随后,每一张特征图都要进行一系列调整,以提高它的画质、改善它的明暗对比度。
Next, the maps are swiped again, but this time rather than stopping at each pixel, the filtertakes a snapshot every few pixels.
接下来,对这些特征图再次进行扫描,但这一次,滤光器不会在像素上逐一停留,而是每几个像素快拍一次。
That produces a new set of maps of lower resolution.
这样,得到一组新的分辨率较低的特征图。
These highlight the salient features while reining in computing power.
这些步骤凸显图像最显著的特征,同时对计算资源进行严格控制。
The whole process is then repeated, with several hundred filters probing for more elaborateshapes rather than just a few scouring for simple ones.
然后,将整个过程重复一遍,用几百个滤光器探查更为精细的物体形状,而不是随便扫视一些简单的形状。
The resulting array of feature maps is run through one final set of filters.
由此得到的特征图阵列,被输送经过最后一组滤光器。
These classify objects into general categories, such as pedestrians or cars.
它们可以对物体进行大体归类是行人还是汽车等等。
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