From 36b2506a84e966ffdb22360f84989dd1e274b0b9 Mon Sep 17 00:00:00 2001 From: NotXia <35894453+NotXia@users.noreply.github.com> Date: Tue, 4 Feb 2025 20:16:16 +0100 Subject: [PATCH] Fix typos --- .../sections/_object_detection.tex | 2 +- .../sections/_segmentation.tex | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/year2/machine-learning-for-computer-vision/sections/_object_detection.tex b/src/year2/machine-learning-for-computer-vision/sections/_object_detection.tex index 12a0448..0375cd8 100644 --- a/src/year2/machine-learning-for-computer-vision/sections/_object_detection.tex +++ b/src/year2/machine-learning-for-computer-vision/sections/_object_detection.tex @@ -84,7 +84,7 @@ \indenttbox \begin{remark} - Recall is monotonically decreasing, while precision can both decrease and increase. + Recall is monotonically increasing, while precision can both decrease and increase. \end{remark} \end{example} diff --git a/src/year2/machine-learning-for-computer-vision/sections/_segmentation.tex b/src/year2/machine-learning-for-computer-vision/sections/_segmentation.tex index 889d853..5cfc915 100644 --- a/src/year2/machine-learning-for-computer-vision/sections/_segmentation.tex +++ b/src/year2/machine-learning-for-computer-vision/sections/_segmentation.tex @@ -53,7 +53,7 @@ \end{figure} \item[Depth comparison features] \marginnote{Depth comparison features} - Given a depth image $D$ and the offsets $\theta = (\Delta p, \Delta n)$, each pixel $x$ of $D$ produces a feature as follows: + Given a depth image $D$ and the offsets $\theta = (\Delta p, \Delta n)$, each pixel $x$ of $D$ produces a set features of form: \[ f(x; D, (\Delta p, \Delta n)) = D\left[ x + \frac{\Delta p}{D[x]} \right] - D\left[ x + \frac{\Delta n}{D[x]} \right] \] In other words, each $x$ is described by the difference in depth between two points offset from $x$. The depth at background pixels is a large positive number.