A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Image Analysis Using Irregular Tessellations
IEEE Transactions on Pattern Analysis and Machine Intelligence
The adaptive pyramid: a framework for 2D image analysis
CVGIP: Image Understanding
Picture Segmentation by a Tree Traversal Algorithm
Journal of the ACM (JACM)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual Organization for Artificial Vision Systems
Perceptual Organization for Artificial Vision Systems
Evolutionary Approaches to Figure-Ground Separation
Applied Intelligence
Figure-Ground Discrimination: A Combinatorial Optimization Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Triangle: Engineering a 2D Quality Mesh Generator and Delaunay Triangulator
FCRC '96/WACG '96 Selected papers from the Workshop on Applied Computational Geormetry, Towards Geometric Engineering
Image Segmentation Using Local Variation
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Vectorized image segmentation via trixel agglomeration
Pattern Recognition
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
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We present a novel multi-scale image segmentation approach based on irregular triangular and polygonal tessellations produced by proximity graphs. Our approach consists of two separate stages: polygonal seeds generation followed by an iterative bottom-up polygon agglomeration. We employ constrained Delaunay triangulation combined with the principles known from visual perception to extract an initial irregular polygonal tessellation of the image. These initial polygons are built upon a triangular mesh composed of irregular sized triangles, whose spatial arrangement is adapted to the image content. We represent the image as a graph with vertices corresponding to the built polygons and edges reflecting polygon relations. The segmentation problem is then formulated as Minimum Spanning Tree (MST) construction. We build a successive fine-to-coarse hierarchy of irregular polygonal partitions by an iterative graph contraction. It uses local information and merges the polygons bottom-up based on local region- and edge- based characteristics.