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Learning to detect natural image boundaries using local brightness, color, and texture cues.
- Source :
-
IEEE transactions on pattern analysis and machine intelligence [IEEE Trans Pattern Anal Mach Intell] 2004 May; Vol. 26 (5), pp. 530-49. - Publication Year :
- 2004
-
Abstract
- The goal of this work is to accurately detect and localize boundaries in natural scenes using local image measurements. We formulate features that respond to characteristic changes in brightness, color, and texture associated with natural boundaries. In order to combine the information from these features in an optimal way, we train a classifier using human labeled images as ground truth. The output of this classifier provides the posterior probability of a boundary at each image location and orientation. We present precision-recall curves showing that the resulting detector significantly outperforms existing approaches. Our two main results are 1) that cue combination can be performed adequately with a simple linear model and 2) that a proper, explicit treatment of texture is required to detect boundaries in natural images.
- Subjects :
- Cluster Analysis
Color
Computer Graphics
Image Enhancement methods
Information Storage and Retrieval methods
Numerical Analysis, Computer-Assisted
Reproducibility of Results
Sensitivity and Specificity
User-Computer Interface
Algorithms
Artificial Intelligence
Image Interpretation, Computer-Assisted methods
Pattern Recognition, Automated
Signal Processing, Computer-Assisted
Subtraction Technique
Subjects
Details
- Language :
- English
- ISSN :
- 0162-8828
- Volume :
- 26
- Issue :
- 5
- Database :
- MEDLINE
- Journal :
- IEEE transactions on pattern analysis and machine intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 15460277
- Full Text :
- https://doi.org/10.1109/TPAMI.2004.1273918