Computer Vision Foundations · 免费预览

平移不变性与卷积

为什么处理过程不应依赖于内容所处的位置,以及卷积是唯一具有该性质的线性算子这一定理。

1. Content can appear anywhere

Content can appear anywhere

2. The local average as the canonical example

The local average as the canonical example

3. Convolution, the only such operator

Convolution, the only such operator

4. Properties you get for free

Properties you get for free

5. What translation invariance buys

A filter is described as linear and translation-invariant. Which statement follows from the theory of this lesson?

6. Checking equivariance on an example

An image contains a bright dot, and a blur filter produces a small blob around it. The dot is moved 40 pixels to the right and the filter runs again. What does translation equivariance predict about the new output?

7. A tiny convolution

Convolve the kernel h=[1,1]h = [1,1] with the signal ℓin=[2,4]\ell_{\text{in}} = [2,4], computing h∘ℓin=[2,6,4]h \circ \ell_{\text{in}} = [2, 6, 4]. Write the middle value of the output.

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