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Measures of Image and Video Segmentation

Measures of Image and Video Segmentation

Pushpajit A. Khaire, Roshan R. Kotkondawar
Copyright: © 2018 |Pages: 26
ISBN13: 9781522552048|ISBN10: 1522552049|EISBN13: 9781522552055
DOI: 10.4018/978-1-5225-5204-8.ch049
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MLA

Khaire, Pushpajit A., and Roshan R. Kotkondawar. "Measures of Image and Video Segmentation." Computer Vision: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2018, pp. 1182-1207. https://doi.org/10.4018/978-1-5225-5204-8.ch049

APA

Khaire, P. A. & Kotkondawar, R. R. (2018). Measures of Image and Video Segmentation. In I. Management Association (Ed.), Computer Vision: Concepts, Methodologies, Tools, and Applications (pp. 1182-1207). IGI Global. https://doi.org/10.4018/978-1-5225-5204-8.ch049

Chicago

Khaire, Pushpajit A., and Roshan R. Kotkondawar. "Measures of Image and Video Segmentation." In Computer Vision: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1182-1207. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5204-8.ch049

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Abstract

Study on Video and Image segmentation is currently limited by the lack of evaluation metrics and benchmark datasets that covers the large variety of sub-problems appearing in image and video segmentation. Proposed chapter provides an analysis of Evaluation Metrics, Datasets for Image and Video Segmentation methods. Importance is on wide-ranging, Datasets robust Metrics which used for evaluation purposes without inducing any bias towards the evaluation results. Introductory Section discusses traditional image and video segmentation methods available, the importance and need of measures, metrics and dataset required to evaluate segmentation algorithms are discussed in next section. Main focus of the chapter explains the measures, metrics and dataset available for evaluation of segmentation techniques of both image and video. The goal is to provide details about a set of impartial datasets and evaluation metrics and to leave the final evaluation of the evaluation process to the understanding of the reader.

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