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Last modified by annedevismes on 2021/06/08 11:56
From version 2.3
edited by puchades
on 2020/03/25 14:56
Change comment: There is no comment for this version
To version 1.5
edited by puchades
on 2020/03/25 14:46
Change comment: There is no comment for this version

Summary

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9 9  
10 10  As a general rule, pixel classification is suitable for images in which there are clear differences in the colour, intensity and/ or texture of the feature-of-interest versus the background and other structures.  If there is non-specific labelling in the image that is very similar in appearance to the labelling-of-interest, object classification may allow the non-specific labelling to be filtered out based on object level features such as size and shape. The best approach is determined by trial and error.
11 11  
12 -=== Pixel classification workflow ===
13 13  
14 -For a quick introduction, watch: [[https:~~/~~/www.youtube.com/watch?v=5N0XYW9gRZY&feature=youtu.be>>url:https://www.youtube.com/watch?v=5N0XYW9gRZY&feature=youtu.be]]
13 +=== H3 Headings Will Appear In The Table of Content ===
15 15  
16 -=== Object classification workflow ===
15 +==== You can also add images ====
17 17  
17 +[[image:Collaboratory.Apps.Article.Code.ArticleSheet@placeholder.jpg]]
18 18  
19 -==== ====
19 +Photo by David Clode
20 20  
21 +==== Or code ====
21 21  
23 +Code blocks can be added by using the code macro:
24 +
25 +{{code language="python"}}
26 +x = 1
27 +if x == 1:
28 + # indented four spaces
29 + print("x is 1.")
30 +{{/code}}
31 +
32 +(% class="wikigeneratedid" id="HH4Won27tAppearinToC" %)
22 22