A multi-class driver behavior dataset for real-time detection and road safety enhancementdoi: 10.5281/zenodo.14908802. by Arafat Sahin Afridi, Arafath Kafy, Ms. Nazmun Nessa Moon, Md. Shahriar Shakil

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Automated Seedling Contour Determination and Segmentation Using Support Vector Machine and Image Features by Samsuzzaman, Md Nasim Reza, Sumaiya Islam, Kyu-Ho Lee, Md Asrakul Haque, Md Razob Ali, Yeon Jin Cho, Dong Hee Noh, Sun-Ok Chung

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Tomato leaf dataset: A dataset for multiclass disease detection and classificationMendeley Data by Ahmed Imtiaz, Fahad Bin Islam Swapnil, Syed Rayhan Masud, Debajyoti Karmaker

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Quality Assessment of Plain Chest X-Rays of the Expatriates in Jordan: Multi-Centres Study by Ayasrah M, Al Hrout RA, Rawahneh K

“…A quantitative classification approach was used to assess CXR for ACR and EC compliance, which included exposure, anatomical visibility, organ superimposition, and image annotation.Results: Only 15% of CXRs passed all of the quality standards. …”
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Bangladeshi Vehicle Classification and Detection Using Deep Convolutional Neural Networks With Transfer Learning by Farid, Proshanta Kumer Das, Monirul Islam, Ebna Sina

“…Next, we have customised the LabelImg image annotation tool to improve the following features: (a) Changing Label Font & Border, (b) Detecting Incorrect Labels, (c) Abbreviating Label Names, (d) Improving Crosshair & Bounding Box Guide, (e) Adding Progress Information, and (f) Improving File List Panel. …”
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The Bangladesh road traffic sign dataset in real-world images for traffic sign recognitionzenodo by Md. Ariful Islam, Dewan Md. Farid

“…A Total of 31 distinct traffic sign images were collected including Crossroad, Emergency Stopping, Sharp left turn. For image annotation, a sophisticated tool, Roboflow, has been utilized and data augmentation techniques have been applied to enhance the diversity of the images. …”
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Pseudo label refining for semi-supervised temporal action localization. by Lingwen Meng, Guobang Ban, Guanghui Xi, Siqi Guo

“…Video annotation is more tedious and time-consuming compared with image annotation. Therefore, the semi-supervised method that combines labeled and unlabeled data for joint training has attracted increasing attention from academics and industry. …”
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A photogrammetric image-point dataset for the semantic segmentation of heritage buildingsZenodo by Eugenio Pellis, Andrea Masiero, Michele Betti, Grazia Tucci, Pierre Grussenmeyer

“…The annotation of the point clouds was performed manually, while the image annotation was generated through a projection process based on the labels assigned to the point clouds.…”
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Significant reduction in manual annotation costs in ultrasound medical image database construction through step by step artificial intelligence pre-annotation. by Fu Zheng, Liu XingMing, Xu JuYing, Tao MengYing, Yang BaoJian, Shan Yan, Ye KeWei, Lu ZhiKai, Huang Cheng, Qi KeLan, Chen XiHao, Du WenFei, He Ping, Wang RunYu, Ying Ying, Bu XiaoHui

“…This study investigates the feasibility of reducing manual image annotation costs in medical image database construction by utilizing a step by step approach where the Artificial Intelligence model (AI model) trained on a previous batch of data automatically pre-annotates the next batch of image data, taking ultrasound image of thyroid nodule annotation as an example. …”
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Epiphytic diatom community structure and richness is determined by macroalgal host and location in the South Shetland Islands (Antarctica). by Andrea M Burfeid-Castellanos, Rafael P Martín-Martín, Michael Kloster, Carlos Angulo-Preckler, Conxita Avila, Bánk Beszteri

“…We used a non-acidic method for diatom digestion, followed by slidescanning and diatom identification by manual annotation through a web-browser-based image annotation platform. Epiphytic diatom species richness was higher on Deception Island as a whole, whereas individual macroalgal specimens harboured richer diatom assemblages on Livingston Island. …”
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HOLa: HoloLens Object Labeling by Schwimmbeck Michael, Khajarian Serouj, Holzapfel Konstantin, Schmidt Johannes, Remmele Stefanie

“…HOLa does not have to be adjusted to a specific image appearance and could thus alleviate AR research in any application field.We evaluate HOLa for different degrees of image complexity in open liver surgery and in medical phantom experiments. Using HOLa for image annotation can increase the labeling speed by more than 500 times while providing Dice scores between 0.875 and 0.982, which are comparable to human annotators. …”
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Classification of Fritillaria thunbergii appearance quality based on machine vision and machine learning technology by DONG Chengye, LI Dongfang, FENG Huaiqu, LONG Sifang, XI Te, ZHOU Qin’an, WANG Jun

“…In order to classify the appearance quality level of Fritillaria thunbergii, the F. thunbergii dataset was constructed with the DigiEye system followed by an image annotation tool. Several statistical learning and object detection algorithms were selected to train and test the F. thunbergii dataset. …”
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Advancing fishery dependent and independent habitat assessments using automated image analysis: A fisheries management agency case study. by Scott N Evans, Bronson Philippa, Carlo Mattone, Nick Konzewitsch, Renae K Hovey, Marcus Sheaves, Gary A Kendrick, Lynda M Bellchambers

“…In this study, we develop and evaluate two residual network-based automatic image annotation models to process fishery specific habitat data to support ecosystem-based fisheries management in the Exmouth Gulf Prawn Managed Fishery in Western Australia. …”
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Recent Progress in Ocean Intelligent Perception and Image Processing and the Impacts of Nonlinear Noise by Huayu Liu, Ying Li, Tao Qian, Ye Tang

“…It also reviewed adaptive image processing processes and their critical support for ocean image recognition and detection, such as image annotation, feature enhancement, and image segmentation. …”
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APPROACH TO IMAGE ANALYSIS FOR COMPUTER VISION SYSTEMS by N. A. Iskra

“…Further, the experiments on resulting model interpretation were conducted, namely image annotation, i.e. generating a textual description of the image content. …”
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Enhancing DeepLabv3+ Convolutional Neural Network Model for Precise Apple Orchard Identification Using GF-6 Remote Sensing Images and PIE-Engine Cloud Platform by Guining Gao, Zhihan Chen, Yicheng Wei, Xicun Zhu, Xinyang Yu

“…The innovation of this research lies in combining image annotation and object-oriented methods during training, improving annotation efficiency and accuracy. …”
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A new dataset for measuring the performance of blood vessel segmentation methods under distribution shifts. by Matheus Viana da Silva, Natália de Carvalho Santos, Julie Ouellette, Baptiste Lacoste, Cesar H Comin

“…This is especially true for blood vessel segmentation since one or more specialists are usually required for image annotation, and creating ground truth labels for just a single image can take up to several hours. …”
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Automatic Leather Defect Detection and Classification Using Single-Channel and Multi-Channel Neural Networks by Majda Curtic-Hodzic, Aldina Ajkunic, Emir Sokic, Almir Salihbegovic, Lejla Arapovic, Nedim Osmic, Samim Konjicija

“…The evaluation was carried out using a combination of a custom-developed dataset and publicly available datasets, assessed with standard performance metrics. Moreover, an image annotation tool was developed to facilitate precise defect labeling and the creation of variable-size datasets. …”
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Visual explainability of 250 skin diseases viewed through the eyes of an AI‐based, self‐supervised vision transformer—A clinical perspective by Ramy Abdel Mawgoud, Christian Posch

“…Recently, self‐supervised (SS) Vision Transformers have emerged, capturing complex visual patterns in hundreds of classes without any need for tedious image annotation. Objectives This study aimed to form the basis for an inexpensive and explainable AI system, targeted at the vastness of clinical skin diagnoses by comparing so‐called ‘self‐attention maps’ of an SS and a supervised ViT on 250 skin diseases—visualizations showing areas of interest for each skin disease. …”
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High performance with fewer labels using semi-weakly supervised learning for pulmonary embolism diagnosis by Zixuan Hu, Hui Ming Lin, Shobhit Mathur, Robert Moreland, Christopher D. Witiw, Laura Jimenez-Juan, Matias F. Callejas, Djeven P. Deva, Ervin Sejdić, Errol Colak

“…Abstract This study proposes a semi-weakly supervised learning approach for pulmonary embolism (PE) detection on CT pulmonary angiography (CTPA) to alleviate the resource-intensive burden of exhaustive medical image annotation. Attention-based CNN-RNN models were trained on the RSNA pulmonary embolism CT dataset and externally validated on a pooled dataset (Aida and FUMPE). …”
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