Titlebook: Advanced Intelligent Computing Technology and Applications; 20th International C De-Shuang Huang,Wei Chen,Yijie Pan Conference proceedings

fundoplication

書(shū)目名稱Advanced Intelligent Computing Technology and Applications影響因子(影響力)




書(shū)目名稱Advanced Intelligent Computing Technology and Applications影響因子(影響力)學(xué)科排名




書(shū)目名稱Advanced Intelligent Computing Technology and Applications網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Advanced Intelligent Computing Technology and Applications網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Advanced Intelligent Computing Technology and Applications被引頻次




書(shū)目名稱Advanced Intelligent Computing Technology and Applications被引頻次學(xué)科排名




書(shū)目名稱Advanced Intelligent Computing Technology and Applications年度引用




書(shū)目名稱Advanced Intelligent Computing Technology and Applications年度引用學(xué)科排名




書(shū)目名稱Advanced Intelligent Computing Technology and Applications讀者反饋




書(shū)目名稱Advanced Intelligent Computing Technology and Applications讀者反饋學(xué)科排名

黃瓜

MAPNet: A Multi-scale Attention Pooling Network for Ultrasound Medical Image Segmentationnt autonomous feature extraction capability and good feature representation ability. Traditional convolutional neural networks usually use standard convolutional layers to extract features. However, in medical image segmentation, a pixel may correspond to multiple different scale structures, such as

Acumen

欲望小妹

MOD-YOLO: Improved YOLOv5 Based on Multi-softmax and Omni-Dimensional Dynamic Convolution for Multi- of bridge defect categories often occurs simultaneously, it is difficult for target detection methods targeting a single label to achieve accurate bridge defect detection. This paper proposes a bridge defect detection scheme YOLOv5 based on multi-softmax and omni-dimensional dynamic convolution (MO

空氣傳播

Color Image Steganography Based on Two-Channel Preprocessing and U-Net Networkrocess where secret information may be stolen. With the increasing application of the U-Net network, a novel image steganography technique based on the U-Net structure is designed. The special two-channel preprocessing network is designed to fuse image features, and the SENet attention mechanism has

Axon895

Application of a Hybrid Particle Image Velocimetry Method Based on Window Function in the Field of Te initial velocity field generated based on cross-correlation algorithm in mixed particle image velocimetry methods. This can result in inaccurate offset images generated, leading to errors in generating the final fine velocity field using optical flow method. This article adds a window function to

綠州

Gum-Disease

Refinement Correction Network for Scene Text Detectionliability of text detection. To this end, existing models primarily employ deep convolutional networks to extract semantic information from images. However, the multiple convolutions and downsampling operations in network lead to varying degrees of defects in shallow and deep features. To address th

救護(hù)車

蔓藤圖飾

Unsupervised Extremely Low-Light Image Enhancement with a Laplacian Pyramid Network. To overcome these two problems, we propose an unsupervised Extremely Low-light image enhancement via a Laplacian Pyramid Network (ELLPN). Concretely, concerning the first quandary, we propose to enforce semantic content and style constraints in the low-frequency components of the image’s Laplacian