Image micro-target recognition method based on multi-layer empirical mode decomposition algorithm

Yin, Hongpeng

doi:10.1109/icmtma54903.2022.00079

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…The fuzzy C-means algorithm is a straightforward and effective unsupervised clustering algorithm that can efficiently converge with appropriate initial conditions. However, real-world image segmentation and recognition applications often face challenges such as blurry and indistinct boundaries [13][14][15]. The fuzzy C-means clustering algorithm is capable of handling this ambiguity, and it can retain more original image information during the segmentation process, resulting in better robustness than hard clustering algorithms.…”

Section: Fuzzy C-means Algorithm For Image Segmentation and Recognitionmentioning

confidence: 99%

Image recognition and position technology based on super-pixel fuzzy C-means clustering in industrial assembly systems

Yuan¹,

Sun²,

Wang³

2023

Third International Conference on Image Processing and Intelligent Control (IPIC 2023)

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Improved fuzzy c-means (FCM) clustering algorithms have been widely used for image recognition and localization. However, in industrial assembly systems, the unsatisfactory pixel merging and segmentation results between local adjacent windows, combined with the differences in the shape, size, and material of parts, as well as variations in lighting conditions, make target image recognition and localization a challenge. Most algorithms struggle to achieve the expected results and have high computational complexity. In this study, we propose a super-resolution-based FCM clustering algorithm that is faster and more accurate for image recognition and localization in industrial assembly systems with irregular part sizes. We first use multiscale morphological gradient operations to obtain high-resolution images. Then, we use the fast FCM clustering algorithm to achieve the recognition and extraction of specific target images. Finally, we use the Sobel operator to determine the target's position. The experimental results demonstrate that the proposed algorithm shows higher accuracy and efficiency in image recognition and localization for industrial assembly systems.

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Section: Fuzzy C-means Algorithm For Image Segmentation and Recognitionmentioning

confidence: 99%

Image recognition and position technology based on super-pixel fuzzy C-means clustering in industrial assembly systems

Yuan¹,

Sun²,

Wang³

2023

Third International Conference on Image Processing and Intelligent Control (IPIC 2023)

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“…In recent years, with the rapid development of artificial intelligence, target recognition, as a hot research technology in computer vision, has been widely used within the scope of many fields [1][2][3]. In the retail industry, intelligent retail containers have the advantages of low operating cost and high flexibility through the combination of unmanned intelligent service and mobile payment, and intelligent shopping gradually replaces the traditional retail method.…”

Section: Introductionmentioning

confidence: 99%

Identification of Small Goods under the Adversarial Network

2024

International Journal of Science and Engineering Applications

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Small targets are easily lost and misjudged in detection tasks due to their relatively low resolution in images. Aiming at the problems of large recognition error and weak generalisation ability of small goods in the commodity recognition task when facing complex conditions such as hand occlusion and poor lighting, this paper proposes an adaptive small goods recognition method based on adversarial networks. Firstly, an end-to-end adversarial process-adapted network model is designed using the adversarial and recognition process a priori, and the super-resolution network is used as a generator for reconstructing the semantic information of small commodities, and the recognition network is used as a discriminator for classification and authenticity judgement of the input image. In addition, a new target feature reconstruction function is designed for reconstructing discriminative feature information. In order to solve the problem of large recognition error caused by the difference in the appearance of goods, a feature map-based attention mechanism is proposed for enhancing the sensory field of the recognition network, so that the recognition of large and small goods achieves more accurate results. Tested on SVHN dataset, CIFAR10 dataset and homemade small goods dataset, the adversarial structure improves the recognition accuracy by 4.1% compared with the cascade structure. Meanwhile, ablation experiments are designed to verify the effectiveness of the target feature reconstruction function and the feature graph attention mechanism for feature reconstruction and small goods recognition, respectively. The experimental results show that the method improves the accuracy of small goods recognition and is robust to multi-scale goods recognition and partial occlusion.

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Image micro-target recognition method based on multi-layer empirical mode decomposition algorithm

Cited by 2 publications

References 7 publications

Image recognition and position technology based on super-pixel fuzzy C-means clustering in industrial assembly systems

Image recognition and position technology based on super-pixel fuzzy C-means clustering in industrial assembly systems

Identification of Small Goods under the Adversarial Network

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