Application of mathematical morphology operation with memristor-based computation-in-memory architecture for detecting manufacturing defects

Zhou, Ying; Gao, Bin; Zhang, Qingtian; Yao, Peng; Geng, Yiwen; Sun, Wen; Zhao, Meiran; Xi, Yue; Tang, Jianshi; Qian, He; Wu, Huaqiang

doi:10.1016/j.fmre.2021.06.020

Cited by 6 publications

(1 citation statement)

References 31 publications

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“…Finally, some morphological operations applied on the isolated binary images include dilating to find the maximum local values in frames and then filling to remove the holes by convolution of an isolated image and disk structure element (SE) with radius one [ 36 ]. Furthermore, all binarized frames have normalized to enhance images by generating a new range from an existing one.…”

Section: Framework For Gait Recognitionmentioning

confidence: 99%

Model-based and model-free deep features fusion for high performed human gait recognition

Yousef¹,

Khalil

Samra

et al. 2023

J Supercomput

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In the last decade, the need for a non-contact biometric model for recognizing candidates has increased, especially after the pandemic of COVID-19 appeared and spread worldwide. This paper presents a novel deep convolutional neural network (CNN) model that guarantees quick, safe, and precise human authentication via their poses and walking style. The concatenated fusion between the proposed CNN and a fully connected model has been formulated, utilized, and tested. The proposed CNN extracts the human features from two main sources: (1) human silhouette images according to model-free and (2) human joints, limbs, and static joint distances according to a model-based via a novel, fully connected deep-layer structure. The most commonly used dataset, CASIA gait families, has been utilized and tested. Numerous performance metrics have been evaluated to measure the system quality, including accuracy, specificity, sensitivity, false negative rate, and training time. Experimental results reveal that the proposed model can enhance recognition performance in a superior manner compared with the latest state-of-the-art studies. Moreover, the suggested system introduces a robust real-time authentication with any covariate conditions, scoring 99.8% and 99.6% accuracy in identifying casia (B) and casia (A) datasets, respectively.

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Section: Framework For Gait Recognitionmentioning

confidence: 99%