Multi-task Learning for Newspaper Image Segmentation and Baseline Detection Using Attention-Based U-Net Architecture

Bansal, Anukriti; Mukherjee, Prerana; Joshi, Divyansh; Tripathi, Devashish; Singh, Arun

doi:10.1007/978-3-030-86159-9_32

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…For picture segmentation, the image is next submitted to the U‐Net model (Bansal et al, 2021). It is commonly utilized to understand and segment images.…”

Section: The Proposed Modelmentioning

confidence: 99%

Three‐dimensional dental image segmentation and classification using deep learning with tunicate swarm algorithm

Awari

Neelakandan²,

Janagaraj³

et al. 2022

Expert Systems

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Dentistry frequently makes use of intraoral scanning technologies to digitally acquire the three‐dimensional (3D) geometry of teeth. In recent times, dental clinics over the globe utilize used computer aided diagnosis (CAD) models to make treatment plans, for example, orthodontics. Orthodontic CAD system acts as a vital part of the advanced dentistry field. A 3D dental model, computed by patient impression, as input and aids dentist in the extraction, moving, deletion, and rearranging of teeth to simulate treatment output. Tooth segmentation and labelling is the basic and foremost element of the CAD model which needs to be addressed. Automated segmentation and classification of 3D dental images using advanced machine learning and deep learning (DL) models become essential. This article introduces a new 3D dental image segmentation and classification using DL with tunicate swarm algorithm (3DDISC‐DLTSA) model. The major intention of the 3DDISC‐DLTSA system is to segment the tooth model and identify seven distinct tooth types. To accomplish this, the presented 3DDISC‐DLTSA model performs image pre‐processing in two stages namely image filtering and U‐Net segmentation. In addition, the 3DDISC‐DLTSA model derives DenseNet‐169 model for feature extraction purposes. For the recognition and classification of tooth type, the TSA based hyperparameter tuning process is carried out which helps to accomplish maximum classification performance. A wide range of experimental analyses is performed and the outcomes are inspected under many aspects. On dataset‐1, 3DDISC‐DLTSA model accuracy rose by 96.67%. On dataset‐3, 3DDISC‐DLTSA model accuracy rose by 97.48% and algorithm accuracy by 97.35%. The 3DDISC‐DLTSA model outperformed more modern models, according to the comparative investigation.

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“…For picture segmentation, the image is next submitted to the U‐Net model (Bansal et al, 2021). It is commonly utilized to understand and segment images.…”

Section: The Proposed Modelmentioning

confidence: 99%

Three‐dimensional dental image segmentation and classification using deep learning with tunicate swarm algorithm

Awari

Neelakandan²,

Janagaraj³

et al. 2022

Expert Systems

View full text Add to dashboard Cite

show abstract

Deep handwritten diagram segmentation

Pravalpruk,

Dailey

2024

IET Image Processing

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Handwriting is a natural way to communicate and exchange ideas, but converting handwritten diagrams to application‐specific digital formats requires skill and time. Automatic handwritten document conversion can save time, but diagrams and text require different recognition engines. Since accurate segmentation of handwritten diagrams can improve the accuracy of later diagram recognition steps, the authors propose to solve the problem of segmentation of text and non‐text elements of handwritten diagrams using deep semantic segmentation. The model, DeepDP is a flexible U‐net style architecture that can be tuned in complexity to a level appropriate for a particular dataset and diagram type. Experiments on a public hand‐drawn flowchart dataset and a business process diagram dataset show excellent performance, with a per pixel accuracy of 98.6% on the public flowchart datasets and improvement over the 99.3% text stroke accuracy and 96.6% non‐text stroke accuracy obtained by state of the art methods that use online stroke information. On the smaller offline business process diagram dataset, the method obtains a per‐pixel accuracy of 96.9%.

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Multi-task Learning for Newspaper Image Segmentation and Baseline Detection Using Attention-Based U-Net Architecture

Cited by 2 publications

References 21 publications

Three‐dimensional dental image segmentation and classification using deep learning with tunicate swarm algorithm

Three‐dimensional dental image segmentation and classification using deep learning with tunicate swarm algorithm

Deep handwritten diagram segmentation

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