Publication

Towards Perceptually Driven Segmentation Evaluation Metrics

Touradj Ebrahimi
2004
Conference Papers

Abstract

To be reliable, an automatic segmentation evaluation metric has to be validated by subjective tests. In this paper, a formal protocol for subjective tests for segmentation quality assessment is presented. The most common artifacts produced by segmentation algorithms are identified and an extensive analysis of their effects on the perceived quality is performed. A psychophysical experiment was performed to assess the quality of video with segmentation errors. The results show how an objective segmentation evaluation metric can be defined as a function of various error types.

Official source

https://infoscience.epfl.ch/entities/publication/c3ec8b56-aa3a-4aae-909e-ec1909bbfbe2

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Touradj Ebrahimi
2004
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/c3ec8b56-aa3a-4aae-909e-ec1909bbfbe2

About this result

Related concepts (32)

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In and computer vision, image segmentation is the process of partitioning a into multiple image segments, also known as image regions or image objects (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.

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Related publications (4)

Towards Perceptually Driven Segmentation Evaluation Metrics

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