status(" Libraries:" HAVE_opencv_python2 THEN "${PYTHON2_LIBRARIES}" ELSE NO)
endif()
status(" numpy:" PYTHON2_NUMPY_INCLUDE_DIRS THEN "${PYTHON2_NUMPY_INCLUDE_DIRS} (ver ${PYTHON2_NUMPY_VERSION})" ELSE "NO (Python wrappers can not be generated)")
status(" packages path:" PYTHON2_EXECUTABLE THEN "${PYTHON2_PACKAGES_PATH}" ELSE "-")
status(" install path:" HAVE_opencv_python2 THEN "${__INSTALL_PATH_PYTHON2}" ELSE "-")
endif()
if(BUILD_opencv_python3)
...
...
@@ -1491,7 +1491,7 @@ if(BUILD_opencv_python3)
status(" Libraries:" HAVE_opencv_python3 THEN "${PYTHON3_LIBRARIES}" ELSE NO)
endif()
status(" numpy:" PYTHON3_NUMPY_INCLUDE_DIRS THEN "${PYTHON3_NUMPY_INCLUDE_DIRS} (ver ${PYTHON3_NUMPY_VERSION})" ELSE "NO (Python3 wrappers can not be generated)")
status(" packages path:" PYTHON3_EXECUTABLE THEN "${PYTHON3_PACKAGES_PATH}" ELSE "-")
status(" install path:" HAVE_opencv_python3 THEN "${__INSTALL_PATH_PYTHON3}" ELSE "-")
abstract = {This work presents a new efficient method for fitting ellipses to scattered data. Previous algorithms either fitted general conics or were computationally expensive. By minimizing the algebraic distance subject to the constraint 4ac-b<sup>2</sup>=1, the new method incorporates the ellipticity constraint into the normalization factor. The proposed method combines several advantages: It is ellipse-specific, so that even bad data will always return an ellipse. It can be solved naturally by a generalized eigensystem. It is extremely robust, efficient, and easy to implement},
author = {Fitzgibbon, Andrew and Pilu, Maurizio and Fisher, Robert B.},
doi= {10.1109/34.765658},
isbn= {0162-8828},
issn= {01628828},
journal = {IEEE Transactions on Pattern Analysis and Machine
Intelligence},
doi = {10.1109/34.765658},
isbn = {0162-8828},
issn = {01628828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
number = {5},
pages= {476--480},
pmid= {708},
title= {{Direct least square fitting of ellipses}},
pages= {476--480},
pmid= {708},
title = {Direct least square fitting of ellipses},
volume = {21},
year= {1999}
}
@Article{taubin1991,
abstract = {The author addresses the problem of parametric
representation and estimation of complex planar curves in
2-D surfaces in 3-D, and nonplanar space curves in 3-D.
Curves and surfaces can be defined either parametrically or
implicitly, with the latter representation used here. A
planar curve is the set of zeros of a smooth function of
two variables <e1>x</e1>-<e1>y</e1>, a surface is the set
of zeros of a smooth function of three variables
<e1>x</e1>-<e1>y</e1>-<e1>z</e1>, and a space curve is the
intersection of two surfaces, which are the set of zeros of
two linearly independent smooth functions of three
variables <e1>x</e1>-<e1>y</e1>-<e1>z</e1> For example, the
surface of a complex object in 3-D can be represented as a
subset of a single implicit surface, with similar results
for planar and space curves. It is shown how this unified
representation can be used for object recognition, object
position estimation, and segmentation of objects into
meaningful subobjects, that is, the detection of `interest
regions' that are more complex than high curvature regions
abstract = {The author addresses the problem of parametric representation and estimation of complex planar curves in 2-D surfaces in 3-D, and nonplanar space curves in 3-D. Curves and surfaces can be defined either parametrically or implicitly, with the latter representation used here. A planar curve is the set of zeros of a smooth function of two variables <e1>x</e1>-<e1>y</e1>, a surface is the set of zeros of a smooth function of three variables <e1>x</e1>-<e1>y</e1>-<e1>z</e1>, and a space curve is the intersection of two surfaces, which are the set of zeros of two linearly independent smooth functions of three variables <e1>x</e1>-<e1>y</e1>-<e1>z</e1> For example, the surface of a complex object in 3-D can be represented as a subset of a single implicit surface, with similar results for planar and space curves. It is shown how this unified representation can be used for object recognition, object position estimation, and segmentation of objects into meaningful subobjects, that is, the detection of `interest regions' that are more complex than high curvature regions and, hence, more useful as features for object recognition},
author = {Taubin, Gabriel},
doi= {10.1109/34.103273},
isbn= {0162-8828},
issn= {01628828},
doi= {10.1109/34.103273},
isbn= {0162-8828},
issn= {01628828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
number = {11},
pages= {1115--1138},
title= {{Estimation of planar curves, surfaces, and nonplanar
space curves defined by implicit equations with
applications to edge and range image segmentation}},
pages = {1115--1138},
title = {Estimation of planar curves, surfaces, and nonplanar space curves defined by implicit equations with applications to edge and range image segmentation},
