The Method of Representing Grayscale Images in Pseudo Color Using Equal-Contrast Color Space

Potashnikov, A. M.; Vlasuyk, I. V.; Ivanchev, V.V.; Balobanov, A. V.

doi:10.1109/ieeeconf48371.2020.9078584

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…The JET color mapping algorithm is the most commonly used in computer vision, which maps the gray image (0-255) into the pseudo-color image, as shown in Figure 6. The JET mapping algorithm produces pseudo-color images with a high contrast ratio and enhances the information content of the image, which can improve the visual effect of the sample images, effectively highlight the details of the image, and extract features from the sample image [42].…”

Section: Pseudo-color Enhancementmentioning

confidence: 99%

Hybrid Architecture Based on CNN and Transformer for Strip Steel Surface Defect Classification

Xiong

2022

Electronics

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Strip steel surface defects occur frequently during the manufacturing process, and these defects cause hidden risks in the use of subsequent strip products. Therefore, it is crucial to classify the strip steel’s surface defects accurately and efficiently. Most classification models of strip steel surface defects are generally based on convolutional neural networks (CNNs). However, CNNs, with local receptive fields, do not have admirable global representation ability, resulting in poor classification performance. To this end, we proposed a hybrid network architecture (CNN-T), which merges CNN and Transformer encoder. The CNN-T network has both strong inductive biases (e.g., translation invariance, locality) and global modeling capability. Specifically, CNN first extracts low-level and local features from the images. The Transformer encoder then globally models these features, extracting abstract and high-level semantic information and finally sending them to the multilayer perceptron classifier for classification. Extensive experiments show that the classification performance of CNN-T outperforms pure Transformer networks and CNNs (e.g., GoogLeNet, MobileNet v2, ResNet18) on the NEU-CLS dataset (training ratio is 80%) with a 0.28–2.23% improvement in classification accuracy, with fewer parameters (0.45 M) and floating-point operations (0.12 G).

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Section: Pseudo-color Enhancementmentioning

confidence: 99%