Two linear time Union-Find strategies for image processing
Theoretical Computer Science
View-Based Recognition Using an Eigenspace Approximation to the Hausdorff Measure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotation-invariant pattern matching using wavelet decomposition
Pattern Recognition Letters
Linear-time connected-component labeling based on sequential local operations
Computer Vision and Image Understanding
Evaluation of MPEG-7 shape descriptors against other shape descriptors
Multimedia Systems
A linear-time component-labeling algorithm using contour tracing technique
Computer Vision and Image Understanding
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast connected-component labelling in three-dimensional binary images based on iterative recursion
Computer Vision and Image Understanding
Fourier-Based Object Description in Defect Image Retrieval
Machine Vision and Applications
Hybrid object labelling in digital images
Machine Vision and Applications
A Run-Based Two-Scan Labeling Algorithm
IEEE Transactions on Image Processing
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This paper presents a Fourier descriptor based image alignment algorithm (FDBIA) for applications of automatic optical inspection (AOI) performed in real-time environment. It deliberates component detection and contour tracing algorithms and uses the magnitude and phase information of Fourier descriptors to establish correspondences between the target objects detected in the reference and the inspected images, so the parameters for aligning the two images can be estimated accordingly. To enhance the computational efficiency, the proposed component detection and contour tracing algorithms use the run length encoding (RLE) and Blobs tables to represent the pixel information in the regions of interest. The Fourier descriptors derived from the component boundaries are used to match the target objects. Finally, the transformation parameters for aligning the inspected image with the reference image are estimated based on a novel phase-shifted technique. Experimental results show that the proposed FDBIA algorithm sustains similar accuracy as achieved by the commercial software Easyfind against various rotation and translation conditions. Also, the computational time consumed by the FDBIA algorithm is significantly shorter than that by Easyfind.