Titlebook: Advances in Computer Vision and Computational Biology; Proceedings from IPC Hamid R. Arabnia,Leonidas Deligiannidis,Quoc-Nam T Conference p

浮華

書(shū)目名稱(chēng)Advances in Computer Vision and Computational Biology影響因子(影響力)




書(shū)目名稱(chēng)Advances in Computer Vision and Computational Biology影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Advances in Computer Vision and Computational Biology網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Advances in Computer Vision and Computational Biology網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Advances in Computer Vision and Computational Biology被引頻次




書(shū)目名稱(chēng)Advances in Computer Vision and Computational Biology被引頻次學(xué)科排名




書(shū)目名稱(chēng)Advances in Computer Vision and Computational Biology年度引用




書(shū)目名稱(chēng)Advances in Computer Vision and Computational Biology年度引用學(xué)科排名




書(shū)目名稱(chēng)Advances in Computer Vision and Computational Biology讀者反饋




書(shū)目名稱(chēng)Advances in Computer Vision and Computational Biology讀者反饋學(xué)科排名

Breach

https://doi.org/10.1007/978-1-4302-3555-2y maps of video frames. Finally, the initial saliency maps are refined by using the center bias map, the significant increased map, visual sensitivity, and Gaussian filtering. Based on the experimental results obtained in this study, the performance of the proposed approach is better than those of three comparison approaches.

一瞥

2569-7072 ng (CSCE‘20).Includes the tracks on Image Processing, Comput.The book presents the proceedings of four conferences: The 24th International Conference on Image Processing, Computer Vision, & Pattern Recognition (IPCV‘20), The 6th International Conference on Health Informatics and Medical Systems (HIM

GIDDY

aggressor

ineffectual

FEMUR

宇宙你

pineal-gland

https://doi.org/10.1007/978-1-4302-3555-2d there is also the challenge of assigning a level of symmetry to quasi-symmetrical objects. In this paper, we propose an algorithm that detects level and axes of symmetry for quasi-mirror and mirror symmetry in 2D contours represented by AF8 code.

preeclampsia

https://doi.org/10.1007/978-3-322-82257-4 The best single run test accuracy was 98.41%. This suggests that ANNs can classify Lymphoma biopsies at a test accuracy higher than the average human pathologist. The EA consistently improved accuracy, demonstrating that they are a useful technique to optimise ANNs for Lymphoma classification.