Fix typos in docs and docstrings

docs/src/tutorials/glcm.md

@@ -2,7 +2,7 @@
 
 Once a spatial relationship is defined, we create a GLCM of size (Range of Intensities x Range of Intensities) all initialised to `0`. For eg, a `8` bit single channel Image will have a `256x256` GLCM. We then traverse through the image and for every pair of intensities we find for the defined spatial relationship, we increment that cell of the matrix.
 
-![Gray Level Co-occurence Matrix](../img/glcm.png)
+![Gray Level Co-occurrence Matrix](../img/glcm.png)
 
 Each entry of the GLCM[i,j] holds the count of the number of times that pair of intensities appears in the image with the defined spatial relationship.
 

docs/src/tutorials/object_detection.md

@@ -95,7 +95,7 @@ end
 ![Original](../img/scores.png)
 
 You can see that classifier gave low score to not-human class (i.e. high score to human class) at positions corresponding to humans in the original image. 
-Below we threshold the image and supress non-minimal values to get the human locations. We then plot the bounding boxes using `ImageDraw`.
+Below we threshold the image and suppress non-minimal values to get the human locations. We then plot the bounding boxes using `ImageDraw`.
 
 ```julia
 using ImageDraw, ImageView

src/ImageFeatures.jl

@@ -36,7 +36,7 @@ export
     lbp_rotation_invariant,
     multi_block_lbp,
 
-    #Gray Level Co Occurence Matrix
+    #Gray Level Co-Occurrence Matrix
     glcm,
     glcm_symmetric,
     glcm_norm,

src/brief.jl

@@ -7,7 +7,7 @@ brief_params = BRIEF([size = 128], [window = 9], [sigma = 2 ^ 0.5], [sampling_ty
 | :------- | :--- | :---------- |
 | **size** | Int | Size of the descriptor |
 | **window** | Int | Size of sampling window |
-| **sigma** | Float64 | Value of sigma used for inital gaussian smoothing of image |
+| **sigma** | Float64 | Value of sigma used for initial gaussian smoothing of image |
 | **sampling_type** | Function | Type of sampling used for building the descriptor (See [BRIEF Sampling Patterns](#brief-sampling-patterns)) |
 | **seed** | Int | Random seed used for generating the sampling pairs. For matching two descriptors, the seed used to build both should be same. |
 

src/glcm.jl

@@ -4,7 +4,7 @@
     glcm = glcm(img, distance, angles, mat_size=16)
     glcm = glcm(img, distances, angles, mat_size=16)
 
-Calculates the GLCM (Gray Level Co-occurence Matrix) of an image. The `distances` and `angles` arguments may be
+Calculates the GLCM (Gray Level Co-occurrence Matrix) of an image. The `distances` and `angles` arguments may be
 a single integer or a vector of integers if multiple GLCMs need to be calculated. The `mat_size` argument is used
 to define the granularity of the GLCM.
 """

src/hog.jl

@@ -3,7 +3,7 @@
 hog_params = HOG([orientations = 9], [cell_size = 8], [block_size = 2], [block_stride = 1], [norm_method = "L2-norm"])
 ```
 
-Histogram of Oriented Gradient (HOG) is a dense feature desciptor usually used for object detection. See "Histograms of Oriented Gradients for Human Detection" by Dalal and Triggs.
+Histogram of Oriented Gradient (HOG) is a dense feature descriptor usually used for object detection. See "Histograms of Oriented Gradients for Human Detection" by Dalal and Triggs.
 
 Parameters:
 -    orientations   = number of orientation bins