Remove refs to composition docs

docs/analysis_approach.md

@@ -105,8 +105,6 @@ the region of interest with the [`roi_objects` function](roi_objects.md).
 
 * Once the target object or objects are segmented you then need to decide if it is desirable to connect or split the 
 objects.
-* Even if there is a single plant in an image it may be detected as multiple objects, in which case it may need to be
-joined or composed together using the [object composition function](object_composition.md).
 * If there are multiple plants in an object and you would like to analyze them individually (get shape parameters for 
 each plant for example) then there are functions in PlantCV to split the image apart so there is a single target object 
 in each sub-image. For more information on this process see the [Multi Plant Tutorial](tutorials/multi-plant_tutorial.md). 

docs/homology_y_axis_pseudolandmarks.md

@@ -4,13 +4,12 @@ Divide plant object into twenty equidistant bins along the y-axis and assign pse
 actual (not scaled) position. Once this data is scaled this approach may provide some information regarding shape 
 independent of size.
 
-**plantcv.homology.y_axis_pseudolandmarks**(*img, obj, mask, label="default"*)
+**plantcv.homology.y_axis_pseudolandmarks**(*img, mask, label="default"*)
 
 **returns** landmarks_on_leftside (left), landmarks_on_right (right), landmarks_at_center_along_the_horizontal_axis (center_h)
 
 - **Parameters:**
  - img - A copy of the original image (RGB or grayscale) generated using np.copy
- - obj - A contour of the plant object (this should be output from the object_composition.py fxn)
  - mask - This is a binary image. The object should be white and the background should be black.
  - label - Optional label parameter, modifies the variable name of observations recorded
 - **Context:**
@@ -32,7 +31,7 @@ pcv.params.debug = "plot"
 
 # Identify a set of land mark points
 # Results in set of point values that may indicate tip points
-left, right, center_h = pcv.homology.y_axis_pseudolandmarks(img=img, obj=obj, mask=mask, label="default")
+left, right, center_h = pcv.homology.y_axis_pseudolandmarks(img=img, mask=mask, label="default")
 
 # Access data stored out from y_axis_pseudolandmarks
 left_landmarks = pcv.outputs.observations['default']['left_lmk']['value']
@@ -43,4 +42,4 @@ left_landmarks = pcv.outputs.observations['default']['left_lmk']['value']
 
 ![Screenshot](img/documentation_images/y_axis_pseudolandmarks/yap_output.jpg)
 
-**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/main/plantcv/plantcv/y_axis_pseudolandmarks.py)
+**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/main/plantcv/plantcv/homology/y_axis_pseudolandmarks.py)

docs/params.md

@@ -19,7 +19,7 @@ Attributes are accessed as plantcv.params.*attribute*.
 **debug_outdir**: The directory to output debug images to when `plantcv.debug` = "print".
 
 **line_thickness**: The line thickness for plots created by [plantcv.analyze.size](analyze_size.md), [plantcv.analyze.bound_horizontal](analyze_bound_horizontal2.md).
-[plantcv.analyze.bound_vertical](analyze_bound_vertical2.md), [plantcv.roi_objects](roi_objects.md), [plantcv.object_composition](object_composition.md),
+[plantcv.analyze.bound_vertical](analyze_bound_vertical2.md), [plantcv.roi_objects](roi_objects.md),
 [plantcv.homology.scale_features](homology_scale_features.md), [plantcv.homology.x_axis_pseudolandmarks](homology_x_axis_pseudolandmarks.md), [plantcv.homology.y_axis_pseudolandmarks](homology_y_axis_pseudolandmarks.md),
 [plantcv.morphology.prune](prune.md), [plantcv.morphology.find_branch_pts](find_branch_pts.md), [plantcv.morphology.check_cycles](check_cycles.md),
  [plantcv.morphology.find_tips](find_tips.md), [plantcv.morphology.segment_skeleton](segment_skeleton.md), [plantcv.morphology.segment_tangent_angle](segment_tangent_angle.md),