Description: 这个是我初次做的小程序，实现两个3D矩形在直线上的碰撞检测，应该还存在很多不足，目前本人只能做到这些-This is my first time to do a small procedure, the two realize 3D rectangle in a straight line on the collision detection, there are still many deficiencies should be, at present I can only do these Platform: |
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Author:qyz |
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Description: radon变换除了在处理二维三维的投影数据时重建算法的应用，在直线检测中有应用-In addition to radon transform processing data in 2D and 3D projection reconstruction algorithm applications, there are applications in line detection Platform: |
Size: 1024 |
Author:zerazhna |
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Description: DeepFace一文依旧是沿着“检测-对齐-人脸表示-分类”这一人脸识别技术路线来的，其贡献在于对人脸对齐和人脸表示环节的改进。1）在人脸对齐环节，引入了3D人脸模型对有姿态的人脸就行分片的仿射对齐。2）在人脸表示环节，利用一个9层的深度卷积在包含4000人、400万张人脸的数据集上学习人脸表示，这个9层的DCNN网络有超过1.2亿个参数。本文的模型在LFW数据集上取得了97.25 的平均精度（逼近了人类97.5 的极限），同时在Youtube数据集上取得了当前最好的结果，比之前的NO.1整整高出了12.7 。-DeepFace article is still along the detection- alignment- face representation- classification of this face recognition technology line, its contribution to the face alignment and face representation of the improvement. 1) In the face alignment part, the 3D human face model is introduced to affine alignment of slices with face. 2) In the face representation session, using a 9-layer depth convolution on a data set containing 4,000 human faces and 4 million human faces, the 9-layer DCNN network has more than 120 million parameters. The model obtained 97.25 average precision (approximating the 97.5 limit of human) on the LFW dataset, and achieved the best result on the Youtube dataset, 12.7 higher than the previous NO.1 Platform: |
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Author:罗齐 |
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