ize -> ise

Author: tobyhodges

_extras/edge-detection.md

@@ -82,7 +82,7 @@ This method uses a series of steps, some incorporating other types of edge detec
 The skimage `skimage.feature.canny()` function performs the following steps:
 
 1. A Gaussian blur
-   (that is characterized by the `sigma` parameter,
+   (that is characterised by the `sigma` parameter,
    see [_Blurring Images_]({{ page.root }}{% link _episodes/06-blurring.md %}))
    is applied to remove noise from the image.
    (So if we are doing edge detection via this function,
@@ -248,7 +248,7 @@ Then, when we run the program, we can use the sliders to
 vary the values of the sigma and threshold parameters
 until we are satisfied with the results.
 After we have determined suitable values,
-we can use the simpler program to utilize the parameters without
+we can use the simpler program to utilise the parameters without
 bothering with the user interface and sliders.
 
 Here is a Python program that shows how to apply Canny edge detection,
@@ -471,5 +471,5 @@ check out in [the documentation](https://scikit-image.org/docs/dev/api/skimage.v
 
 As with blurring, there are other options for finding edges in skimage.
 These include `skimage.filters.sobel()`,
-which you will recognize as part of the Canny method.
+which you will recognise as part of the Canny method.
 Another choice is `skimage.filters.laplace()`.

episodes/01-introduction.md

@@ -7,9 +7,9 @@ questions:
 computer vision?"
 - "What are morphometric problems?"
 objectives:
-- "Recognize scientific questions that could be solved with image processing
+- "Recognise scientific questions that could be solved with image processing
  / computer vision."
-- "Recognize morphometric problems (those dealing with the number, size, or
+- "Recognise morphometric problems (those dealing with the number, size, or
 shape of the objects in an image)."
 keypoints:
 - "Simple Python and skimage (scikit-image) techniques can be used to solve genuine

episodes/02-image-basics.md

@@ -40,7 +40,7 @@ we need to spend some time understanding how these abstractions work.
 
 ## Pixels
 
-It is important to realize that images are stored as rectangular arrays
+It is important to realise that images are stored as rectangular arrays
 of hundreds, thousands, or millions of discrete "picture elements,"
 otherwise known as *pixels*.
 Each pixel can be thought of as a single square point of coloured light.
@@ -526,7 +526,7 @@ A larger number in a channel means that more of that primary colour is present.
 >
 > > ## Solution
 > >
-> > 1. (255, 0, 0) represents red, because the red channel is maximized, while
+> > 1. (255, 0, 0) represents red, because the red channel is maximised, while
 > > 	the other two channels have the minimum values.
 > > 2. (0, 255, 0) represents green.
 > > 3. (0, 0, 255) represents blue.
@@ -596,7 +596,7 @@ Such an image is an example of *raster graphics*.
 
 ## Image formats
 
-Although the images we will manipulate in our programs are conceptualized as
+Although the images we will manipulate in our programs are conceptualised as
 rectangular arrays of RGB triplets,
 they are not necessarily created, stored, or transmitted in that format.
 There are several image formats we might encounter,
@@ -611,7 +611,7 @@ Some formats we might encounter, and their file extensions, are shown in this ta
 
 ## BMP
 
-The file format that comes closest to our preceding conceptualization of images
+The file format that comes closest to our preceding conceptualisation of images
 is the Device-Independent Bitmap, or BMP, file format.
 BMP files store raster graphics images as long sequences of binary-encoded numbers
 that specify the colour of each pixel in the image.
@@ -714,12 +714,12 @@ If you already know, you can skip to the challenge below.
 Since image files can be very large,
 various *compression* schemes exist for saving
 (approximately) the same information while using less space.
-These compression techniques can be categorized as *lossless* or *lossy*.
+These compression techniques can be categorised as *lossless* or *lossy*.
 
 ### Lossless compression
 
 In lossless image compression,
-we apply some algorithm (i.e., a computerized procedure) to the image,
+we apply some algorithm (i.e., a computerised procedure) to the image,
 resulting in a file that is significantly smaller than
 the uncompressed BMP file equivalent would be.
 Then, when we wish to load and view or process the image,
@@ -949,7 +949,7 @@ take precautions to always preserve the original files**.
 
 ## Summary of image formats used in this lesson
 
-The following table summarizes the characteristics of the BMP, JPEG, and TIFF
+The following table summarises the characteristics of the BMP, JPEG, and TIFF
 image formats:
 
 | Format   | Compression   | Metadata   | Advantages            | Disadvantages      |

episodes/05-creating-histograms.md

@@ -28,7 +28,7 @@ As it pertains to images, a *histogram* is a graphical representation showing
 how frequently various colour values occur in the image.
 We saw in
 [the _Image Basics_ episode]({{ page.root }}{% link _episodes/02-image-basics.md %})
-that we could use a histogram to visualize
+that we could use a histogram to visualise
 the differences in uncompressed and compressed image formats.
 If your project involves detecting colour changes between images,
 histograms will prove to be very useful,
@@ -164,7 +164,7 @@ it produces this histogram:
 > We will not use it in this lesson in order to understand how to
 > calculate histograms in more detail.
 > In practice, it is a good idea to use this function,
-> because it visualizes histograms more appropriately than `plt.plot()`.
+> because it visualises histograms more appropriately than `plt.plot()`.
 > Here, you could use it by calling
 > `plt.hist(image.flatten(), bins=256, range=(0, 1))`
 > instead of

episodes/08-connected-components.md

@@ -9,7 +9,7 @@ objectives:
 - "Learn about pixel connectivity."
 - "Learn how Connected Component Analysis (CCA) works."
 - "Use CCA to produce an image that highlights every object in a different colour."
-- "Characterize each object with numbers that describe its appearance."
+- "Characterise each object with numbers that describe its appearance."
 keypoints:
 - "We can use `skimage.measure.label` to find and label connected objects in an image."
 - "We can use `skimage.measure.regionprops` to measure properties of labeled objects."
@@ -66,7 +66,7 @@ Note that for brevity,
 ~~~
 {: .output}
 
-The pixels are organized in a rectangular grid.
+The pixels are organised in a rectangular grid.
 In order to understand pixel neighborhoods
 we will introduce the concept of "jumps" between pixels.
 The jumps follow two rules:
@@ -347,7 +347,7 @@ to convert the colours in the image
 (recall that we already used the `skimage.color.rgb2gray()` function
 to convert to grayscale).
 With `skimage.color.label2rgb()`,
-all objects are coloured according to a list of colours that can be customized.
+all objects are coloured according to a list of colours that can be customised.
 We can use the following commands to convert and show the image:
 
 ~~~