A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward a Symbolic Representation of Intensity Changes in Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Description of interest regions with local binary patterns
Pattern Recognition
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
A completed modeling of local binary pattern operator for texture classification
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
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Gradient calculation and edge detection are well-known problems in image processing and the fundament for many approaches for line detection, segmentation, contour extraction, or model fitting. A large variety of algorithms for edge detection already exists but strong image noise is still a challenge. Especially in automatic surveillance and reconnaissance applications with visual-optical, infrared, or SAR imagery, high distance to objects and weak signal-to-noise-ratio are difficult tasks to handle. In this paper, a new approach using Local Binary Patterns (LBPs) is presented, which is a crossover between texture analysis and edge detection. It shows similar results as the Canny edge detector under normal conditions but performs better in presence of noise. This characteristic is evaluated quantitatively with different artificially generated types and levels of noise in synthetic and natural images.