Grammar and typo fixes

Consistency pass between all docs, for punctuation and terminology
Converted some spellings to american english (optimisation=optimization, customisation=customization, millimetres=millimeters, etc)

Author: RevOtisElevator

1_Getting_Started/about_feature_comparison.md

@@ -1,7 +1,7 @@
 # ARToolKit Feature Comparison
 ARToolKit v2.x and ARToolKit v5.x, while sharing a small subset of features, are vastly different. The latter represents nearly 8 years of further development of the former.
 
-As well as obvious feature differences, the changes cover a wide variety of less obvious areas, including fundamental algorithms, internal design (modularity, reuse), optimisation, external API design, connections to third-party systems, documentation and developer experience
+As well as obvious feature differences, the changes cover a wide variety of less obvious areas, including fundamental algorithms, internal design (modularity, reuse), optimization, external API design, connections to third-party systems, documentation and developer experience
 
 ##Natural Feature Tracking
 Natural feature tracking is a major feature present in ARToolKit v5.x that is not present in v2.x
@@ -16,7 +16,7 @@ Natural feature tracking is a major feature present in ARToolKit v5.x that is no
 * Variable square pictorial marker (template) resolution
 * 2D barcode marker support
 * Error detection and correction in barcode markers (BCH coding)
-* Automatic binarisation threshold selection for square tracking
+* Automatic binarization threshold selection for square tracking
 * Pose estimate optimization using non-linear refinement
 * Robust pose estimator using M-estimation
 * Robust pose estimation from multi-square markers
@@ -27,7 +27,7 @@ Natural feature tracking is a major feature present in ARToolKit v5.x that is no
 * Web-based tools for barcode and NFT marker generation
 * On-device camera calibration app for Android which feeds into a distributed camera calibration database
 * Cloud-based distributed camera calibration database
-* On-device optical/stereo-optical calibration app for Android.
+* On-device optical/stereo-optical calibration app for Android
 * New tools for square marker testing
 
 ##Stereo and Optical See-Through Support
@@ -50,21 +50,21 @@ Natural feature tracking is a major feature present in ARToolKit v5.x that is no
 * JPEG sequence input module (e.g. from M-JPEG stream, or high-resolution images) support
 * Linux/OSX lib1394 input support
 * Android video support
-* Support for high-resolution still-image capture during live tracking on iOS.
+* Support for high-resolution still-image capture during live tracking on iOS
 
 ##Mobile Focus
-* Mobile-optimised (register size, memory usage)
+* Mobile-optimized (register size, memory usage)
 * OpenGL ES and ES 2.x support
 * Multi-platform mobile support
 * Automatic provision of camera calibration for Apple iOS devices.
-* Automatic provision of camera calibration data for Android devices via distributed camera calibration system.
+* Automatic provision of camera calibration data for Android devices via distributed camera calibration system
 * Integration with GPS and compass (iOS)
 
-##Optimisation and Internals
+##Optimization and Internals
 * Full 64-bit support
 * User-selectable floating point precision
 * Hand-tuned ARM assembly in performance critical sections
-* Optimised pathway for YUV video streams
+* Optimized pathway for YUV video streams
 * Multithreading used throughout
 
 ##New Languages and APIs

1_Getting_Started/about_rendering.md

@@ -1,7 +1,7 @@
 #Graphics, Models, and Rendering
-ARToolKit includes support for loading models from the filesystem and rendering them in your scene. However, this aspect of your application is completely customisable. The support ranges from use of basic OpenGL drawing commands in some of the demos, through to high-quality rendering of a large variety of models via the OpenSceneGraph framework. Additionally, ARToolKit integrates with a wide variety of third-party rendering and game engines, including the popular Unity3D game engine via [ARToolKit for Unity][artk_unity].
+ARToolKit includes support for loading models from the filesystem and rendering them in your scene. However, this aspect of your application is completely customizable. The support ranges from use of basic OpenGL drawing commands in some of the demos, through to high-quality rendering of a large variety of models via the OpenSceneGraph framework. Additionally, ARToolKit integrates with a wide variety of third-party rendering and game engines, including the popular Unity3D game engine via [ARToolKit for Unity][artk_unity].
 
-ARToolKit for iOS and Android also include loading of static Wavefront .obj files (including materials) via **libEden**.
+ARToolKit for iOS and Android also includes loading of static Wavefront .obj files (including materials) via **libEden**.
 
 The rendering method used in the example applications in ARToolKit for Desktop is as follows:
 

1_Getting_Started/general_deploy_application.md

@@ -39,7 +39,7 @@ or for a 64-bit executable:
 
 You might need to adjust the above if the path to your standalone's .exe is a subfolder of {app}.
 
-Additionally, if you use the QuickTime support included in ARvideo, you should obtain the redistributable installer for QuickTime from Apple via the Apple Developer Support.
+Additionally, if you use the QuickTime support included in ARvideo, you should obtain the redistributable installer for QuickTime from Apple via Apple Developer Support.
 
 ##OS X
 

2_Configuration/config_adjusting_exposure.md

@@ -1,9 +1,9 @@
 #Adjusting for Over-/Under-Exposed Video Sources
-Part of ARToolKit's tracking operates on a binarized image, that is, an image that has been converted into black and white pixels only. All pixels above a certain level of brightness, referred to as the "binarization threshhold", or just "threshhold" are converted to white, and all pixels below this threshhold are converted to black.
+Part of ARToolKit's tracking operates on a binarized image, that is, an image that has been converted into black and white pixels only. All pixels above a certain level of brightness, referred to as the "binarization threshold", or just "threshold" are converted to white, and all pixels below this threshold are converted to black.
 
-The binarization threshhold is an 8-bit number that is in the range `[0, 255]`, inclusive. The default value is usually set in the middle of this range, allowing ARToolKit to easily find markers in images that have good contrast. The default value is defined by the symbol `AR_DEFAULT_LABELING_THRESH` in arConfig.h.
+The binarization threshold is an 8-bit number that is in the range `[0, 255]`, inclusive. The default value is usually set in the middle of this range, allowing ARToolKit to easily find markers in images that have good contrast. The default value is defined by the symbol `AR_DEFAULT_LABELING_THRESH` in arConfig.h.
 
-Where images have poor contrast, or are over- or under-exposed, the default binarization threshhold should be adjusted to compensate. This is achieved by calling the ARToolKit function `arGetLabelingThresh()` to get the current threshhold value, adjusting it up for an image that is too bright, or down for an image that is too dark, and then calling `arSetThreshold()` with the new value. This is demonstrated in this snippet from the simpleLite example:
+Where images have poor contrast, or are over- or under-exposed, the default binarization threshold should be adjusted to compensate. This is achieved by calling the ARToolKit function `arGetLabelingThresh()` to get the current threshold value, adjusting it up for an image that is too bright, or down for an image that is too dark, and then calling `arSetThreshold()` with the new value. This is demonstrated in this snippet from the simpleLite example:
 <pre>
     static void Keyboard(unsigned char key, int x, int y)
     {
@@ -20,13 +20,13 @@ Where images have poor contrast, or are over- or under-exposed, the default bina
                 break;
         }
         if (threshChange) {
-            int threshhold;
-            arGetLabelingThresh(gARHandle, &threshhold);
-            threshhold += threshChange;
-            if (threshhold < 0) threshhold = 0;
-            if (threshhold > 255) threshhold = 255;
-            arSetLabelingThresh(gARHandle, threshhold);
-            printf("Threshhold changed to %d.\n", threshhold);
+            int threshold;
+            arGetLabelingThresh(gARHandle, &threshold);
+            threshold += threshChange;
+            if (threshold < 0) threshold = 0;
+            if (threshold > 255) threshold = 255;
+            arSetLabelingThresh(gARHandle, threshold);
+            printf("threshold changed to %d.\n", threshold);
         }
     }
 </pre>

2_Configuration/config_camera_calibration.md

@@ -64,7 +64,7 @@ The help text is reproduced here:
       -cornerx=n: specify the number of corners on chessboard in X direction.
       -cornery=n: specify the number of corners on chessboard in Y direction.
       -imagenum=n: specify the number of images captured for calibration.
-      -pattwidth=n: specify the square width in the chessbaord.
+      -pattwidth=n: specify the square width in the chessboard.
       -h -help --help: show this message
 </pre>
 
@@ -87,7 +87,7 @@ When the camera can clearly see all the intermediate corners, the X marks turn R
 If some of the corners are obscured by the edges of the camera frame, or poor lighting or reflection, the crosses will be GREEN, and no calibration image can be captured until the optical conditions are changed.
 ![A poor view of the calibration board, NOT ready to capture.][Calibration_example_not_OK]
 
-Once you have an image with all red crosses, you can press the space bar on the keyboard. The image will be captured, and the locations of the X points will be printed to the terminal window, and the counter will increment.
+Once you have an image with all red crosses, you can press the spacebar on the keyboard. The image will be captured, and the locations of the X points will be printed to the terminal window, and the counter will increment.
 
 In order to obtain a good calibration for the camera, it is important to obtain images of the calibration board at a variety of angles to the camera lens. The images below give examples of the configurations of the calibration board you should try to obtain. Note that these involve holding the camera at different angles to the board, including upside-down:
 ![Calibration example OK 1][example OK 1]
@@ -122,7 +122,7 @@ Err[10]: 0.385920[pixel]
 Filename[camera_para.dat]:
 </pre>
 
-If the calibration data is good, there should be very low estimated error in each image: hopefully, less than 1 pixel. *Error greater than 2 pixels indicates a poor calibration, and it should be abandoned and restarted.*
+If the calibration data is good, there should be very low estimated error in each image: hopefully, less than 1 pixel. *Errors greater than 2 pixels indicates a poor calibration, and it should be abandoned and restarted.*
 
 At this point, you can press return or enter on the keyboard to save the data in a file named "camera_para.dat".
 

3_Marker_Training/marker_barcode.md

@@ -1,4 +1,4 @@
-# Using 2D Barcode Markers
+# Using 2D-Barcode Markers
 ARToolKit is well known for the appearance of its [markers][marker_about]: square markers with a black border, and with an arbitrary user-definable image in the interior. The "Hiro" marker used by default in the ARToolKit examples is an iconic example.
 
 Multiple markers can be used to represent different objects or coordinate systems in an AR application. Additionally, a pattern can perform an important function in calculating the orientation of a square marker, since a pattern, being rotationally asymmetric, distinguishes between the four possible orientations of a marker in two dimensional space (the marker plane).

3_Marker_Training/marker_nft_fiducial_markers.md

@@ -53,7 +53,7 @@ The tracking in this application is initialized by the appearance of a marker. O
 
 For more information continue to the page [Running the nftSimple example][2]
 
-A more complex example, showing the use of 2D barcode markers outside the NFT surface can be downloaded [here][3].
+A more complex example, showing the use of 2D-barcode markers outside the NFT surface can be downloaded [here][3].
 
 [marker_about]: 3_Marker_Training:marker_about
 [1]: 3_Marker_Training:marker_nft_training

3_Marker_Training/marker_nft_training.md

@@ -39,7 +39,7 @@ In many cases, it may be simplest to start with a physical print of the tracked
 -   You are augmenting the pages of a book, magazine, or other printed material for which you do not have the design artwork.
 -   You have digital artwork, but the physical print differs considerably in brightness, color or tone.
 
-If using a scanner or camera that needs a "resolution" setting, you can just directly use the maximum resolution calculated by the checkResolution tool.
+If using a scanner or camera that needs a "resolution" setting, you can directly use the maximum resolution calculated by the checkResolution tool.
 
 If using a scanner or camera that calculates in terms of image width and height, multiply the resolution calculated by the physical width and height: `width in pixels = width in inches x dots per inch` and  `height in pixels = height in inches x dots per inch`
 

3_Marker_Training/marker_troubleshooting.md

@@ -20,7 +20,7 @@ On Windows, type:
 
 check_id should launch and open a video window.
 
-If you also wish to display pose-estimates errors, OR you wish to check recognition of template markers, you will need to define a multi-marker configuration file first. By default, check\_id reads it's multimarker configuration from up to two multimarker configuration files specified on the command line. You can test (for example) using the pre-supplied file `Data/cubeMarkerConfig.dat` (which is set to track the cube marker whose image is supplied in PDF form in `doc/patterns/Cubes/cube00-05-a4.pdf` or `/doc/patterns/Cubes/cube00-05-latter.pdf`) using the following launch syntax.
+If you also wish to display pose-estimates errors or wish to check recognition of template markers, you will need to define a multi-marker configuration file first. By default, check\_id reads its multimarker configuration from up to two multimarker configuration files specified on the command line. You can test (for example) using the pre-supplied file `Data/cubeMarkerConfig.dat` (which is set to track the cube marker whose image is supplied in PDF form in `doc/patterns/Cubes/cube00-05-a4.pdf` or `/doc/patterns/Cubes/cube00-05-latter.pdf`) using the following launch syntax.
 On Linux / OS X, type:
 <pre>
     ./check_id Data/cubeMarkerConfig.dat

4_Android/android_about.md

@@ -14,7 +14,7 @@ The first section, *ARToolKit's SDK Structure* link below, covers the structure
 - [Developing with ARToolKitWrapper and ARBaseLib][android_developing]
 - [Native Development Information][android_native]
 - [Camera Preferences][android_preferences_activity]
-- [Using automatic online camera calibration retrieval][android_camera_calibration_service]
+- [Using Automatic Online Camera Calibration Retrieval][android_camera_calibration_service]
 - [ARToolKit Camera Calibration for Android][android_camera_calibration] - [View in Google Play Store][camera_calibration]
 - [Codex Interactivus for Android][example_codex_interactivus] - [View in Google Play Store][codex_interactivus]
 - [Advanced Device-Specific Setup - Epson Moverio BT-200][android_bt]