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We present a robust shape model for localizing a set of feature points on a 2D image. Previous shape alignment models assume Gaussian observation noise and attempt to fit a regularized shape using all the observed data. However, such an assumption is vulnerable to gross feature detection errors resulted from partial occlusions or spurious background features. We address this problem by using a hypothesis-and-test...
When intelligent robot with visual guiding is cutting steel plates, the reflection interference from the captured images is possibly leading to the failure of tracking, and therefore the image processing and analysis methods is require of high robustness. An algorithm of tracking image feature points with active structured light is proposed based on comprehensive applications with advanced template-matching...
Georeferencing is a key component in GIS systems. However, its performance has been dragged by the manual georeferencing process. In this paper, we develop a scalable and error tolerant automated georeferencing scheme for general affine transformations called localized matching and global optimization (LM-GO). Under LM-GO, the complexity of control point pair (CPP) matching is not directly tied to...
Shape classification is one of the central topics in computer vision. Due to the robust shape representation, curvature scale space (CSS) has been adopted as the default in the MPEG-7 standard. One of the drawbacks of the standard CSS algorithm is its complicated and time-consuming search for all possible ways of aligning the contour maxima from both CSS images by shifting and mirroring the query...
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