2013
DOI: 10.1016/j.patrec.2013.04.026
|View full text |Cite
|
Sign up to set email alerts
|

Using classifiers as heuristics to describe local structure in Active Shape Models with small training sets

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2

Citation Types

0
2
0

Year Published

2013
2013
2016
2016

Publication Types

Select...
1
1
1

Relationship

1
2

Authors

Journals

citations
Cited by 3 publications
(2 citation statements)
references
References 12 publications
0
2
0
Order By: Relevance
“…It is also important to note that the search method does not really need high precision levels in the estimation of the true position of the landmark. In Tedín et al [9] the authors proposed the Heuristic Local Appearance Model (HLAM). They successfully applied it for a small training set of hands under varying illumination and for a synthetic training set where the images presented spurious borders that fooled other local appearance models.…”
Section: A Active Shape Modelsmentioning
confidence: 99%
See 1 more Smart Citation
“…It is also important to note that the search method does not really need high precision levels in the estimation of the true position of the landmark. In Tedín et al [9] the authors proposed the Heuristic Local Appearance Model (HLAM). They successfully applied it for a small training set of hands under varying illumination and for a synthetic training set where the images presented spurious borders that fooled other local appearance models.…”
Section: A Active Shape Modelsmentioning
confidence: 99%
“…Here the same classifier as in Tedín et al [9] was chosen: the M5' classifier [10]. This classifier is implemented in the well-known WEKA [11] data mining software distribution, and this is the implementation used here.…”
Section: A Active Shape Modelsmentioning
confidence: 99%