Frequency-Dividing Downsampling Module of the Lifting Scheme for Image Classification

Wang, Zhichao; Shi, Zishan; Wang, Dingwen; He, Chu

doi:10.1109/icme52920.2022.9859671

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Current cutting-edge research in computer vision covers the fields of object detection and tracking [1,2], image classification [3] etc. On the other hand, in view of the hardware limitations of camera devices, image stitching has emerged as a traditional and challenging task in the field of computer vision, and is widely used in applications such as disease observation [4][5][6], virtual reality [7,8] and autonomous driving [9,10].…”

Section: Introductionmentioning

confidence: 99%

Multi‐scale homography estimation based on dual feature aggregation transformer

Chen

Sun

et al. 2022

IET Image Processing

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The accuracy of registration in image stitching task directly affects the performance of subsequent stages. Traditional registration methods rely heavily on the quality of the features when calculating the homography matrix, resulting in alignment failures in low‐texture or low‐overlap scenes due to extracting insufficient features. On the other hand, existing DNN‐based methods for homography estimation are more robust in multiple scenes but previous work usually employs an overly simple convolutional network structure to directly regress the homography, ignoring the redundant information contained in the feature maps so that their prediction accuracy is inferior to the traditional methods in simple scenarios. To overcome the disadvantages of the two methods, a Multi‐scale structure is proposed to extract feature maps at three scales and design two modules to handle the matrix prediction respectively. The DFA‐T module analyzes semantic information on the high‐level features to accomplish coarse‐grained alignment while the Contextual Correlation module on the bottom level to accomplish more accurate alignment. Experiments demonstrate that this method provides more accurate alignment results than the existing state‐of‐the‐art DNN‐based methods and outperforms traditional algorithms with more stable results in some extreme scenarios.

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Section: Introductionmentioning

confidence: 99%

Multi‐scale homography estimation based on dual feature aggregation transformer

Chen

Sun

et al. 2022

IET Image Processing

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Texture classification network integrating adaptive wavelet transform

Yu,

He,

Wang

et al. 2024

Int. J. Wavelets Multiresolut Inf. Process.

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Graves’ disease is a common condition that is diagnosed clinically by determining the smoothness of the thyroid texture and its morphology in ultrasound images. Currently, the most widely used approach for the automated diagnosis of Graves’ disease utilizes Convolutional Neural Networks (CNNs) for both feature extraction and classification. However, these methods demonstrate limited efficacy in capturing texture features. Given the high capacity of wavelets in describing texture features, this research integrates learnable wavelet modules utilizing the Lifting Scheme into CNNs and incorporates a parallel wavelet branch into the ResNet18 model to enhance texture feature extraction. Our model can analyze texture features in spatial and frequency domains simultaneously, leading to optimized classification accuracy. We conducted experiments on collected ultrasound datasets and publicly available natural image texture datasets, our proposed network achieved 97.27% accuracy and 95.60% recall on ultrasound datasets, 60.765% accuracy on natural image texture datasets, surpassing the accuracy of ResNet and confirming the effectiveness of our approach.

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Frequency-Dividing Downsampling Module of the Lifting Scheme for Image Classification

Cited by 2 publications

References 12 publications

Multi‐scale homography estimation based on dual feature aggregation transformer

Multi‐scale homography estimation based on dual feature aggregation transformer

Texture classification network integrating adaptive wavelet transform

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