@conference {18285,
	title = {Tampering identification using Empirical Frequency Response},
	booktitle = {Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on},
	year = {2009},
	month = {2009/04//},
	pages = {1517 - 1520},
	abstract = {With the widespread popularity of digital images and the presence of easy-to-use image editing software, content integrity can no longer be taken for granted, and there is a strong need for techniques that not only detect the presence of tampering but also identify its type. This paper focusses on tampering-type identification and introduces a new approach based on the empirical frequency response (EFR) to address this problem. We show that several types of tampering operations, both linear shift invariant (LSI) and non-LSI, can be characterized consistently and distinctly by their EFRs. We then extend the approach to estimate the EFR for scenarios where only the final image is available. Theoretical reasoning supported by experimental results verify the effectiveness of this method for identifying the type of a tampering operation.},
	keywords = {aproach;JPEG, coding;multimedia, compression;frequency, compression;LSI, computing;security, data;, editing, EFR, forensics;tampering-type, Frequency, identification;data, image, invariant;multimedia, of, response;image, response;linear, shift, software;empirical, system;digital},
	doi = {10.1109/ICASSP.2009.4959884},
	author = {Chuang,Wei-Hong and Swaminathan,A. and M. Wu}
}
@article {12525,
	title = {Vehicle Speed Estimation Using Acoustic Wave Patterns},
	journal = {Signal Processing, IEEE Transactions on},
	volume = {57},
	year = {2009},
	month = {2009/01//},
	pages = {30 - 47},
	abstract = {We estimate a vehicle{\textquoteright}s speed, its wheelbase length, and tire track length by jointly estimating its acoustic wave pattern with a single passive acoustic sensor that records the vehicle{\textquoteright}s drive-by noise. The acoustic wave pattern is determined using the vehicle{\textquoteright}s speed, the Doppler shift factor, the sensor{\textquoteright}s distance to the vehicle{\textquoteright}s closest-point-of-approach, and three envelope shape (ES) components, which approximate the shape variations of the received signal{\textquoteright}s power envelope. We incorporate the parameters of the ES components along with estimates of the vehicle engine RPM, the number of cylinders, and the vehicle{\textquoteright}s initial bearing, loudness and speed to form a vehicle profile vector. This vector provides a <i>fingerprint</i> that can be used for vehicle identification and classification. We also provide possible reasons why some of the existing methods are unable to provide unbiased vehicle speed estimates using the same framework. The approach is illustrated using vehicle speed estimation and classification results obtained with field data.},
	keywords = {acoustic, Doppler, estimation;passive, estimation;road, estimation;wheelbase, factor;acoustic, identification;vehicle, length;Doppler, length;vehicle, likelihood, patterns;envelope, processing;maximum, profile, sensor;tire, shape;maximum, shift, shift;acoustic, signal, speed, track, vector;vehicle, vehicles;, wave},
	isbn = {1053-587X},
	doi = {10.1109/TSP.2008.2005750},
	author = {Cevher, V. and Chellapa, Rama and McClellan, J.H.}
}
@conference {13237,
	title = {Improved fast gauss transform and efficient kernel density estimation},
	booktitle = {Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on},
	year = {2003},
	month = {2003/10//},
	pages = {664 -671 vol.1 - 664 -671 vol.1},
	abstract = {Evaluating sums of multivariate Gaussians is a common computational task in computer vision and pattern recognition, including in the general and powerful kernel density estimation technique. The quadratic computational complexity of the summation is a significant barrier to the scalability of this algorithm to practical applications. The fast Gauss transform (FGT) has successfully accelerated the kernel density estimation to linear running time for low-dimensional problems. Unfortunately, the cost of a direct extension of the FGT to higher-dimensional problems grows exponentially with dimension, making it impractical for dimensions above 3. We develop an improved fast Gauss transform to efficiently estimate sums of Gaussians in higher dimensions, where a new multivariate expansion scheme and an adaptive space subdivision technique dramatically improve the performance. The improved FGT has been applied to the mean shift algorithm achieving linear computational complexity. Experimental results demonstrate the efficiency and effectiveness of our algorithm.},
	keywords = {adaptive, algorithm;multivariate, complexity;computer, complexity;Gaussian, computational, density, estimation;mean, expansion, Gauss, processes;computational, recognition;quadratic, scheme;pattern, shift, space, subdivision, technique;computer, theory;, transform;kernel, vision;estimation, vision;fast},
	doi = {10.1109/ICCV.2003.1238383},
	author = {Yang,C. and Duraiswami, Ramani and Gumerov, Nail A. and Davis, Larry S.}
}
