Benchtop Methodology For Evaluating The Automatic Segmentation Of Ladar Images
AUTOMATIC TARGET RECOGNITION XI(2001)
摘要
Numerous approaches to segmentation exist requiring an evaluation technique to determine the most appropriate technique to use for a specific ladar design. A benchtop evaluation methodology that uses multiple measures is used to evaluate ladar-specific image segmentation algorithms. The method uses multiple measures along with an inter-algorithmic approach that was recently introduced for evaluating Synthetic Aperture Radar (SAR) imagery. Ladar imagery is considered to be easier to segment than SAR since it generally contains less speckle and has both a range and intensity map to assist in segmentation. A system of multiple measures focuses on area, shape and edge closeness to judge the segmentation. The judgement is made on the benchtop by comparing the segmentation to supervised hand-segmented images. To demonstrate the approach, a ladar image is segmented using several segmentation approaches introduced in literature. The system of multiple measures is then demonstrated on the segmented ladar images. An interpretation of the results is given. The system of multiple measures is demonstrated as capable of evaluating a single object in the image scene or multiple objects. This paper demonstrates that the original evaluation approach designed for evaluating SAR imagery can be generalized across differing sensor modalities even though the segmentation and sensor acquisition approaches are different.
更多查看译文
关键词
segmentation, edge detection, image segmentation, evaluation, quality, metrics, measures, LADAR
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络