In this paper, we describe a fully automatic approach for detecting and matching geometrical corner feature correspondences between aerial images with larger scale and view variations. The main assumption of the approach is the fact that many man-made environments contain a large number of parallel linear features. We exploit this observation towards efficient detection and estimation of vanishing points. Given the vanishing points within an image, building geometrical corner features are obtained by the intersections of pairs of building outlines corresponding to different vanishing points. The experiments performed on the infrared aerial image sequences evaluate the stability and distinctiveness of the proposed features which are undergone appearance changes due to projective deformation.
This paper presents a scheme for estimating two-band amplitude scale attack within a quantization-based watermarking context. Quantization-based watermarking schemes comprise a class of watermarking schemes that achieves the channel capacity in terms of additive noise attacks. Unfortunately, Quantization-based watermarking schemes are not robust against Linear Time Invariant (LTI) filtering attacks. We concentrate on a multi-band amplitude scaling attack that modifies the spectrum of the signal using an analysis/synthesis filter bank. First we derive the probability density function (PDF) of the attacked data. Second, using a simplified approximation of the PDF model, we derive a Maximum Likelihood (ML) procedure for estimating two-band amplitude scaling factor. Finally, experiments are performed with synthetic and real audio signals showing the good performance of the proposed estimation technique under realistic conditions.