Image Field Categorization and Edge/Corner Detection from Gradient Covariance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Behavior of the Laplacian of Gaussian Extrema
Journal of Mathematical Imaging and Vision
Custom-Built Moments for Edge Location
IEEE Transactions on Pattern Analysis and Machine Intelligence
A statistical-genetic algorithm to select the most significant features in mammograms
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Image quality assessment by discrete orthogonal moments
Pattern Recognition
Corisco: Robust edgel-based orientation estimation for generic camera models
Image and Vision Computing
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In this paper, a novel model-based approach is proposed for generating a set of image feature maps (or primal sketches). For each type of feature, a piecewise smooth parametric model is developed to characterize the local intensity function in an image. Projections of the intensity profile onto a set of orthogonal Zernike-moment-generating polynomials are used to estimate model-parameters and, in turn, generate the desired feature map. A small set of moment-based detectors is identified that can extract various kinds of primal sketches from intensity as well as range images. One main advantage of using parametric model-based techniques is that it is possible to extract complete information (i.e., model parameters) about the underlying image feature, which is desirable in many high-level vision tasks. Experimental results are included to demonstrate the effectiveness of proposed feature detectors