COCO Annotator

Stefanics, Daniela; Fox, Markus

doi:10.1145/3578495.3578502

Cited by 11 publications

(6 citation statements)

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“…For this purpose, various forms of annotators have been used, viz. Openlabelling [67], LabelMe [68], labelImg [69], imglab [70], ImageTagger [71], computer vision annotation tool (CVAT) [72], common objects in context (COCO) annotator [73], visual object tagging tool (VoTT) [74], VGG image annotator (VIA) [75], and and Yolo_mark [76]. It should be noted that usage of such annotators is suitable for smaller datasets and not for bigger-sized datasets.…”

Section: Dataset Of Pamentioning

confidence: 99%

Sustainability insights on learning-based approaches in precision agriculture in internet-of-things

Panduranga,

Ranganathasharma

2024

IJECE

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Precision agriculture (PA) is meant to automate the complete agricultural processes with the sole target of enhanced crop yield with reduced cost of operation. However, deployment of PA in internet of things (IoT) based architecture demands solutions towards addressing various challenges where most are related to proper and precise predictive management of agricultural data. In this perspective, it is noted that learning-based approaches have made some contributory success towards addressing different variants of issues in PA; however, such methods suffer from certain loopholes, primarily related to the non-inclusion of practical constraints of IoT infrastructure in PA and lack of emphasis towards bridging the trade-off between higher accuracy and computational burden that is eventually associated with this. This paper contributes towards highlighting the strengths and weaknesses of recent learning approaches and contributes towards novel findings.

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Section: Dataset Of Pamentioning

confidence: 99%

Sustainability insights on learning-based approaches in precision agriculture in internet-of-things

Panduranga,

Ranganathasharma

2024

IJECE

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“…Therefore, we contribute to expanding the dataset by developing segmentation for orthodontic appliances and introducing three more classes, namely bands, brackets, and retainers. This process was under a supervision of a dentist using the COCO-Annotator tool [15]. We attended weekly meetings where related issues and questions were discussed.…”

Section: Experiments 31 Datasetmentioning

confidence: 99%

Self-Supervised Learning with Masked Image Modeling for Teeth Numbering, Detection of Dental Restorations, and Instance Segmentation in Dental Panoramic Radiographs

Almalki

Latecki

2023

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

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The computer-assisted radiologic informative report has received increasing research attention to facilitate diagnosis and treatment planning for dental care providers. However, manual interpretation of dental images is limited, expensive, and time-consuming. Another barrier in dental imaging is the limited number of available images for training, which is a challenge in the era of deep learning. This study proposes a novel self-distillation (SD) enhanced self-supervised learning on top of the masked image modeling (SimMIM) Transformer, called SD-SimMIM, to improve the outcome with a limited number of dental radiographs. In addition to the prediction loss on masked patches, SD-SimMIM computes the self-distillation loss on the visible patches. We apply SD-SimMIM on dental panoramic X-rays for teeth numbering, detection of dental restorations and orthodontic appliances, and instance segmentation tasks. Our results show that SD-SimMIM outperforms other self-supervised learning methods. Furthermore, we augment and improve the annotation of an existing dataset of panoramic X-rays.

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“…It should be noted that the training stage of the deep learning models does not use images from copper-surfaced DA-G20 biofilms in order to evaluate their robustness. With the help of subject matter experts at the 2D-BEST center, we label the bacterial cells and backdrop in the biofilm picture datasets using the COCO Annotator tool [46]. Then, we used that labeling dataset for training, validating, and testing the model.…”

Section: Bacterial Cell Segmentation Via Yolactmentioning

confidence: 99%

BiofilmScanner: A Computational Intelligence Approach to Obtain Bacterial Cell Morphological Attributes from Biofilm Image

Rahman¹,

Azam²,

Hossen³

et al. 2023

Preprint

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Desulfovibrio alaskensis G20 (DA-G20) is utilized as a model for sulfate-reducing bacteria (SRB) that are associated with corrosion issues caused by microorganisms. SRB-based biofilms are thought to be responsible for the billion-dollar-per-year bio-corrosion of metal infrastructure. Understanding the extraction of the bacterial cells' shape and size properties in the SRB-biofilm at different growth stages will assist with the design of anti-corrosion techniques. However, numerous issues affect current approaches, including time-consuming geometric property extraction, low efficiency, and high error rates. This paper proposes BiofilScanner, a Yolact-based deep learning method integrated with invariant moments to address these problems. Our approach efficiently detects and segments bacterial cells in an SRB image while simultaneously invariant moments measure the geometric characteristics of the segmented cells with low errors. The numerical experiments of the proposed method demonstrate that the BiofilmScanner is 2.1x and 6.8x faster than our earlier Mask-RCNN and DLv3+ methods for detecting, segmenting, and measuring the geometric properties of the cell. Furthermore, the BiofilmScanner achieved an F1-score of 85.28% while Mask-RCNN and DLv3+ obtained F1-scores of 77.67% and 75.18%, respectively.

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COCO Annotator

Cited by 11 publications

References 0 publications

Sustainability insights on learning-based approaches in precision agriculture in internet-of-things

Sustainability insights on learning-based approaches in precision agriculture in internet-of-things

Self-Supervised Learning with Masked Image Modeling for Teeth Numbering, Detection of Dental Restorations, and Instance Segmentation in Dental Panoramic Radiographs

BiofilmScanner: A Computational Intelligence Approach to Obtain Bacterial Cell Morphological Attributes from Biofilm Image

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