A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scaling Theorems for Zero Crossings
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Local Visual Operator Which Recognizes Edges and Lines
Journal of the ACM (JACM)
Boundary and Object Detection in Real World Images
Journal of the ACM (JACM)
Improving boundary detection using variable resolution masks
Pattern Recognition Letters
Performance Assessment Through Bootstrap
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge Detection with Embedded Confidence
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
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The first module is a parallel process computing local edge strength and direction, while the last module is sequential process following edges. The originality of the overall method resides in the intermediate module, which is seen as a generalization of the nonmaximum-deletion algorithm. The role of this module is twofold: It enables one to postpone some deletion to the last module where contextual information is available, and it transmits the local edge direction in order to guide the contour following. A postprocessing method called learning edges is proposed as a refinement of the method. The binary edge images extracted from various gray-level images illustrate the power of the strategy.