Quaternion discrete orthogonal Hahn moments convolutional neural network for color image classification and face recognition

Alami, Abdelmajid El; Mesbah, Abderrahim; Berrahou, Nadia; Lakhili, Zouhir; Berrahou, Aissam; Qjidaa, Hassan

doi:10.1007/s11042-023-14866-4

Cited by 3 publications

(1 citation statement)

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“…Color imagery refers to the subjective emotions associated with specific colors, such as emotions, images, and symbols [9][10]. Color emotion refers to the influence of mood, atmosphere and personality conveyed in a product or environment [11][12].…”

Section: Color Imagementioning

confidence: 99%

Machine Vision Prediction Model for Product Color Design by Kansei Engineering

Han

2024

Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development,

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Color is a visual element of people, it can bring people different levels, different feelings, and can make people have associations and emotional resonance. Under the background of Kansei engineering, product color design is an important research field. This paper aims to discuss the predictive model of Kansei engineering in product color design, in order to improve the quality of products and meet the needs of consumers. Through empirical research and user survey, this paper verifies the accuracy and effectiveness of product color perceptual image generated by machine vision prediction model for the elderly products. The survey and experimental results show that support vector machines perform best, with an accuracy of 91% and a recall rate 4% higher than BP neural networks.

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Section: Color Imagementioning

confidence: 99%

Machine Vision Prediction Model for Product Color Design by Kansei Engineering

Han

2024

Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development,

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CFF-Net: Cross-Hierarchy Feature Fusion Network Based on Composite Dual-Channel Encoder for Surface Defect Segmentation

Qian,

Ding,

Jiang

et al. 2024

Electronics

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In industries spanning manufacturing to software development, defect segmentation is essential for maintaining high standards of product quality and reliability. However, traditional segmentation methods often struggle to accurately identify defects due to challenges like noise interference, occlusion, and feature overlap. To solve these problems, we propose a cross-hierarchy feature fusion network based on a composite dual-channel encoder for surface defect segmentation, called CFF-Net. Specifically, in the encoder of CFF-Net, we design a composite dual-channel module (CDCM), which combines standard convolution with dilated convolution and adopts a dual-path parallel structure to enhance the model’s capability in feature extraction. Then, a dilated residual pyramid module (DRPM) is integrated at the junction of the encoder and decoder, which utilizes the expansion convolution of different expansion rates to effectively capture multi-scale context information. In the final output phase, we introduce a cross-hierarchy feature fusion strategy (CFFS) that combines outputs from different layers or stages, thereby improving the robustness and generalization of the network. Finally, we conducted comparative experiments to evaluate CFF-Net against several mainstream segmentation networks across three distinct datasets: a publicly available Crack500 dataset, a self-built Bearing dataset, and another publicly available SD-saliency-900 dataset. The results demonstrated that CFF-Net consistently outperformed competing methods in segmentation tasks. Specifically, in the Crack500 dataset, CFF-Net achieved notable performance metrics, including an Mcc of 73.36%, Dice coefficient of 74.34%, and Jaccard index of 59.53%. For the Bearing dataset, it recorded an Mcc of 76.97%, Dice coefficient of 77.04%, and Jaccard index of 63.28%. Similarly, in the SD-saliency-900 dataset, CFF-Net achieved an Mcc of 84.08%, Dice coefficient of 85.82%, and Jaccard index of 75.67%. These results underscore CFF-Net’s effectiveness and reliability in handling diverse segmentation challenges across different datasets.

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Satellite Image Restoration via an Adaptive QWNNM Model

Xu,

Zhang,

Crabbe

2024

Remote Sensing

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Due to channel noise and random atmospheric turbulence, retrieved satellite images are always distorted and degraded and so require further restoration before use in various applications. The latest quaternion-based weighted nuclear norm minimization (QWNNM) model, which utilizes the idea of low-rank matrix approximation and the quaternion representation of multi-channel satellite images, can achieve image restoration and enhancement. However, the QWNNM model ignores the impact of noise on similarity measurement, lacks the utilization of residual image information, and fixes the number of iterations. In order to address these drawbacks, we propose three adaptive strategies: adaptive noise-resilient block matching, adaptive feedback of residual image, and adaptive iteration stopping criterion in a new adaptive QWNNM model. Both simulation experiments with known noise/blurring and real environment experiments with unknown noise/blurring demonstrated that the effectiveness of adaptive QWNNM models outperformed the original QWNNM model and other state-of-the-art satellite image restoration models in very different technique approaches.

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Quaternion discrete orthogonal Hahn moments convolutional neural network for color image classification and face recognition

Cited by 3 publications

References 50 publications

Machine Vision Prediction Model for Product Color Design by Kansei Engineering

Machine Vision Prediction Model for Product Color Design by Kansei Engineering

CFF-Net: Cross-Hierarchy Feature Fusion Network Based on Composite Dual-Channel Encoder for Surface Defect Segmentation

Satellite Image Restoration via an Adaptive QWNNM Model

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