Tilings and patterns
Extracting periodicity of a regular texture based on autocorrelation functions
Pattern Recognition Letters
Detecting, localizing and grouping repeated scene elements from an image
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Geometric Grouping of Repeated Elements within Images
Shape, Contour and Grouping in Computer Vision
A Computational Model for Periodic Pattern Perception Based on Frieze and Wallpaper Groups
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture periodicity detection: features, properties, and comparisons
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
An evolutionary system for near-regular texture synthesis
Pattern Recognition
| Hi-index | 0.00 |
In repeated pattern analysis, peak number detection in autocorrelation is of key importance, which subsequently determines the correctness of the constructed lattice. Previous work inevitably needs users to select peak number manually, which limits its generalization to applications in large image database. The main contribution of this paper is to propose an optimization-based approach for automatic peak number detection, i.e., we first formulate it as an optimization problem by a straightforward yet effective criterion function, and then resort to Simulated Annealing to optimize it. Based on this approach, we design a new feature to depict image symmetry property which can be automatically extracted for repeated pattern retrieval. Experimental results demonstrate the effectiveness of the optimization approach and the superiority of symmetry feature over wavelet feature in discriminating similar repeated patterns.