Trace Inference, Curvature Consistency, and Curve Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual grouping of curved lines
Proceedings of a workshop on Image understanding workshop
Inferring global perceptual contours from local features
International Journal of Computer Vision - Special issue on computer vision research at the University of Southern California
Local Scale Control for Edge Detection and Blur Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Perceptual Grouping by Selection of a Logically Minimal Model
International Journal of Computer Vision
Segmentation Given Partial Grouping Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
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This paper addresses the problem of grouping image primitives; its principal contribution is an explicit definition of the Gestalt principle of Prägnanz , which organizes primitives into descriptions of images that are both simple and stable. Our definition of Prägnanz assumes just two things: that a vector of free variables controls some general grouping algorithm, and a scalar function measures the information in a grouping. Stable descriptions exist where the gradient of the function is zero, and these can be ordered by information content (simplicity) to create a "grouping" or "Gestalt" scale description. We provide a simple measure for information in a grouping based on its structure alone, leaving our grouper free to exploit other Gestalt principles as we see fit. We demonstrate the value of our definition of Prägnanz on several real-world images.