4 research outputs found
Camera distortion self-calibration using the plumb-line constraint and minimal Hough entropy
In this paper we present a simple and robust method for self-correction of
camera distortion using single images of scenes which contain straight lines.
Since the most common distortion can be modelled as radial distortion, we
illustrate the method using the Harris radial distortion model, but the method
is applicable to any distortion model. The method is based on transforming the
edgels of the distorted image to a 1-D angular Hough space, and optimizing the
distortion correction parameters which minimize the entropy of the
corresponding normalized histogram. Properly corrected imagery will have fewer
curved lines, and therefore less spread in Hough space. Since the method does
not rely on any image structure beyond the existence of edgels sharing some
common orientations and does not use edge fitting, it is applicable to a wide
variety of image types. For instance, it can be applied equally well to images
of texture with weak but dominant orientations, or images with strong vanishing
points. Finally, the method is performed on both synthetic and real data
revealing that it is particularly robust to noise.Comment: 9 pages, 5 figures Corrected errors in equation 1
Session 7: \u3cem\u3eFinding Needles in Haystacks: Rare Category Detection using Semi-supervised Active Learning\u3c/em\u3e
Rare category detection addresses the problem of exploring data sets that are too large for unaided analysis. The Farpoint algorithm utilizes machine learning techniques to discover sparsely represented classes, through interactive queries and semi-supervised clustering. Application of the algorithm to skewed MNIST datasets is used to empirically characterize performance
Detection of Anomalies in Urban Traffic Imagery
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