Method of Optimal Circular Path for Iris Template Matching

Матвеев, И. А.; Novik, Vladimir P.

doi:10.1134/s1054661819010176

Cited by 2 publications

(2 citation statements)

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“…Of course, the general convolutional neural network extracts a variety of features, so each feature map contains 10 8 weights, and the final parameters are also approximately Nx10 8 , where N is the number of feature maps.…”

Section: Parameter Reduction and Weight Sharing Of Convolutional Neural Networkmentioning

confidence: 99%

“…Many traditional medical motion image recognition algorithms have been studied, including template matching [8], statistical recognition [9], and fuzzy sets [10] and so on. Traditional medical motion images contain rich colors, edges, etc., but due to complex background, variable light, occlusion, angle of view and other factors, the accuracy of motion recognition algorithms is not high.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Recognition of Medical Motion Image Combining Convolutional Neural Network With Internet of Things

Zhou

Gao

2019

IEEE Access

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For the small-scale motion in medical motion images, the traditional medical motion image intelligent recognition algorithm has low recognition accuracy, and requires a large amount of calculation statistics. There is no self-learning function, which seriously affects the accuracy and speed of medical motion image recognition. Therefore, in order to improve the accuracy of human body small-scale motion recognition in medical motion images and the computational efficiency of large-scale data sets, an intelligent recognition algorithm based on convolutional neural network for medical motion images is proposed. The algorithm first learns the dense trajectory features and depth features, and then further fuses the dense trajectory features with the deep learning features. Finally, the extreme learning machine is applied to the convolutional neural network, and the fused features are further trained as input information of the convolutional neural network, and the features from the bottom layer to the upper layer can be extracted step by step from the raw data of the pixel level. Simulation experiments show that the algorithm can effectively improve the recognition accuracy of small-scale motion in medical moving images and improve the speed of motion.INDEX TERMS Internet of things, convolutional neural network, medical motion image, intelligent recognition.

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Section: Parameter Reduction and Weight Sharing Of Convolutional Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%