Influence of segmentation over feature measurement

Zhang, Yu Jin

doi:10.1016/0167-8655(94)00083-f

Cited by 19 publications

(9 citation statements)

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“…Some examples of geometric features are the area, bending energy, eccentricity, form factor, normalized mean absolute curvature, perimeter and sphericity of objects. [40] Among them, the area of objects is more suitable than others to appraise the quality of differently segmented images. [2,40] …”

Section: < 6 >mentioning

confidence: 99%

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A survey on evaluation methods for image segmentation

Zhang

1996

Pattern Recognition

916

501

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This paper studies different methods proposed so far for segmentation evaluation. Most methods can be classified into three groups: the analytical, the empirical goodness and the empirical discrepancy groups. Each group has its own characteristics. After a brief description of each method in every group, some comparative discussions about different method groups are first carried out. An experimental comparison for some empirical (goodness and discrepancy) methods commonly used is then performed to provide a rank of their evaluation abilities. In addition, some special methods are also discussed. This study is helpful for an appropriate use of existing evaluation methods and for improving their performance as well as for systematically designing new evaluation methods.

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Section: < 6 >mentioning

confidence: 99%

“…[40] Among them, the area of objects is more suitable than others to appraise the quality of differently segmented images. [2,40] …”

Section: < 6 >mentioning

confidence: 99%

A survey on evaluation methods for image segmentation

Zhang

1996

Pattern Recognition

916

501

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“…The number of pixels incorrectly classified as edge pixels or the number of incorrectly segmented pixels, their position and the number of regions are among the different discrepancy measures proposed in the literature (Baddeley, 1992;Goumeidane et al, 2003;Heyden, 1989;Huang and Dom, 1995;Lewis and Brown, 2001;Lim and Lee, 1990;Odet et al, 2002;Pratt, 1978;Rees et al, 2002;RomanRoldan et al, 2001;Strasters and Gerbrands, 1991;Weszka and Rosenfeld, 1978;Yasnoff and Bacus, 1984;Yasnoff et al, 1977). Additional discrepancy measures based on region features such as area, eccentricity or perimeter, among others, have also been considered (Zhang, 1995;Gerbrands, 1992, 1994).…”

Section: Discussion On General Work On Segmentation Performance Evalumentioning

confidence: 97%

On the use of the overlapping area matrix for image segmentation evaluation: A survey and new performance measures

Ortiz

Oliver

2006

Pattern Recognition Letters

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“…The reference image is often obtained manually with the help of a human expert, and the segmented image is from a segmentation algorithm. Common error measures are the number of mis-segmented pixels [4,5], the position of mis-segmented pixels [6], the number of objects in the image [7,14], or the geometric features of segmented objects such as area, perimeter, or sphericity [8]. Almost all empirical methods are constructed by considering image segmentation as a process of pixel labeling, except for [14] which focuses on the number of objects exclusively with regard to the sizes.…”

Section: Introductionmentioning

confidence: 99%