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7th International Conference on Mechanical and Aerospace Engineering (ICMAE)

作者： Feetham, Luke Aouf, Nabil Dubois-Matra, Olivier Bourdarias, Clement Cranfield Univ Ctr Elect Warfare Shrivenham MK43 0AL Oxon England ESA ESTEC Nav & Control Sect Noordwijk Netherlands Airbus Def & Space Mureaux France

ISBN: (纸本)9781467388290

This paper describes the acquisition of a collection of representative image datasets for use in the development of autonomous planetary descent and landing navigation systems. These datasets were recently obtained using the VISILAB test- bench at ESA-ESTEC in The Netherlands, and represent realistic powered descent trajectories over a Lunar-type surface, but can also be relevant to a wide range of other mission types. The datasets will be made available upon request for research purposes by the academic community.

关键词： autonomous planetary landing image datasets pin-point landing hazard detection digital elevation model computer vision

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A survey of historical document image datasets

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INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION 2022年第4期25卷 305-338页

作者： Nikolaidou, Konstantina Seuret, Mathias Mokayed, Hamam Liwicki, Marcus Lulea Univ Technol EISLAB Machine Learning Grp Aurorum 1 S-97187 Lulea Norrbotten Sweden Friedrich Alexander Univ Pattern Recognit Lab Comp Vis Grp Martensstr 3 D-91058 Erlangen Bavaria Germany

This paper presents a systematic literature review of image datasets for document image analysis, focusing on historical documents, such as handwritten manuscripts and early prints. Finding appropriate datasets for historical document analysis is a crucial prerequisite to facilitate research using different machine learning algorithms. However, because of the very large variety of the actual data (e.g., scripts, tasks, dates, support systems, and amount of deterioration), the different formats for data and label representation, and the different evaluation processes and benchmarks, finding appropriate datasets is a difficult task. This work fills this gap, presenting a meta-study on existing datasets. After a systematic selection process (according to PRISMA guidelines), we select 65 studies that are chosen based on different factors, such as the year of publication, number of methods implemented in the article, reliability of the chosen algorithms, dataset size, and journal outlet. We summarize each study by assigning it to one of three pre-defined tasks: document classification, layout structure, or content analysis. We present the statistics, document type, language, tasks, input visual aspects, and ground truth information for every dataset. In addition, we provide the benchmark tasks and results from these papers or recent competitions. We further discuss gaps and challenges in this domain. We advocate for providing conversion tools to common formats (e.g., COCO format for computer vision tasks) and always providing a set of evaluation metrics, instead of just one, to make results comparable across studies.

关键词： Historical documents image datasets Document image analysis Machine learning

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Semi-automatic Approach to Create Fish image datasets for Aquaculture Applications 15

Semi-automatic Approach to Create Fish Image Datasets for Aq...

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15th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Crescitelli, Alberto Maximiliano Gansel, Lars Christian Zhang, Houxiang Norwegian Univ Sci & Technol Dept Biol Sci Alesund Norway Norwegian Univ Sci & Technol Dept Ocean Operat & Civil Engn Alesund Norway

ISBN: (纸本)9781728151694

Continuous monitoring of wild fish populations and their interaction with farmed fish is important, not only for marine biologists, but also for the aquaculture sector. Nowadays many efforts are made in trying to recognize fish species underwater effectively. Different techniques of computer vision and deep learning are being proposed to solve this problem, but only few benchmarks of fish species images to train and test these algorithms are publicly available. Furthermore, most of these datasets do not include species that are related to aquaculture. Big datasets of images are usually created manually, which can he highly time consuming. This paper presents preliminary results of a system developed to create large image datasets of fish species semi-automatically. The system combines simple techniques of image processing with the state of the art of deep neural networks in an iterative process to extract, label and annotate images from video sources. In order to validate the system, video samples were taken inside and outside cages of a fish farm in Norway. A set of experiments were conducted for two species: salmon and saithe. A small dataset of 200 images for each of the species, as a lest case, was successfully created.

关键词： image datasets Fish detection Object recognition Marine aquaculture applications

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A Geometrical Characterization on Feature Density of image datasets

A Geometrical Characterization on Feature Density of Image D...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Liang, Zhen Zhao, Changyuan Liu, Wanwei Xue, Bai Yang, Wenjing Natl Univ Def Technol Inst Quantum Informat Changsha Peoples R China Natl Univ Def Technol State Key Lab High Performance Comp Changsha Peoples R China Chinese Acad Sci Inst Software Beijing Peoples R China Natl Univ Def Technol Coll Comp Sci & Technol Changsha Peoples R China

ISBN: (纸本)9781665468916

Recently, the interpretability and verification of deep learning have attracted enormous attention from both academic and industrial communities, aiming to gain users' trust and ease their concerns. To guide learning procedures or data operations carried out in a more interpretable way, in this paper, we put a similar perspective on image datasets, the inputs of deep learning. Based on manifold learning, we work out an interpretable geometrical characterization on the curvity of manifolds to depict the feature density of datasets, which is represented with the ratio of the Euclidean distance and the geodesic distance. It is a noteworthy characteristic of image datasets and we take the dataset compression and enhancement problems as application instances via sample credit assignment with the geometrical information. Experiments on typical image datasets have justified the effectiveness and enormous prospect of the presented geometrical characteristic.

关键词： geometrical characterization feature density image datasets interpretability curvity

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Scarcity of publicly available oral cancer image datasets for machine learning research

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ORAL ONCOLOGY 2022年第0期126卷 105737-105737页

作者： Sengupta, Namrata Sarode, Sachin C. Sarode, Gargi S. Ghone, Urmi Dr DY Patil Vidyapeeth Dr DY Patil Dent Coll & Hosp Dept Oral Pathol & Microbiol Pune 411018 Maharashtra India

Publicly available image datasets of pathologies have easy accessibility and thus, are increasingly being used in the field of machine learning and medical diagnosis. As oral cancer is the most common head and neck cancer with an increasing incidence rate, it is of paramount importance to know the status of publicly available datasets. We designed a systematic search (PubMed, Google Scholar, Google Dataset Search, and Google) to identify the publicly available oral cancer image datasets. After carefully screening 332 articles/datasets, only one met the selection criteria and was available publicly. However, it contained images of cancerous lesions of only lips and tongue. This first-of-its-kind analysis made realize a dire need for publicly available datasets in oral cancer. It will help researchers in the development of effective machine learning algorithms for oral cancer.

关键词： Oral cancer Machine learning image datasets Diagnosis

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PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels

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PATTERN RECOGNITION 2025年 161卷

作者： Zhang, Qian Zhu, Yi Cordeiro, Filipe R. Chen, Qiu Jiangsu Open Univ Sch Informat Technol Nanjing 210036 Peoples R China Kogakuin Univ Grad Sch Engn Dept Elect Engn & Elect Tokyo 1638677 Japan Univ Fed Rural Pernambuco Dept Comp Visual Comp Lab Recife Brazil

Large-scale image datasets frequently contain unavoidable noisy labels, resulting in overfitting in deep neural networks and declining performance. Most existing methods for learning from noisy labels operate as one-stage frameworks, where training data division and semi-supervised learning (SSL) are intertwined for optimization. Accordingly, their effectiveness is significantly influenced by the precision of the separated clean set, prior knowledge of noise, and the robustness of SSL. In this paper, we propose a progressive sample selection framework with contrastive loss for noisy labels named PSSCL. This framework operates in two stages, using robust and contrastive losses to augment the robustness of the model. Stage I focuses on identifying a small clean set through a long-term confidence detection strategy, while stage II aims to enhance performance by expanding this clean set. PSSCL demonstrates significant improvement across various benchmarks when compared with state-of-the-art methods. The code is available at https://***/LanXiaoPang613/PSSCL.

关键词： Noisy label Deep neural networks Semi-supervised learning image datasets

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Comparative Study on Evaluating the Performance of Automated Bacterial Colony Counting with Available APP and Software on Generated image Dataset

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SN Computer Science 2025年第4期6卷 1-16页

作者： Arora, Prachi Tewary, Suman Krishnamurthi, Srinivasan Kumari, Neelam School of Computing Indian Institute of Information Technology Una (IIIT-Una) Una 177209 India Academy of Scientific and Innovative Research (AcSIR) Ghaziabad 201002 India Thin Film Coating Facility CSIR-Central Scientific Instruments Organisation (CSIR-CSIO) Sector 30-C Chandigarh 160030 India Materials Science and Sensor Applications CSIR-Central Scientific Instruments Organisation (CSIR-CSIO) Sector 30-C Chandigarh 160030 India Advanced Materials and Processes Division CSIR-National Metallurgical Laboratory (CSIR-NML) Jamshedpur 831007 India MTCC-Gene bank CSIR-Institute of Microbial Technology (CSIR-IMTECH) Sector 39-A Chandigarh 160039 India

Recent developments in image analysis and interpretation using computer vision techniques have shown potential for novel applications in microbiology laboratories to support the task of automation, aiming for faster and more reliable detection. image processing techniques and machine learning models can be valuable tools in the screening process, helping technicians spend less time classifying no-growth results and quickly separating the categories for further analysis. In this context, creating a dataset of different bacterial strain images is a fundamental objective for developing and improving the accuracy of image processing models. Therefore, this manuscript acquired a dataset of water samples with different bacterial strain images on a petri dish following a standardized process with controlled conditions of positioning and lighting. The image acquisition device was also developed with a light-emitting diode (LED) and diffuser as a lighting source and a smartphone camera with 16 MP resolution. In addition, the present manuscript also focuses on comparing the accuracy of the proposed algorithm with the available apps and software using the custom-built imaging device. Hence, the resulting dataset consists of 100 images, which is helpful for researchers working in image processing to develop an algorithm for automated counting of bacterial colonies on petri dishes. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bacterial colonies image datasets image processing Imaging device Segmentation Water samples

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Recent review on image clustering

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IET image PROCESSING 2015年第11期9卷 1020-1032页

作者： Ahmed, Nasir Pakistan Inst Engn & Appl Sci Dept Comp & Informat Sci Islamabad Pakistan

In this review, image clustering problem is discussed starting from global learning based clustering approaches such as Kmeans to the recent challenges in this domain. In global learning based clustering models, cluster evaluation criteria was formulated on whole image datasets. In local learing based clustering models, local neighbourhood information in image data matrices were utilised. However, performances of local learning based clustering models may face limitations for image datasets that contain images with pose, illumination, or occlusion variations. Further, both global learning and local learning approaches were combined in clustering models. Still, image clustering performances are not significantly improved. I elaborate challenges in image clustering problem by categorising image datasets as Gaussian-like or multimodal. Clustering performances on 14 benchmark image datasets of almost all state-of-the-art clustering models are optimal only for Gaussian-like image datasets. Thus, image clustering performance has direct correlation with the distribution of image datasets. Further, by employing optimal image descriptor, clustering performances are data dependent. Almost all exiting clustering models are based on second-order statistics. Owing to which, multimode image patterns may not be effectively handled even by exploiting both local and global information in image data matrices.

关键词： image processing reviews learning (artificial intelligence) review image clustering problem global learning based clustering approaches global learning based clustering models cluster evaluation criteria image datasets nonlinear manifold image data matrices illumination occlusion learning based clustering models image clustering performance second-order statistics multimode image patterns

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Dense-PU: Learning a Density-Based Boundary for Positive and Unlabeled Learning

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IEEE ACCESS 2024年 12卷 90287-90298页

作者： Sevetlidis, Vasileios Pavlidis, George Mouroutsos, Spyridon G. Gasteratos, Antonios Univ Campus Kimmeria Athena Res Ctr Xanthi 67100 Greece Democritus Univ Thrace Dept Elect & Comp Engn Univ Campus Kimmeria Xanthi 67100 Greece Democritus Univ Thrace Dept Prod & Management Engn Xanthi 67100 Greece

In this study, a novel approach for solving the PU learning problem is proposed based on an anomaly detection strategy. A Convolutional Autoencoder (CAE) is used to extract latent encodings from positive-labeled data, which are then linearly combined to acquire new samples that lie between them. These new samples were used as embeddings to define a boundary that approximates the positive class. Data points that were significantly different from the majority of the data were assumed to be negative samples. Once a set of negative samples is obtained, the problem can be treated as a typical binary-classification problem. This approach was evaluated using benchmark image datasets, CIFAR-10 and Fashion-MNIST, yielding F1-scores of 91.96% and 94.80% on the two datasets respectively. These results demonstrate the efficacy of Dense-PU in enhancing classification performance in identifying negative samples in unlabeled data.

关键词： Training Encoding Task analysis Labeling image reconstruction Anomaly detection Supervised learning image analysis Machine learning AI PU-learning weakly supervised learning image datasets machine learning anomaly detection

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A survey on analysis of human faces and facial expressions datasets

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2020年第3期11卷 553-571页

作者： Khan, Gulraiz Samyan, Sahar Khan, Muhammad Usman Ghani Shahid, Muhammad Wahla, Samyan Qayyum Univ Engn Al khawarizmi Inst Comp Sci & Technol Lahore Pakistan

Facial expressions are the basic input for visual emotion detection. They are of great importance in computer vision society. In last decade, substantial amount of work has been done in the field of facial expressions datasets. This survey covers all of the publically available databases in detail and provides necessary information about these sets. This review delivers comprehensive support to researchers in selection of their desired dataset. The datasets are organized in decreasing order of their importance with respect to diversity in expressions, poses, number of images and resolution. This survey also provides comprehensive tabular comparison of different face based databases.

关键词： Emotion Face Facial expressions image datasets Video datasets

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