biopixR (version 1.1.0)

objectDetection: Object detection

Description

This function identifies objects in an image using either edge detection or thresholding methods. It gathers the coordinates and centers of the identified objects, highlighting the edges or overall coordinates for easy recognition.

Usage

objectDetection(img, method = "edge", alpha = 1, sigma = 2, vis = TRUE)

Value

list of 3 objects:

• data.frame of labeled regions with the central coordinates (including size information).

• All coordinates that are in labeled regions.

• Image where object edges/coordinates (purple) and detected centers (green) are colored.

Arguments

img

image (import by importImage)

method

choose method for object detection ('edge' / 'threshold')

alpha

threshold adjustment factor (numeric / 'static' / 'interactive' / 'gaussian') (only needed for 'edge')

sigma

smoothing (numeric / 'static' / 'interactive' / 'gaussian') (only needed for 'edge')

vis

creates image were object edges/coordinates (purple) and detected centers (green) are highlighted (TRUE | FALSE)

Details

The objectDetection function provides several methods for calculating the alpha and sigma parameters, which are critical for edge detection:

1. Input of a Numeric Value:

   • Users can directly input numeric values for alpha and sigma, allowing for precise control over the edge detection parameters.

2. Static Scanning:

   • When both alpha and sigma are set to "static", the function systematically tests all possible combinations of these parameters within the range (alpha: 0.1 - 1.5, sigma: 0 - 2). This exhaustive search helps identify the optimal parameter values for the given image. (Note: takes a lot of time)

3. Interactive Selection:

   • Setting the alpha and sigma values to "interactive" initiates a Tcl/Tk graphical user interface (GUI). This interface allows users to adjust the parameters interactively, based on visual feedback. To achieve optimal results, the user must input the necessary adjustments to align the parameters with the specific requirements of the image. The user can also switch between the methods through the interface.

4. Multi-Objective Optimization:

   • For advanced parameter optimization, the function easyGParetoptim will be utilized for multi-objective optimization using Gaussian process models. This method leverages the 'GPareto' package to perform the optimization. It involves building Gaussian Process models for each objective and running the optimization to find the best parameter values.

Examples

res_objectDetection <- objectDetection(beads,
                                       method = 'edge',
                                       alpha = 1,
                                       sigma = 0)
res_objectDetection$marked_objects |> plot()

res_objectDetection <- objectDetection(beads,
                                       method = 'threshold')
res_objectDetection$marked_objects |> plot()