A Computational Approach to Edge Detection
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On the Detection of Dominant Points on Digital Curves
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Curve matching and stereo calibration
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An Active Testing Model for Tracking Roads in Satellite Images
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Inferring global perceptual contours from local features
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Edge Detection and Ridge Detection with Automatic Scale Selection
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Embedding Gestalt Laws in Markov Random Fields
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Edge Detection by Helmholtz Principle
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Shape Matching and Object Recognition Using Shape Contexts
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Statistical Edge Detection: Learning and Evaluating Edge Cues
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Class-Specific, Top-Down Segmentation
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Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
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A Grouping Principle and Four Applications
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Salient Closed Boundary Extraction with Ratio Contour
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Segmentation Induced by Scale Invariance
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Large Margin Methods for Structured and Interdependent Output Variables
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Discriminative Sparse Image Models for Class-Specific Edge Detection and Image Interpretation
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Multi-scale Improves Boundary Detection in Natural Images
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Search-based structured prediction
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From Images to Shape Models for Object Detection
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Boundary detection using f-measure-, filter- and feature- (F3) boost
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Contour Detection and Hierarchical Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Our goal is to detect boundaries of objects or surfaces occurring in an arbitrary image. We present a new approach that discovers boundaries by sequential labeling of a given set of image edges. A visited edge is labeled as on or off a boundary, based on the edge's photometric and geometric properties, and evidence of its perceptual grouping with already identified boundaries. We use both local Gestalt cues (e.g., proximity and good continuation), and the global Helmholtz principle of non-accidental grouping. A new formulation of the Helmholtz principle is specified as the entropy of a layout of image edges. For boundary discovery, we formulate a new, policy iteration algorithm, called SLEDGE. Training of SLEDGE is iterative. In each training image, SLEDGE labels a sequence of edges, which induces loss with respect to the ground truth. These sequences are then used as training examples for learning SLEDGE in the next iteration, such that the total loss is minimized. For extracting image edges that are input to SLEDGE, we use our new, low-level detector. It finds salient pixel sequences that separate distinct textures within the image. On the benchmark Berkeley Segmentation Datasets 300 and 500, our approach proves robust and effective. We outperform the state of the art both in recall and precision for different input sets of image edges.