Interactive Segmentation Techniques: Algorithms and by Jia He, Chang-Su Kim, C.-C. Jay Kuo

By Jia He, Chang-Su Kim, C.-C. Jay Kuo

This booklet makes a speciality of interactive segmentation thoughts, that have been greatly studied in contemporary many years. Interactive segmentation emphasizes transparent extraction of gadgets of curiosity, whose destinations are approximately indicated via human interactions in line with excessive point perception.

This e-book will first introduce vintage graph-cut segmentation algorithms after which talk about cutting-edge options, together with graph matching tools, area merging and label propagation, clustering equipment, and segmentation equipment according to facet detection.

A comparative research of those tools can be supplied with quantitative and qualitative functionality overview, as a way to be illustrated utilizing normal and artificial images.

Also, large statistical functionality comparisons should be made. professionals and cons of those interactive segmentation equipment could be mentioned, and their purposes can be mentioned.

There were just a couple of surveys on interactive segmentation innovations, and people surveys don't hide fresh state-of-the paintings strategies. through delivering finished up to date survey at the speedy constructing subject and the functionality review, this e-book might help readers research interactive segmentation strategies fast and carefully.

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Additional resources for Interactive Segmentation Techniques: Algorithms and Performance Evaluation (Springer Briefs in Electrical and Computer Engineering — Signal Processing)

Sample text

The complexity of the resulting algorithm can be near-linear, and the memory requirement is reduced while the segmentation quality can be preserved. 8 Discussion The graph-cut segmentation method is popular in practical applications and becomes one of the most important interactive segmentation techniques because of its solid theoretical foundation and good performance. The min-cut/max-flow framework is based on the maximal a posteriori (MAP), which is the conditional probability of user interactions.

Attribute u is typically chosen to be the normalized mean intensity of a sub-region. Any two adjacent sub-regions have an edge. To explore the structural information of the graph, each directed edge is assigned a vector denoted by v, which has a direction and a magnitude. 62) where θ is the angle between vectors v1 and v2 and |v1 | and |v2 | are their magnitudes. The graph of an input image yields the input graph, G i , while the image with user scribbles can be represented by a model graph denoted by G m .

22). 27) s B (xi ) = ∞, if xi ∈ Ω F , 0, Otherwise. 29) and and G l (xi ) = which is normalized by the foreground and background geodesic distance D F (xi ) and D B (xi ). Price et al. [12] redefined this regional term and minimized the cut cost in Eq. 4). 30) (i, j)∈E where E 2 is the same boundary term as that in Eq. 16), and λ R and λ B are parameters used to weight the relative importance of the region and boundary components. A greater value in λ R helps reduce the short-cutting problem, which is caused by a small boundary cost term.

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