HooverCompareSegmentation - Hoover compare segmentation¶

Compare two segmentations with Hoover metrics

Detailed description¶

This application compares a machine segmentation (MS) with a partial ground truth segmentation (GT). The Hoover metrics are used to estimate scores for correct detection, over-segmentation, under-segmentation and missed detection.: The application can output the overall Hoover scores along with coloredimages of the MS and GT segmentation showing the state of each region (correct detection, over-segmentation, under-segmentation, missed) The Hoover metrics are described in : Hoover et al., “An experimental comparison of range image segmentation algorithms”, IEEE PAMI vol. 18, no. 7, July 1996.

Parameters¶

This section describes in details the parameters available for this application. Table [1] presents a summary of these parameters and the parameters keys to be used in command-line and programming languages. Application key is HooverCompareSegmentation .

[1]	Table: Parameters table for Hoover compare segmentation.

Parameter Key	Parameter Name	Parameter Type
ingt	Input ground truth	Input image
inms	Input machine segmentation	Input image
bg	Background label	Int
th	Overlapping threshold	Float
outgt	Colored ground truth output	Output image
outms	Colored machine segmentation output	Output image
rc	Correct detection score	Float
rf	Over-segmentation score	Float
ra	Under-segmentation score	Float
rm	Missed detection score	Float
inxml	Load otb application from xml file	XML input parameters file
outxml	Save otb application to xml file	XML output parameters file

Input ground truth: A partial ground truth segmentation image.
Input machine segmentation: A machine segmentation image.
Background label: Label value of the background in the input segmentations.
Overlapping threshold: Overlapping threshold used to find Hoover instances.
Colored ground truth output: The colored ground truth output image.
Colored machine segmentation output: The colored machine segmentation output image.
Correct detection score: Overall score for correct detection (RC).
Over-segmentation score: Overall score for over segmentation (RF).
Under-segmentation score: Overall score for under segmentation (RA).
Missed detection score: Overall score for missed detection (RM).
Load otb application from xml file: Load otb application from xml file.
Save otb application to xml file: Save otb application to xml file.

Example¶

To run this example in command-line, use the following:

otbcli_HooverCompareSegmentation -ingt maur_GT.tif -inms maur_labelled.tif -outgt maur_colored_GT.tif uint8

To run this example from Python, use the following code snippet:

#!/usr/bin/python

# Import the otb applications package
import otbApplication

# The following line creates an instance of the HooverCompareSegmentation application
HooverCompareSegmentation = otbApplication.Registry.CreateApplication("HooverCompareSegmentation")

# The following lines set all the application parameters:
HooverCompareSegmentation.SetParameterString("ingt", "maur_GT.tif")

HooverCompareSegmentation.SetParameterString("inms", "maur_labelled.tif")

HooverCompareSegmentation.SetParameterString("outgt", "maur_colored_GT.tif")
HooverCompareSegmentation.SetParameterOutputImagePixelType("outgt", 1)

# The following line execute the application
HooverCompareSegmentation.ExecuteAndWriteOutput()

Limitations¶

None

Authors¶

This application has been written by OTB-Team.