617 research outputs found
A PatchMatch-based Dense-field Algorithm for Video Copy-Move Detection and Localization
We propose a new algorithm for the reliable detection and localization of
video copy-move forgeries. Discovering well crafted video copy-moves may be
very difficult, especially when some uniform background is copied to occlude
foreground objects. To reliably detect both additive and occlusive copy-moves
we use a dense-field approach, with invariant features that guarantee
robustness to several post-processing operations. To limit complexity, a
suitable video-oriented version of PatchMatch is used, with a multiresolution
search strategy, and a focus on volumes of interest. Performance assessment
relies on a new dataset, designed ad hoc, with realistic copy-moves and a wide
variety of challenging situations. Experimental results show the proposed
method to detect and localize video copy-moves with good accuracy even in
adverse conditions
Image Recognition Using Modified Zernike Moments
Abstract: Zernike moments are complex moments with the orthogonal Zernike polynomials as kernel function, compared with other moments; Zernike moments have greater advantages in image rotation and low noise sensitivity. Because of the Zernike moments have image rotation invariance, and can construct arbitrary high order moments, it can be used for target recognition. In this paper, the Zernike moment algorithm is improved, which makes it having scale invariance in the processing of digital image. At last, an application of the improved Zernike moments in image recognition is given. Copyright © 2014 IFSA Publishing, S. L
An Evaluation of Popular Copy-Move Forgery Detection Approaches
A copy-move forgery is created by copying and pasting content within the same
image, and potentially post-processing it. In recent years, the detection of
copy-move forgeries has become one of the most actively researched topics in
blind image forensics. A considerable number of different algorithms have been
proposed focusing on different types of postprocessed copies. In this paper, we
aim to answer which copy-move forgery detection algorithms and processing steps
(e.g., matching, filtering, outlier detection, affine transformation
estimation) perform best in various postprocessing scenarios. The focus of our
analysis is to evaluate the performance of previously proposed feature sets. We
achieve this by casting existing algorithms in a common pipeline. In this
paper, we examined the 15 most prominent feature sets. We analyzed the
detection performance on a per-image basis and on a per-pixel basis. We created
a challenging real-world copy-move dataset, and a software framework for
systematic image manipulation. Experiments show, that the keypoint-based
features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and
Zernike features perform very well. These feature sets exhibit the best
robustness against various noise sources and downsampling, while reliably
identifying the copied regions.Comment: Main paper: 14 pages, supplemental material: 12 pages, main paper
appeared in IEEE Transaction on Information Forensics and Securit
Applying machine learning methods for characterization of hexagonal prisms from their 2D scattering patterns – an investigation using modelled scattering data
This document is the Accepted Manuscript version of the following article: Emmanuel Oluwatobi Salawu, Evelyn Hesse, Chris Stopford, Neil Davey, and Yi Sun, 'Applying machine learning methods for characterization of hexagonal prisms from their 2D scattering patterns – an investigation using modelled scattering data', Journal of Quantitative Spectroscopy and Radiative Transfer, Vol. 201, pp. 115-127, first published online 5 July 2017. Under embargo. Embargo end date: 5 July 2019. The Version of Record is available online at doi: https://doi.org/10.1016/j.jqsrt.2017.07.001. © 2017 Elsevier Ltd. All rights reserved.Better understanding and characterization of cloud particles, whose properties and distributions affect climate and weather, are essential for the understanding of present climate and climate change. Since imaging cloud probes have limitations of optical resolution, especially for small particles (with diameter < 25 μm), instruments like the Small Ice Detector (SID) probes, which capture high-resolution spatial light scattering patterns from individual particles down to 1 μm in size, have been developed. In this work, we have proposed a method using Machine Learning techniques to estimate simulated particles’ orientation-averaged projected sizes (PAD) and aspect ratio from their 2D scattering patterns. The two-dimensional light scattering patterns (2DLSP) of hexagonal prisms are computed using the Ray Tracing with Diffraction on Facets (RTDF) model. The 2DLSP cover the same angular range as the SID probes. We generated 2DLSP for 162 hexagonal prisms at 133 orientations for each. In a first step, the 2DLSP were transformed into rotation-invariant Zernike moments (ZMs), which are particularly suitable for analyses of pattern symmetry. Then we used ZMs, summed intensities, and root mean square contrast as inputs to the advanced Machine Learning methods. We created one random forests classifier for predicting prism orientation, 133 orientation-specific (OS) support vector classification models for predicting the prism aspect-ratios, 133 OS support vector regression models for estimating prism sizes, and another 133 OS Support Vector Regression (SVR) models for estimating the size PADs. We have achieved a high accuracy of 0.99 in predicting prism aspect ratios, and a low value of normalized mean square error of 0.004 for estimating the particle’s size and size PADs.Peer reviewe
- …