% DSC in the region of interest. It also has broader applications in multimodality image segmentation. We open-source the code through a GitHub repository.INDEX TERMS Image registration, image annotation, magnetic resonance imaging, computed tomography, unsupervised learning, superresolution.

Primary malignant bone tumors, such as osteosarcoma, significantly affect the pediatric and young adult populations, necessitating early diagnosis for effective treatment. This study developed a high‐performance artificial intelligence (AI) model to detect osteosarcoma from X‐ray images using highly accurate annotated data to improve diagnostic accuracy at initial consultations. Traditional models trained on unannotated data have shown limited success, with sensitivities of approximately 60%–70%. In contrast, our model used a data‐centric approach with annotations from an experienced oncologist, achieving a sensitivity of 95.52%, specificity of 96.21%, and an area under the curve of 0.989. The model was trained using 468 X‐ray images from 31 osteosarcoma cases and 378 normal knee images with a strategy to maximize diversity in the training and validation sets. It was evaluated using an independent dataset of 268 osteosarcoma and 554 normal knee images to ensure generalizability. By applying the U‐net architecture and advanced image processing techniques such as renormalization and affine transformations, our AI model outperforms existing models, reducing missed diagnoses and enhancing patient outcomes by facilitating earlier treatment. This study highlights the importance of high‐quality training data and advocates a shift towards data‐centric AI development in medical imaging. These insights can be extended to other rare cancers and diseases, underscoring the potential of AI in transforming diagnostic processes in oncology. The integration of this AI model into clinical workflows could support physicians in early osteosarcoma detection, thereby improving diagnostic accuracy and patient care. [ABSTRACT FROM AUTHOR]

Plant diseases are a significant threat to modern agricultural productivity. Hydroponic systems are also affected for various reasons. Reliable and efficient detection methods are essential for early intervention and management of diseases in hydroponics. This study investigates the use of You Only Look Once (YOLO) models, namely YOLOv8 and YOLOv9, for the detection of plant diseases in a hydroponic environment. A diverse dataset was prepared, comprising images from a hydroponics system setup and the New Plant Disease Image Dataset from Kaggle. Custom annotated images were used to train and test the models and compare their accuracy, processing speed, and robustness in hydroponic systems. The results showed that YOLOv9 is slightly better than YOLOv8 in terms of detection accuracy, as it achieved 88.38% compared to 87.22%, respectively. YOLOv8 requires less computational resources and takes relatively less time than YOLOv9 for real-time plant disease detection. Therefore, it is recommended for portable devices. [ABSTRACT FROM AUTHOR]

Malaria is a major public health issue in many regions of Africa, including Tanzania. The Tanzania Malaria National Strategic Plan (2021–2025) emphasizes on high-quality testing services availability, high coverage of timely diagnosis of malaria, and availability of innovative diagnostic systems for effective detection, treatment and control of malaria. This would be achieved by employing state of the art technologies like Machine learning. However, Machine learning requires diverse dataset to work effectively and efficiently. Therefore, this paper presents blood smear imagery dataset that can be used by researchers to develop computer vision systems for malaria parasite detection. The imagery dataset were acquired by setting up a 40X-2500X Real 4 K compound microscope with a 4k SONY IMX334 sensor camera mounted to it in five health centres of Tanga region. Blood samples taken according to normal routine of diagnosing patients in health care, were stained using Giemsa reagent and examined under microscope. Following these procedures, the study collected and annotated Thick infected blood smear images (n=1139); Thick uninfected blood smear images (n=1071); Thin uninfected blood smear images (n=270); and Thin infected blood smear images (n=1064). Furthermore, the curated dataset have been uploaded in a public Harvard data verse repository. In summary, the dataset aims to support the creation of diagnostic tools that improve malaria detection, thereby advancing health outcomes and aiding malaria control initiatives in Tanzania and other regions impacted by the disease.

The motility pattern of the reticulo-rumen is a key factor affecting feed intake, rumen digesta residence time, and rumen fermentation. However, it is difficult to study reticulo-ruminal motility using general methods owing to the complexity of the reticulo-ruminal structure. Thus, we aimed to develop a technique to demonstrate the reticulo-ruminal motility pattern in static goats. Six Xiangdong black goats (half bucks and half does, body weight 29.5 ± 1.0 kg) were used as model specimens. Reticulo-ruminal motility videos were obtained using medical barium meal imaging technology. Videos were then analyzed using image annotation and the centroid method. The results showed that reticulo-ruminal motility was divided into primary (stages I, II, III, and IV) and secondary contraction, and the movements of ruminal digesta depended on reticulo-ruminal motility. Our results indicated common motility between the ruminal dorsal sac and ruminal dorsal blind sac. We observed that stages I (3.92 vs. 3.21 s) (P < 0.01), II (4.81 vs. 4.23 s) (P < 0.01), and III (5.65 vs. 5.15 s) (P < 0.05); interval (53.79 vs. 50.95 s); secondary contraction time (10.5 vs. 10 s); and were longer, whereas stage IV appeared to be shorter in the bucks than in the does (7.83 vs. 14.67 s) (P < 0.01). The feasibility of using barium meal imaging technology for assessing reticulo-ruminal and digesta motility was verified in our study. We determined the duration of each stage of reticulo-ruminalmotility and collected data on the duration and interval of each stage of ruminal motility in goats. This research provides new insights for the study of gastrointestinal motility and lays a solid foundation for the study of artificial rumen. [ABSTRACT FROM AUTHOR]

In this work, we investigate the impact of annotation quality and domain expertise on the performance of Convolutional Neural Networks (CNNs) for semantic segmentation of wear on titanium nitride (TiN) and titanium carbonitride (TiCN) coated end mills. Using an innovative measurement system and customized CNN architecture, we found that domain expertise significantly affects model performance. Annotator 1 achieved maximum mIoU scores of 0.8153 for abnormal wear and 0.7120 for normal wear on TiN datasets, whereas Annotator 3 with the lowest expertise achieved significantly lower scores. Sensitivity to annotation inconsistencies and model hyperparameters were examined, revealing that models for TiCN datasets showed a higher coefficient of variation (CV) of 16.32% compared to 8.6% for TiN due to the subtle wear characteristics, highlighting the need for optimized annotation policies and high-quality images to improve wear segmentation. [ABSTRACT FROM AUTHOR]

The usage of virtual reality (VR) in healthcare field has been gaining attention lately. The main use cases revolve around medical imaging and clinical skill training. Healthcare professionals have found great benefits in these cases when done in VR. While medical imaging on the desktop has lots of available software with various tools, VR versions are mostly stripped-down with only basic tools. One of the many tool groups significantly missing is annotation. In this paper, we survey the current situation of medical imaging software both on the desktop and in the VR environment. We will discuss general information on medical imaging and provide examples of both desktop and VR applications. We will also discuss the current status of annotation in VR, the problems that need to be overcome and possible solutions for them. The findings of this paper should help developers of future medical image annotation tools in choosing which problem they want to tackle and possible methods. The findings will be used to help in our future work of developing annotation tools. [ABSTRACT FROM AUTHOR]

Imagery has become one of the main data sources for investigating seascape spatial patterns. This is particularly true in deep-sea environments, which are only accessible with underwater vehicles. On the one hand, using collaborative web-based tools and machine learning algorithms, biological and geological features can now be massively annotated on 2D images with the support of experts. On the other hand, geomorphometrics such as slope or rugosity derived from 3D models built with structure from motion (sfm) methodology can then be used to answer spatial distribution questions. However, precise georeferencing of 2D annotations on 3D models has proven challenging for deep-sea images, due to a large mismatch between navigation obtained from underwater vehicles and the reprojected navigation computed in the process of building 3D models. In addition, although 3D models can be directly annotated, the process becomes challenging due to the low resolution of textures and the large size of the models. In this article, we propose a streamlined, open-access processing pipeline to reproject 2D image annotations onto 3D models using ray tracing. Using four underwater image datasets, we assessed the accuracy of annotation reprojection on 3D models and achieved successful georeferencing to centimetric accuracy. The combination of photogrammetric 3D models and accurate 2D annotations would allow the construction of a 3D representation of the landscape and could provide new insights into understanding species microdistribution and biotic interactions. [ABSTRACT FROM AUTHOR]

Abstract Pixel‐level labeling of regions of interest in an image is a key step in building a labeled training dataset for supervised deep learning networks of images. However, traditional manual labeling of cancerous regions in digital pathological images by doctors is time‐consuming and inefficient. To address this issue, this paper proposes an automatic labeling method for whole slide images, which combines clustering and edge detection techniques. The proposed method utilizes the multi‐level feature fusion model and the Long‐Short Term Memory network to discriminate the cancerous nature of the whole slide images, thereby improving the classification accuracy of the whole slide images. Subsequently, the automatic labeling of cancerous regions is achieved by integrating a density‐based clustering algorithm and an edge point extraction algorithm, both based on the discriminated results of the cancerous properties of whole slide images. The experimental results demonstrate the effectiveness of the proposed method, which offers an efficient and accurate solution to the challenging task of cancerous region labeling in digital pathological images.

Pixel‐level labeling of regions of interest in an image is a key step in building a labeled training dataset for supervised deep learning networks of images. However, traditional manual labeling of cancerous regions in digital pathological images by doctors is time‐consuming and inefficient. To address this issue, this paper proposes an automatic labeling method for whole slide images, which combines clustering and edge detection techniques. The proposed method utilizes the multi‐level feature fusion model and the Long‐Short Term Memory network to discriminate the cancerous nature of the whole slide images, thereby improving the classification accuracy of the whole slide images. Subsequently, the automatic labeling of cancerous regions is achieved by integrating a density‐based clustering algorithm and an edge point extraction algorithm, both based on the discriminated results of the cancerous properties of whole slide images. The experimental results demonstrate the effectiveness of the proposed method, which offers an efficient and accurate solution to the challenging task of cancerous region labeling in digital pathological images. [ABSTRACT FROM AUTHOR]

Automatic image annotation (AIA) is a mechanism for describing the visual content of an image with a list of semantic labels. Typically, there is a massive imbalance between positive and negative tags in a picture—in other words, an image includes much fewer positive labels than negative ones. This imbalance can negatively affect the optimization process and diminish the emphasis on gradients from positive labels during training. Although traditional annotation models mainly focus on model structure design, we propose a novel unsymmetrical loss function for a deep convolutional neural network (CNN) that performs differently on positives and negatives, which leads to a reduction in the loss contribution from negative labels and also highlights the contribution of positive ones. During the annotation process, we specify a threshold for each label separately based on the Matthews correlation coefficient (MCC). Extensive experiments on high-vocabulary datasets like Corel 5k, IAPR TC-12, and Esp Game reveal that despite ignoring the semantic relationships between labels, our suggested approach achieves remarkable results compared to the state-of-the-art automatic image annotation models. [ABSTRACT FROM AUTHOR]

The “Tea Leaf Age Quality” dataset represents a pioneering agricultural and machine-learning resource to enhance tea leaf classification, detection, and quality prediction based on leaf age. This comprehensive collection includes 2208 raw images from the historic Malnicherra Tea Garden in Sylhet and two other gardens from Sreemangal and Moulvibajar in Bangladesh. The dataset is systematically categorized into four distinct classes (T1: 1–2 days, T2: 3–4 days, T3: 5–7 days, and T4: 7+ days) according to age-based quality criteria. This dataset helps to determine how tea quality changes with age. The most recently harvested leaves (T1) exhibited superior quality, whereas the older leaves (T4) were suboptimal for brewing purposes. It includes raw, unannotated images that capture the natural diversity of tea leaves, precisely annotated versions for targeted analysis, and augmented data to facilitate advanced research. The compilation process involved extensive on-ground data collection and expert consultations to ensure the authenticity and applicability of the dataset. The “Tea Leaf Age Quality” dataset is a crucial tool for advancing deep learning models in tea leaf classification and quality assessment, ultimately contributing to the technological evolution of the agricultural sector by providing detailed age-stratified tea leaf categorization.

Imagery has become one of the main data sources for investigating seascape spatial patterns. This is particularly true in deep-sea environments, which are only accessible with underwater vehicles. On the one hand, using collaborative web-based tools and machine learning algorithms, biological and geological features can now be massively annotated on 2D images with the support of experts. On the other hand, geomorphometrics such as slope or rugosity derived from 3D models built with structure from motion (sfm) methodology can then be used to answer spatial distribution questions. However, precise georeferencing of 2D annotations on 3D models has proven challenging for deep-sea images, due to a large mismatch between navigation obtained from underwater vehicles and the reprojected navigation computed in the process of building 3D models. In addition, although 3D models can be directly annotated, the process becomes challenging due to the low resolution of textures and the large size of the models. In this article, we propose a streamlined, open-access processing pipeline to reproject 2D image annotations onto 3D models using ray tracing. Using four underwater image datasets, we assessed the accuracy of annotation reprojection on 3D models and achieved successful georeferencing to centimetric accuracy. The combination of photogrammetric 3D models and accurate 2D annotations would allow the construction of a 3D representation of the landscape and could provide new insights into understanding species microdistribution and biotic interactions.

Abstract In this paper, based on the data augmentation techniques of bounding box augmentation and the road damage generative adversarial network based augmentation, a robust road damage detection method has been presented. To this end, first, Iran road damage dataset has been collected by means of a dashboard‐installed mobile phone. After processing these images by the blind referenceless image spatial quality evaluator technique, the substandard and inferior data have been automatically eliminated. In the second step, based on the YOLOv5 with several different baseline models, an algorithm has been developed for detecting the road surface damages. In the third step, by using the traditional as well as the bounding box augmentation and road damage generative adversarial network based augmentation techniques, the precision and the robustness of road damage detector under different environmental and field conditions have been improved. Finally, through the ensemble of the best models, the final detector accuracy has been enhanced. The findings of this article indicate that by using the proposed approach, the values of mAP and F1‐score are improved by 13.79% and 7.58%, respectively. The dataset and parts of the code are available at: https://github.com/IranRoadDamageDataset/IRRDD.

Digital image collections are becoming increasingly popular due to their ease of use. Still, the need for adequate indexing information makes it difficult for users to find the specific images they need. With the vast number of digital images generated daily, these databases have become enormous, making accurate image retrieval challenging. One of the most challenging tasks in computer vision and multimedia research is image annotation, where keywords are assigned to an image. Unlike humans, computers can measure colors, textures, and shapes of images but fail to interpret them semantically, known as the semantic gap. This makes image annotation complex. For semantic-level concepts generation the raw image pixels provide not enough Unmistakable information. Which mean for of “words” or “sentences” there is no clear definition with the semantics of an image unlike text annotation. Therefore, this study aims to bridge the semantic gap between low-level computer features and human interpretation of images. The proposed enhanced automatic image annotation system maps multiple labels or into single image, providing an in-depth understanding of the visual content’s meaning. This is achieved by combining Convolutional Neural Networks-based multiple features (Y is the green component of the color, Cb and Cr is the blue component and red component called YCbCr color space and Gaussian–Laplacian Pyramid) and neighbors to recall and balance precision. The image annotation (IA) scheme uses a Global Vectors for Word Representation (GloVe) model with CNN-Gaussian–Laplacian Pyramid and learning representation to predict image annotation (IA) accurately. The proposed image annotation (IA) system was execution on three public datasets and showed excellent flexibility of annotation, improved accuracy, and reduced computational costs compared to existing state-of-the-art methods. The image annotation (IA) framework can provide immense benefits in accurately selecting and extracting image features, minimizing computational complexity and facilitating annotation.

Effective image and video annotation is a fundamental pillar in computer vision and artificial intelligence, crucial for the development of accurate machine learning models. Object tracking and image retrieval techniques are essential in this process, significantly improving the efficiency and accuracy of automatic annotation. This paper systematically investigates object tracking and image acquisition techniques. It explores how these technologies can collectively enhance the efficiency and accuracy of the annotation processes for image and video datasets. Object tracking is examined for its role in automating annotations by tracking objects across video sequences, while image retrieval is evaluated for its ability to suggest annotations for new images based on existing data. The review encompasses diverse methodologies, including advanced neural networks and machine learning techniques, highlighting their effectiveness in various contexts like medical analyses and urban monitoring. Despite notable advancements, challenges such as algorithm robustness and effective human-AI collaboration are identified. This review provides valuable insights into these technologies’ current state and future potential in improving image annotation processes, even showing existing applications of these techniques and their full potential when combined.

Point source detection algorithms play a pivotal role across diverse applications, influencing fields such as astronomy, biomedical imaging, environmental monitoring, and beyond. This article reviews the algorithms used for space imaging applications from ground and space telescopes. The main difficulties in detection arise from the incomplete knowledge of the impulse function of the imaging system, which depends on the aperture, atmospheric turbulence (for ground-based telescopes), and other factors, some of which are time-dependent. Incomplete knowledge of the impulse function decreases the effectiveness of the algorithms. In recent years, deep learning techniques have been employed to mitigate this problem and have the potential to outperform more traditional approaches. The success of deep learning techniques in object detection has been observed in many fields, and recent developments can further improve the accuracy. However, deep learning methods are still in the early stages of adoption and are used less frequently than traditional approaches. In this review, we discuss the main challenges of point source detection, as well as the latest developments, covering both traditional and current deep learning methods. In addition, we present a comparison between the two approaches to better demonstrate the advantages of each methodology.

The ANR CROBORA project investigates the influence of stock shots in fostering a shared European cultural imaginary through television and online media, focusing particularly on the selective deployment of visual elements. This data paper centers on an extensive dataset of French television content preserved by the Institut national de l’audiovisuel (INA), covering the period 2001-2021. Enhanced by meticulous manual annotation, the dataset provides a rich basis for examining the semantic nuances of these visual elements. The project aims to uncover patterns in the usage of these images, tracking their evolution and distribution across time and institutional boundaries. Additionally, the CROBORA project plans to offer this dataset on an innovative visual platform equipped with advanced functionalities to support exploratory data analysis, enabling researchers to integrate various keywords and delve into the data more effectively.

The present study advances object detection and tracking techniques by proposing a novel model combining Automated Image Annotation with Inception v2-based Faster RCNN (AIA-IFRCNN). The research methodology utilizes the DCF-CSRT model for image annotation, Faster RCNN for object detection, and the inception v2 model for feature extraction, followed by a softmax layer for image classification. The proposed AIA-IFRCNN model is evaluated on three benchmark datasets: Bird (Dataset 1), UCSDped2 (Dataset 2), and Under Water (Dataset 3), to determine prediction accuracy, annotation time, Center Location Error (CLE), and Overlap Rate (OR). The experimental results indicate that the AIA-IFRCNN model outperformed existing models regarding detection accuracy and tracking performance. Notably, it achieved a maximum detection accuracy of 95.62 % on Dataset 1, outperforming other models. Additionally, it achieved minimum average CLE values of 4.16, 5.78, and 3.54, and higher overlap rates of 0.92, 0.90, and 0.94 on the respective datasets (1, 2 and 3). Hence, this research work on object detection and tracking using the AIA-IFRCNN model is essential for improving system efficiency and fostering innovation in the field of computer vision and object tracking.

Abstract Background With the emergence of deep-learning methods, tools are needed to capture and standardize image annotations made by experimentalists. In developmental biology, cell lineages are generally reconstructed from time-lapse data. However, some tissues need to be fixed to be accessible or to improve the staining. In this case, classical software do not offer the possibility of generating any lineage. Because of their rigid cell walls, plants present the advantage of keeping traces of the cell division history over successive generations in the cell patterns. To record this information despite having only a static image, dedicated tools are required. Results We developed an interface to assist users in the building and editing of a lineage tree from a 3D labeled image. Each cell within the tree can be tagged. From the created tree, cells of a sub-tree or cells sharing the same tag can be extracted. The tree can be exported in a format compatible with dedicated software for advanced graph visualization and manipulation. Conclusions The TreeJ plugin for ImageJ/Fiji allows the user to generate and manipulate a lineage tree structure. The tree is compatible with other software to analyze the tree organization at the graphical level and at the cell pattern level. The code source is available at https://github.com/L-EL/TreeJ .

Object annotation is essential for computer vision tasks, and more high-quality annotated data can effectively improve the performance of vision models. However, manual annotation is time-consuming (annotating a box takes 35s). Recent studies have explored faster automated annotation, among which weakly supervised methods stand out. Weakly supervised methods learn to automatically localize objects in images from weakly labeled annotations, e.g., class tags or points, replacing manual bounding box annotations. Although using a single weakly labeled annotation can reduce a large amount of time, it leads to poor annotation quality, particularly for the complex scenes containing multiple objects. To balance annotation time and quality, we propose a weakly semi-supervised automated annotation method. Its main idea is to incorporate point-labeled and fully labeled annotations into a teacher-student framework for training, to jointly localize the object bounding boxes on all point-labeled images. We also propose two effective techniques within this framework to better use of these mixed annotations. The first is a point-guided sample assignment technique which optimizes the loss calculation. The second is a pseudo-label filtering technique which generate accurate pseudo labels for model training by utilizing the points and boxes localization confidences. Extensive experiments on MSCOCO demonstrate that our method outperforms existing automated annotation methods. In particular, when using 95% point-labeled and 5% fully labeled data, our approach reduces the annotation time by approximately 52% and achieves an annotation quality of 87.4% mIoU. [ABSTRACT FROM AUTHOR]

In this paper, based on the data augmentation techniques of bounding box augmentation and the road damage generative adversarial network based augmentation, a robust road damage detection method has been presented. To this end, first, Iran road damage dataset has been collected by means of a dashboard‐installed mobile phone. After processing these images by the blind referenceless image spatial quality evaluator technique, the substandard and inferior data have been automatically eliminated. In the second step, based on the YOLOv5 with several different baseline models, an algorithm has been developed for detecting the road surface damages. In the third step, by using the traditional as well as the bounding box augmentation and road damage generative adversarial network based augmentation techniques, the precision and the robustness of road damage detector under different environmental and field conditions have been improved. Finally, through the ensemble of the best models, the final detector accuracy has been enhanced. The findings of this article indicate that by using the proposed approach, the values of mAP and F1‐score are improved by 13.79% and 7.58%, respectively. The dataset and parts of the code are available at: https://github.com/IranRoadDamageDataset/IRRDD. [ABSTRACT FROM AUTHOR]

The present study advances object detection and tracking techniques by proposing a novel model combining Automated Image Annotation with Inception v2-based Faster RCNN (AIA-IFRCNN). The research methodology utilizes the DCF-CSRT model for image annotation, Faster RCNN for object detection, and the inception v2 model for feature extraction, followed by a softmax layer for image classification. The proposed AIA-IFRCNN model is evaluated on three benchmark datasets: Bird (Dataset 1), UCSDped2 (Dataset 2), and Under Water (Dataset 3), to determine prediction accuracy, annotation time, Center Location Error (CLE), and Overlap Rate (OR). The experimental results indicate that the AIA-IFRCNN model outperformed existing models regarding detection accuracy and tracking performance. Notably, it achieved a maximum detection accuracy of 95.62 % on Dataset 1, outperforming other models. Additionally, it achieved minimum average CLE values of 4.16, 5.78, and 3.54, and higher overlap rates of 0.92, 0.90, and 0.94 on the respective datasets (1, 2 and 3). Hence, this research work on object detection and tracking using the AIA-IFRCNN model is essential for improving system efficiency and fostering innovation in the field of computer vision and object tracking. [ABSTRACT FROM AUTHOR]

Background: With the emergence of deep-learning methods, tools are needed to capture and standardize image annotations made by experimentalists. In developmental biology, cell lineages are generally reconstructed from time-lapse data. However, some tissues need to be fixed to be accessible or to improve the staining. In this case, classical software do not offer the possibility of generating any lineage. Because of their rigid cell walls, plants present the advantage of keeping traces of the cell division history over successive generations in the cell patterns. To record this information despite having only a static image, dedicated tools are required. Results: We developed an interface to assist users in the building and editing of a lineage tree from a 3D labeled image. Each cell within the tree can be tagged. From the created tree, cells of a sub-tree or cells sharing the same tag can be extracted. The tree can be exported in a format compatible with dedicated software for advanced graph visualization and manipulation. Conclusions: The TreeJ plugin for ImageJ/Fiji allows the user to generate and manipulate a lineage tree structure. The tree is compatible with other software to analyze the tree organization at the graphical level and at the cell pattern level. The code source is available at https://github.com/L-EL/TreeJ. [ABSTRACT FROM AUTHOR]

Digital imaging has become an essential element in every medical institution. Therefore, medical image retrieval such as chest X-ray (CXR) must be improved via novel feature extraction and annotation activities before they are stored into image databases. To date, many methods have been introduced to annotate medical images using spatial relationships after these features are extracted. However, the annotation performance for each method is inconsistent and does not show promising achievement to retrieve images. It is noticed that each method is still struggling with at least two big problems. Firstly, the recommended annotation model is weak because the method does not consider the object shape and rely on gross object shape estimation. Secondly, the suggested annotation model can only be functional for simple object placement. As a result, it is difficult to determine the spatial relationship feature after they are extracted to annotate images accurately. Hence, this study aims to propose a new model to annotate nodule location within lung zone for CXR image with extracted spatial relationship feature to improve image retrieval. In order to achieve the aim, a methodology that consists of six phases of CXR image annotation using the extracted spatial relationship features is introduced. This comprehensive methodology covers all cycles for image annotation tasks starting from image pre-processing until determination of spatial relationship features for the lung zone in the CXR. The outcome from applying the methodology also enables us to produce a new semi-automatic annotation system named CHEXRIARS which acts as a tool to annotate the extracted spatial relationship features in CXR images. The CHEXRIARS performance is tested using a retrieval test with two common tests namely the precision and recall (PNR). Apart from CHEXRIARS, three other annotation methods that are object slope, object projection and comparison of region boundaries are also included in the retrieval performance test. Overall, the CHEXRIARS interpolated PNR curve shows the best shape because it is the closest curve approaching the value of 1 on the X-axis and Y-axis. Meanwhile the value of area under curve for CHEXRIARS also revealed that this system attained the highest score at 0.856 as compared to the other three annotation methods. The outcome from the retrieval performance test indicated that the proposed annotation model has produced outstanding outcome and improved the image retrieval. [ABSTRACT FROM AUTHOR]

Graph neural networks' application in automatic image annotation is becoming more mature. However, there are still several problems. First, the feature data of the original image obtained by the feature extraction algorithm, such as color features and gradient features, all have the problem of slight intra-class variance and significant inter-class variance. Second, merely utilize the graph convolution neural networks to construct samples or labeled graphs, limiting multimodality's fusion and expansion. This paper uses a parallel graph convolution network based on feature fusion for automatic image annotation. By fusing the sample features, the inherent defects of the features extracted by a single model are reduced, and the annotation performance under the condition of semi-supervised learning is improved. Experiments on three benchmark image annotation datasets show that this method is superior to the existing methods. [ABSTRACT FROM AUTHOR]

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Medical image annotation has significant potential to detect multiple tags. To detect specific tags and labels, most of the conventional learning algorithms took difficulty in matching the tag with corresponding region in the medical images. Hence, most of the medical image annotations fail to reproduce discrimination between various classes. In this research work to tackle medical image discrimination, we proposed a lightweight pyramidal feature-specific learning deep learning network. The proposed pyramidal feature-specific Lightweight Convolutional Neural Network (LDCNN) architecture for classifying local visual regions to annotate the classified region. By employing pyramidal learning, the proposed LDCNN align each medical image annotation with its region. The colour conversion makes low computation complexity, since the medical image exhibits degeneration by colour absorption. Hence, the interpretability and classification effectiveness increase. To evaluate the effectiveness and classification accuracy, we compare the proposed LDCNN with AlexNet and EfficientNet on benchmark datasets like MS-COCO, LC25000 and multiclass Kather datasets. Empirical experimental performance index obtained by the proposed LDCNN outperforms baseline convolutional neural network architecture. The proposed LDCNN achieves 99.6% accuracy, 98.4% sensitivity, 97.9% specificity and 99.1% F1 score. Hence, there is a steady improvement in medical annotation efforts and classification by our proposed lightweight feature-specific learning network. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a large‐scale dataset called the Iran Autonomous Driving Dataset (IADD) is presented, aiming to improve the generalization capability of the deep networks outside of their training domains. The IADD focuses on 2D object detection and contains more than 97,000 annotated images, covering six common object classes in the field of autonomous vehicles. To improve the generalization of the models, a wide variety of driving conditions and domains, including the city and suburban road settings, adverse weather conditions, and various traffic flows, are presented in the IADD images. The results of exhaustive evaluations conducted on several state‐of‐the‐art convolutional neural networks reveal that not only the trained architectures have performed successfully on test data of the IADD, but also they have upheld high precision in the assessments of generalization capability. In order to challenge the models, broad range of simulations have been performed in the CARLA software environment; which due to the synthetic nature of the simulated images, severe domain shifts have been observed between the CARLA and the IADD. Also, the cross‐domain evaluation results have confirmed the efficacy of the IADD in enhancing the generalization ability of the deep learning models. The dataset is available in: https://github.com/ahv1373/IADD .

Given that the meaning of an image is rarely self-evident using traditional keyword and/or content-based descriptions, the general goal of this study is to convert, with minimal human intervention, a stream of web vector graphics into a searchable knowledge graph structure that encodes semantically relevant image contents. To do so, we introduce an original framework titled SSG which automatically converts a stream of SVG images and objects into a semantic graph. We introduce an incremental clustering approach to semantically annotate SVG images and their constituent objects in a fast and efficient manner, using an aggregation of shape, area, color, and location similarity measures. We then produce an RDF graph representation of the input image and integrate it in a reference knowledge graph, incrementally extending its semantic expressiveness to improve future annotation tasks. This achieves semantization of vector image contents with minimum human effort and training data, while complying with native Web standards (i.e., SVG and RDF) to preserve transparency in representing and searching images using Semantic Web stack technologies. Our solution is of linear complexity in the number of images and clusters used. We have conducted a large battery of experiments to test and evaluate our approach. We have created a labelled SVG dataset consisting of 22,553 objects from 750 images based on panoramic dental X-ray images. To our knowledge, it is the first significant dataset of labelled SVG objects and images, which we make available online as a benchmark for future research in this area. Results underline our approach's effectiveness, and its applicability in a practical application domain. [ABSTRACT FROM AUTHOR]

Neutrophil extracellular traps (NETs), pathogen-ensnaring structures formed by neutrophils by expelling their DNA into the environment, are believed to play an important role in immunity and autoimmune diseases. In recent years, a growing attention has been put into developing software tools to quantify NETs in fluorescent microscopy images. However, current solutions require large, manually-prepared training data sets, are difficult to use for users without background in computer science, or have limited capabilities. To overcome these problems, we developed Trapalyzer, a computer program for automatic quantification of NETs. Trapalyzer analyzes fluorescent microscopy images of samples double-stained with a cell-permeable and a cell-impermeable dye, such as the popular combination of Hoechst 33342 and SYTOX™Green. The programis designed with emphasis on software ergonomy and accompanied with step-bystep tutorials to make its use easy and intuitive. The installation and configuration of the software takes less than half an hour for an untrained user. In addition to NETs, Trapalyzer detects, classifies and counts neutrophils at different stages of NET formation, allowing for gaining a greater insight into this process. It is the first tool that makes this possible without large training data sets. At the same time, it attains a precision of classification on par with state-of-the-art machine learning algorithms. As an example application, we show how to use Trapalyzer to study NET release in a neutrophil-bacteria co-culture. Here, after configuration, Trapalyzer processed 121 images and detected and classified 16 000 ROIs in approximately three minutes on a personal computer. The software and usage tutorials are available at https://github.com/Czaki/Trapalyzer. [ABSTRACT FROM AUTHOR]

Modern medical technology offers potential for the automatic generation of datasets that can be fed into deep learning systems. However, even though raw data for supporting diagnostics can be obtained with manageable effort, generating annotations is burdensome and time-consuming. Since annotating images for semantic segmentation is particularly exhausting, methods to reduce the human effort are especially valuable. We propose a combined framework that utilizes unsupervised machine learning to automatically generate segmentation masks. Experiments on two biomedical datasets show that our approach generates noticeably better annotations than Otsu thresholding and k-means clustering without needing any additional manual effort. Using our framework, unannotated datasets can be amended with pre-annotations fully unsupervised thus reducing the human effort to a minimum.

With the rise of large-scale data-driven innovation in AI, data annotation tasks found in digital work environments present an employment opportunity for neurodivergent individuals. Though work in data annotation can potentially ease the high unemployment rate of neurodivergent individuals, limited research focuses on the experience of neurodivergent workers in data annotation micro-tasks. To aid in understanding the experience of neurodivergent crowd workers, we conducted a user study with ten neurodivergent workers between the ages of 18–30. Participants completed three types of micro-tasks in a custom web-based data annotation platform. With the data collected from the platform, we examined individual responses within data annotations, work completion, the time to complete work, and calculated a potential “effective hourly wage,” for each participant based on their responses. Through a survey and semi-structured interview following each task, we learned about the experience of all participants regarding each of the data annotation tasks. Results of the study show: 1) our participants provide diverse annotations that are valuable for employers in digital data annotation work environments; 2) when calculating the “effective hourly wage” of all participants per task, some of our participants would earn less than minimum hourly wage on tasks; and 3) participant perceptions of the tasks matched their responses in the tasks presented.