From c5364a5e26c451f26cb26cbf7218a36de6040404 Mon Sep 17 00:00:00 2001 From: Pietro Monticone Date: Thu, 3 Aug 2023 10:28:26 -0500 Subject: [PATCH 1/6] Update glcm.md --- docs/src/tutorials/glcm.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/src/tutorials/glcm.md b/docs/src/tutorials/glcm.md index 861a596..e8ce8a2 100644 --- a/docs/src/tutorials/glcm.md +++ b/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. From 89fab2e9edb4e8e8e97e96e981b196effd54c2bc Mon Sep 17 00:00:00 2001 From: Pietro Monticone Date: Thu, 3 Aug 2023 10:28:30 -0500 Subject: [PATCH 2/6] Update object_detection.md --- docs/src/tutorials/object_detection.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/src/tutorials/object_detection.md b/docs/src/tutorials/object_detection.md index 9488cd6..4a97fad 100644 --- a/docs/src/tutorials/object_detection.md +++ b/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 From 692deeb495e0832625d9af348de27ce1e9f4bbd7 Mon Sep 17 00:00:00 2001 From: Pietro Monticone Date: Thu, 3 Aug 2023 10:28:33 -0500 Subject: [PATCH 3/6] Update brief.jl --- src/brief.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/brief.jl b/src/brief.jl index 0e92b01..5d413d9 100644 --- a/src/brief.jl +++ b/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. | From 6cddf32d4f7450feea7576d999f0c3aa6090205a Mon Sep 17 00:00:00 2001 From: Pietro Monticone Date: Thu, 3 Aug 2023 10:28:36 -0500 Subject: [PATCH 4/6] Update glcm.jl --- src/glcm.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/glcm.jl b/src/glcm.jl index a8bdbe0..161ae6b 100644 --- a/src/glcm.jl +++ b/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. """ From 79154a0c6e8c7539ee924a7b1859fdbcd3022b41 Mon Sep 17 00:00:00 2001 From: Pietro Monticone Date: Thu, 3 Aug 2023 10:28:40 -0500 Subject: [PATCH 5/6] Update hog.jl --- src/hog.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/hog.jl b/src/hog.jl index 2db6302..4b79142 100755 --- a/src/hog.jl +++ b/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 From 87564393a8ca12e49f74999345ee653f7269b34f Mon Sep 17 00:00:00 2001 From: Pietro Monticone Date: Thu, 3 Aug 2023 10:28:44 -0500 Subject: [PATCH 6/6] Update ImageFeatures.jl --- src/ImageFeatures.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/ImageFeatures.jl b/src/ImageFeatures.jl index 932ab5b..2e18762 100644 --- a/src/ImageFeatures.jl +++ b/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,