Face Editing Based on Facial Recognition Features

The status of French as a language has improved to some extent in recent years as a result of the exchange and blending of cultures from various nations. Each language, including French, must undergo a protracted process in order to become more widely spoken. It is essential to develop top-notch French interpreters in order to lower the barriers preventing cross-border communication. The teaching of French interpretation has recently received more attention in many colleges, but the issue of a single teaching method has long persisted. In recent years, a new approach to teaching the liberal arts has emerged. Its main goal is to implement comprehensive interdisciplinary teaching by fusing contemporary information technology with traditional liberal arts instruction. This paper conducted a cutting-edge study on the teaching method of French interpretation against the backdrop of the new liberal arts in order to alter the teaching strategy and increase teaching effectiveness. In this paper, an intelligent interpretation teaching system was constructed using information technology. Through text classification algorithms, it incorporated and optimized teaching resources. According to the experimental findings, the teaching process was optimized, and the teaching efficiency increased by 7.94% when French interpretation was taught against a backdrop of new liberal arts.

Section: New Liberal Arts Teaching Philosophymentioning

confidence: 99%

[Retracted] Innovation of French Interpretation Teaching Mode in the New Liberal Arts Environment

Zhang

2022

“…People's lives have been greatly improved by the development of information processing, computer, and communication technologies, while social needs are a major driving force behind technological advancement [ 6 ]. Fast processing speed, large information capacity, and high resolution are all advantages of optical image processing technology [ 7 – 9 ]. Due to processing accuracy, poor stability, and inconvenient equipment operation, it is less flexible than digital image processing for all types of requirements, and it is constrained by the actual production process and equipment materials.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Exploring Wavelet Transform‐Based Image Enhancement Algorithm for Image Restoration of Long March National Cultural Park

Wang

2022

China has taken the lead in exploring the construction of large-scale public cultural parks. By restoring the relics of the Long March, we can further promote the spirit of the Red Army, inherit the red gene, contribute to national pride, and contribute to local economic development. A new community will be built with culture, education, tourism, and leisure as the main contents. System aberration, human interference, motion, and system noise can all lead to image quality degradation. Wavelet enhancement technique is to use Wiener filtering in the Fourier region, using other properties of wavelets for filtering. The wavelet analysis method was used to recover the images within the Long March National Park, and the corresponding processing was carried out to achieve a better recovery effect. Through comparative tests, it was found that the algorithm had an average reduction of 223 iterations and an average increase of 11.264 in signal-to-noise ratio when iterating, and the number of iterations was also greatly improved. The higher the wavelet coefficients are, the higher the noise is. This paper introduces a new wavelet transform-based image restoration technique for national parks.

“…The first and most crucial step in computer vision technology, image segmentation is a key topic in many fields, including image processing [ 5 , 6 ], pattern recognition [ 7 , 8 ], and AI. Due to its advantages of independently extracting high-level features from images, NN (Neural Network) has achieved outstanding results in image segmentation tasks in many fields in recent years.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Optimization of Artistic Image Segmentation Algorithm Based on Feed Forward Neural Network under Complex Background Environment

Li¹

2022

Based on the theory and application, this paper discusses the optimization of art image segmentation algorithm based on FFNN (Feed Forward Neural Network). In this paper, residual units are used in the corresponding stages of encoder and decoder, and feature information of several convolution layers in each convolution stage of encoder is extracted at the same time. And the feature pyramid module is used to extract multiscale features from the feature map of the last convolution stage in the encoder. Finally, pixel by pixel additions combine the previously mentioned feature information into the corresponding layer of the decoder. Additionally, an improved weight adaptive algorithm based on feature preservation is suggested in this paper, which addresses the issue that the conventional image segmentation algorithm is noise-sensitive. The adaptive connection weight mechanism is also introduced. The accuracy and recall rates of this optimization algorithm can both reach 96.574%, according to the results of 50% cross-validation. All the segmentation performance evaluation indexes of this algorithm are higher than the existing main algorithms. Moreover, the algorithm takes a short time, does not need too much manual intervention, and can effectively segment artistic images. The optimization algorithm in this paper has certain reference significance for the related research of artistic image segmentation.