With the aim of evaluating image forensics tools, we propose a methodology to create forgeries traces, leaving intact the semantics of the image.
The Rational Polynomial Camera (RPC) model can be used to describe a variety of image acquisition systems in remote sensing, notably optical and Synthetic Aperture Radar (SAR) sensors.
Moreover, it is very difficult to change this order, because once the image is demosaicked, the statistical properties of the noise will be changed dramatically.
In this paper, we review the main variants of these strategies and carry-out an extensive evaluation to find the best way to reconstruct full color images from a noisy mosaic.
Anomaly detectors address the difficult problem of detecting automatically exceptions in an arbitrary background image.
Modeling the processing chain that has produced a video is a difficult reverse engineering task, even when the camera is available.
Using a computational quantitative version of the non-accidentalness principle, we raise the possibility that the psychophysical and the (older) gestaltist setups, both applicable on dot or Gabor patterns, find a useful complement in a Turing test.
This yields RANSAAC, a framework that improves systematically over RANSAC and its state-of-the-art variants by statistically aggregating hypotheses.
In this paper, we reconsider the early computer vision bottom-up program, according to which higher level features (geometric structures) in an image could be built up recursively from elementary features by simple grouping principles coming from Gestalt theory.
In practice, however, scale invariance may be weakened by various sources of error inherent to the SIFT implementation affecting the stability and accuracy of keypoint detection.
We apply this variant to revisit the popular benchmark by Mikolajczyk et al., on classic and new feature detectors.
We present a novel method for automatic vanishing point detection based on primal and dual point alignment detection.