Affine Transformation Based Ontology Sparse Vector Learning Algorithm

Zhu, Linli; Pan, Yu; Wang, Jiangtao

doi:10.21042/amns.2017.1.00009

Cited by 11 publications

(4 citation statements)

References 30 publications

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“…In order to accurately recognize face images, it is necessary to perform posture correction and noise reduction on the face images. Using wavelet denoising method, the geometric structure models of face images with different attitude features are first given [1]. Feature extraction and sample collection are performed on face models in different poses, and an affine transformation between two vector spaces with face image features is constructed in rotation coordinates [2,3].…”

Section: Noise Reduction Of Face Imagesmentioning

confidence: 99%

Recognition Method for Face Local Features Based on Fuzzy Algorithm and Intelligent Data Analysis

2022

Discrete Dynamics in Nature and Society

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Traditional recognition methods for face local features have a low recognition rate, so the recognition method for face local features based on the fuzzy algorithm and intelligent data analysis was designed. Firstly, the wavelet denoising method was used to reduce the noise of face images, and adaptive template matching was performed on the obtained images. Then, the face information was encoded, and the face features were identified to locate the face features. On this basis, the principal components of the face were analyzed to obtain the global features of the face. Finally, through the candidate set of facial local feature recognition, the extraction of face local features, and the fusion of face local features, the recognition of local face features were realized. The experimental results show that the average recognition rate of this method is 88.84% in a noise environment and 97.3% in noise-free environment. It can accurately recognize the face local features, can meet the needs of recognition of the face local features, and has certain practical application significance.

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Section: Noise Reduction Of Face Imagesmentioning

confidence: 99%

Recognition Method for Face Local Features Based on Fuzzy Algorithm and Intelligent Data Analysis

2022

Discrete Dynamics in Nature and Society

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“…The core of raindrop removal for single image based on deep learning is to design a more effective end-to-end mapping model of raindrop image and rainless image to achieve the repair of raindrop area [13]. VDSR (very deep networks for Super-Resolution) is a kind of feedforward deep convolution neural network based on deep learning.…”

Section: Raindrop Removal Model Based On Deep Learningmentioning

confidence: 99%

Single Image Rain Removal Based on Deep Learning and Symmetry Transform

Yang

et al. 2020

Symmetry

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Rainy, as an inevitable weather condition, will affect the acquired image. To solve this problem, a single image rain removal algorithm based on deep learning and symmetric transformation is proposed. Because of the important characteristics of wavelet transform, such as symmetry, orthogonality, flexibility and limited support, wavelet transform is used to remove rain from a single image. The image is denoised by using wavelet decomposition, threshold value and wavelet reconstruction in wavelet transform, and the rain drop image is transformed from RGB space to YUV (luma chroma) space by using deep learning to obtain the brightness component and color component of the image. the brightness component and residual component of the raindrop source image and the ideal recovered image without raindrop are extracted. The residual image and brightness component are overlapped again, the reconstructed image is restored to RGB space by YUV inverse transformation, and the final color raindrop free image is obtained. After training the network, the optimal parameters of the network are obtained, and finally the convolution neural network which can effectively remove the rain line is obtained. Experimental results show that compared with other algorithms, the proposed algorithm achieves the highest value in both peak signal-to-noise ratio (PSNR) and structural similarity, which shows that the image effect of the algorithm is better after rain removal.

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“…Experiment shows that the change of temperature and heat transfer conditions at the cold end has little effect on the output performance [11]. At the same time, researchers found that contact pressure, cold source structure and cooling mode also affect the output performance of thermoelectric generator [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Numerical analysis of semiconductor thermoelectric generator

Xiang

Wang

2020

THERM SCI

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A thermoelectric generation model is proposed based on the structure of thermoelectric generator, working conditions, the effect of air heat transfer and contact resistance in thermoelectric components. In addition, the effect of the thermoelectric generator output performance under the condition of different temperature of the cold and heat source, contact resistance between the cold-end and hot-end, the load resistance and the contact resistance is calculated. The results show that the output voltage is linear associate with the temperature difference between hot and cold ends, however, the output power increase along with the increase of temperature of hot-end and decrease of cold-end. The output voltage reaches 5.76 V and the output power reaches 9.81 W when the temperature difference is 200 °C. Assume that the contact resistance is ignored, the output voltage and power reach peak values of 3.61 V and 3.85 W. The output performance of thermoelectric generator decreases with the increase of thermal contact resistance at hot and cold ends, and the reduction is getting lower and lower. With the increase of the load resistance, the output power increases at the beginning and then decreases. The optimal output power is 3.69 W when the contact resistance is 0 Ω and the optimal load resistance is 3.3 Ω. The maximum output power corresponding to neglecting the contact resistance will be reduced by 13.5% when the contact resistance is 0.5 Ω.

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Affine Transformation Based Ontology Sparse Vector Learning Algorithm

Cited by 11 publications

References 30 publications

Recognition Method for Face Local Features Based on Fuzzy Algorithm and Intelligent Data Analysis

Recognition Method for Face Local Features Based on Fuzzy Algorithm and Intelligent Data Analysis

Single Image Rain Removal Based on Deep Learning and Symmetry Transform

Numerical analysis of semiconductor thermoelectric generator

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