TY - GEN
T1 - Color line detection
AU - Lacroix, Vinciane
PY - 2011
Y1 - 2011
N2 - Color line extraction is an important part of the segmentation process. The proposed method is the generalization of the Gradient Line Detector (GLD) to color images. The method relies on the computation of a color gradient field. Existing color gradient are not "oriented": the gradient vector direction is defined up to π, and not up to 2π as it is for a grey-level image. An oriented color gradient which makes use of an ordering of colors is proposed. Although this ordering is arbitrary, the color gradient orientation changes from one to the other side of a line; this change is captured by the GLD. The oriented color gradient is derived from a generalization from scalar to vector: the components of the gradient are defined as a "signed" distance between weighted average colors, the sign being related to their respective order. An efficient averaging method inspired by the Gaussian gradient brings a scale parameter to the line detector. For the distance, the simplest choice is the Euclidean distance, but the best choice depends on the application. As for any feature extraction process, a post-processing is necessary: local maxima should be extracted and linked into curvilinear segments. Some preliminary results using the Euclidean distance are shown on a few images.
AB - Color line extraction is an important part of the segmentation process. The proposed method is the generalization of the Gradient Line Detector (GLD) to color images. The method relies on the computation of a color gradient field. Existing color gradient are not "oriented": the gradient vector direction is defined up to π, and not up to 2π as it is for a grey-level image. An oriented color gradient which makes use of an ordering of colors is proposed. Although this ordering is arbitrary, the color gradient orientation changes from one to the other side of a line; this change is captured by the GLD. The oriented color gradient is derived from a generalization from scalar to vector: the components of the gradient are defined as a "signed" distance between weighted average colors, the sign being related to their respective order. An efficient averaging method inspired by the Gaussian gradient brings a scale parameter to the line detector. For the distance, the simplest choice is the Euclidean distance, but the best choice depends on the application. As for any feature extraction process, a post-processing is necessary: local maxima should be extracted and linked into curvilinear segments. Some preliminary results using the Euclidean distance are shown on a few images.
KW - Gaussian gradient
KW - color edge
KW - color line
KW - color ordering
UR - https://www.scopus.com/pages/publications/80053021864
U2 - 10.1007/978-3-642-24085-0_33
DO - 10.1007/978-3-642-24085-0_33
M3 - Conference contribution
AN - SCOPUS:80053021864
SN - 9783642240843
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 318
EP - 326
BT - Image Analysis and Processing, ICIAP 2011 - 16th International Conference, Proceedings
T2 - 16th International Conference on Image Analysis and Processing, ICIAP 2011
Y2 - 14 September 2011 through 16 September 2011
ER -