A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Widths Yield Reliable Finite Differences (Computer Vision)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge and Line Feature Extraction Based on Covariance Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of intensity connectedness for image processing
Pattern Recognition Letters
Graphical Models and Image Processing
Comparison of edge detectors: a methodology and initial study
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Edge Detection: Learning and Evaluating Edge Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selection weighted vector directional filters
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
Unimodal thresholding for edge detection
Pattern Recognition
Gaussian-based edge-detection methods-a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A similarity metric for edge images
IEEE Transactions on Pattern Analysis and Machine Intelligence
The design of two-dimensional gradient estimators based on one-dimensional operators
IEEE Transactions on Image Processing
Automatic gradient threshold determination for edge detection
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
On optimal linear filtering for edge detection
IEEE Transactions on Image Processing
Differentiation of discrete multidimensional signals
IEEE Transactions on Image Processing
Grayscale level connectivity: theory and applications
IEEE Transactions on Image Processing
Embedded real-time video processing system on FPGA
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
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One of the fastest methods of localizing edges in images is based on small gradient kernels, such as Sobel, Prewitt, and Roberts. Although small gradient kernels provide a fast way of computing the gradients, they have little control over noise, edge location, and edge orientation. They are known to be only sensitive to step edges and fail to detect smooth boundaries. On the other hand, large kernels provide superior noise suppression characteristics, but they suffer from wide response area around edges. They cause edges of neighboring objects to merge due to their wide support. Problems associated with large gradient kernels prevent their widespread usage. This paper presents a fuzzy topology-based method to facilitate the use of larger gradient kernels. The new method effectively limits the response area around the edge and prevents neighboring objects to affect each other. Synthetic images are used to show the superior noise suppression properties and response characteristics to both step and ramp edges. Natural images are also used to assess the performance of the newly proposed topological gradient estimation qualitatively.