Machine learning and its applications

Arora, Vinay; Ng, Eddie Yin-Kwee; Singh, Arshdeep

doi:10.1016/b978-0-323-90789-7.00002-6

Smart Electrical and Mechanical Systems 2022

DOI: 10.1016/b978-0-323-90789-7.00002-6

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Machine learning and its applications

Vinay Arora

Eddie Yin-Kwee Ng

Arshdeep Singh

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Cited by 3 publications

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“…To obtain HRMS images, image processing is needed. Image processing applies procedures to an image to enhance it or derive valuable information from it [6]. As a remote sensing image processing technique, pansharpening sharpens LRMS images using their corresponding PAN images to produce HRMS images.…”

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confidence: 99%

Pansharpening via Multiscale Embedding and Dual Attention Transformers

Fan,

Liu,

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Pansharpening is a fundamental and crucial image processing task for many remote sensing applications, which generates a high-resolution multispectral image by fusing a lowresolution multispectral image and a high-resolution panchromatic image. Recently, vision transformers have been introduced into the pansharpening task for utilizing global contextual information. However, long-range and local dependencies modeling and multiscale feature learning are all essential to the pansharpening task. Learning and exploiting these various information raises a big challenge and limits the performance and efficiency of existing pansharpening methods. To solve this issue, we propose a pansharpening network based on multiscale embedding and dual attention transformers (MDPNet). Specifically, a multiscale embedding block is proposed to embed multiscale information of the images into vectors. Thus, transformers only need to process a multispectral embedding sequence and a panchromatic embedding sequence to efficiently use multiscale information. Furthermore, an additive hybrid attention transformer is proposed to fuse the embedding sequences in an additive injection manner. Finally, a channel self-attention transformer is proposed to utilize channel correlations for high-quality detail generation. Experiments over QuickBird and WorldView-3 datasets demonstrate the proposed MDPNet outperforms state-of-the-art methods visually and quantitatively with low running time. Ablation studies further verify the effectiveness of the proposed multiscale embedding and transformers in pansharpening.

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confidence: 99%

Pansharpening via Multiscale Embedding and Dual Attention Transformers

Fan,

Liu,

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Machine Learning for Predicting and Optimizing Physicochemical Properties of Deep Eutectic Solvents: Review and Perspectives

López-Flores,

Ramírez-Márquez,

González-Campos

et al. 2024

Ind. Eng. Chem. Res.

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Comparative Analysis of Object Classification Algorithms: Traditional Image Processing Versus Artificial Intelligence – Based Approach

Rosca

2023

JPGT

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In the current era of advanced digital technologies, form recognition is integrated into numerous applications, from computer vision to industrial automation. This paper focuses on a comparative analysis of two distinct form recognition algorithms, namely harnessing the power of artificial intelligence (AI) and image processing techniques. The research is motivated by the need to address the trade-off between speed and complexity in form recognition, with a center on real-world applicability. Traditional image processing-based form recognition approaches often require complex coding, substantial domain expertise, and significant computational resources. This complexity can hinder rapid adaptation to changing requirements and the addition of new forms. The aim is to explore whether AI-powered algorithms can offer a more efficient and versatile alternative, reducing the barriers to entry for form recognition tasks. The primary goal of the paper is to compare the performance of AI-based form recognition with image processing-based methods in terms of speed and accuracy. The second goal is to assess the ease of adapting AI-based algorithms to new forms without extensive code changes. Two form recognition algorithms were designed and implemented, one based on artificial intelligence and a second relying on image processing. The AI-powered algorithm uses neural network architecture trained on a predefined dataset of forms. The image processing algorithm employs edge detection and contour analysis techniques.

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Machine learning and its applications

Cited by 3 publications

References 59 publications

Pansharpening via Multiscale Embedding and Dual Attention Transformers

Pansharpening via Multiscale Embedding and Dual Attention Transformers

Machine Learning for Predicting and Optimizing Physicochemical Properties of Deep Eutectic Solvents: Review and Perspectives

Comparative Analysis of Object Classification Algorithms: Traditional Image Processing Versus Artificial Intelligence – Based Approach

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