A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Registration of Translated and Rotated Images Using Finite Fourier Transforms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of image registration techniques
ACM Computing Surveys (CSUR)
A unified distance transform algorithm and architecture
Machine Vision and Applications
Sequential Operations in Digital Picture Processing
Journal of the ACM (JACM)
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Position-Orientation Masking Approach to Parametric Search for Template Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhanced Perceptual Distance Functions and Indexing for Image Replica Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Class of Algorithms for Fast Digital Image Registration
IEEE Transactions on Computers
A similarity metric for edge images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic target recognition by matching oriented edge pixels
IEEE Transactions on Image Processing
Extension of phase correlation to subpixel registration
IEEE Transactions on Image Processing
Generating fuzzy edge images from gradient magnitudes
Computer Vision and Image Understanding
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The problem of computer-assisted broad area search for specific objects of interest in overhead images is considered. To this end, we present a novel efficient Gradient Direction Matching (GDM) algorithm that matches gradient directions associated with object edges to pixel gradient directions (as opposed to image edges, which are less reliable). GDM seamlessly integrates information associated with pixel location and orientation in such a way that the FFT can be exploited for computational efficiency, and it inherently rejects background clutter. The effects of spatial resolution on GDM statistical performance are studied empirically with the goal of gaining insight into how far GDM computational cost can be reduced before matching performance becomes too severely compromised.