In this work, we present a new model of visual saliency by combing results from existing methods, improving upon their performance and accuracy. By fusing pre-attentive and context-aware methods, we highlight the abilities of state-of-the-art models while compensating for their deficiencies. We put this theory to the test in a series of experiments, comparatively evaluating the visual saliency maps and employing them for content-based image retrieval and thumbnail generation. We find that on average our model yields definitive improvements upon recall and f-measure metrics with comparable precisions. In addition, we find that all image searches using our fused method return more correct images and additionally rank them higher than the searches using the original methods alone.
Developments in rapid acquisition techniques and reconstruction algorithms, such as sensitivity encoding (SENSE)
for MR images and fan-beam filtered backprojection (fFBP) for CT images, have seen widely applications in
medical imaging in recent years. Nevertheless, such techniques introduce spatially varying noise levels in the
reconstructed medical images that may degrade the image quality and hinder subsequent diagnostic inspection.
Though this may be alleviated with multiple scanning images or the sensitivity profiles of imaging device, these
pieces of information are typically unavailable in clinical practice. In this work, we describe a novel local noise
level estimation technique based on the near constancy of kurtosis of medical image in band-pass filtered domain.
This technique can effectively estimate noise levels in the pixel domain and recover the noise map for
reconstructed medical images with nonuniform noise distribution. The advantage of this method is that it requires
no prior knowledge of the imaging devices and can be implemented when only one single medical image is
available. We report experiments that demonstrate the effectiveness of the proposed method in estimating the
local noise levels for medical images quantitatively and qualitatively, and compare its estimation performance
to another recent developed blind noise estimation approach. Finally, we also evaluate the practical denoising
performance of our noise estimation algorithm on medical images when it is used as a front-end to a denoiser
that uses principal component analysis with local pixel grouping (LPG-PCA) technique.
We describe an invertible nonlinear image transformation that is well-matched to the statistical properties of
photographic images, as well as the perceptual sensitivity of the human visual system. Images are first decomposed
using a multi-scale oriented linear transformation. In this domain, we develop a Markov random field
model based on the dependencies within local clusters of transform coefficients associated with basis functions
at nearby positions, orientations and scales. In this model, division of each coefficient by a particular linear
combination of the amplitudes of others in the cluster produces a new nonlinear representation with marginally
Gaussian statistics. We develop a reliable and efficient iterative procedure for inverting the divisive transformation.
Finally, we probe the statistical and perceptual advantages of this image representation, examining
robustness to added noise, rate-distortion behavior, and artifact-free local contrast enhancement.
Digital audio provides a suitable cover for high-throughput
steganography. At 16 bits per sample and sampled at a rate of 44,100
Hz, digital audio has the bit-rate to support large messages. In
addition, audio is often transient and unpredictable, facilitating the hiding of messages. Using an approach similar to our universal image steganalysis, we show that hidden messages alter the underlying
statistics of audio signals. Our statistical model begins by building
a linear basis that captures certain statistical properties of audio
signals. A low-dimensional statistical feature vector is extracted
from this basis representation and used by a non-linear support vector machine for classification. We show the efficacy of this approach on LSB embedding and Hide4PGP. While no explicit assumptions about the content of the audio are made, our technique has been developed and tested on high-quality recorded speech.
Steganographic messages can be embedded into digital images in ways
that are imperceptible to the human eye. These messages, however,
alter the underlying statistics of an image. We previously built
statistical models using first-and higher-order wavelet statistics,
and employed a non-linear support vector machines (SVM) to detect
steganographic messages. In this paper we extend these results to
exploit color statistics, and show how a one-class SVM greatly
simplifies the training stage of the classifier.
Conference Committee Involvement (2)
Computer Vision and Image Analysis of Art II
26 January 2011 | San Francisco Airport, California, United States
Computer Vision and Image Analysis of Art
18 January 2010 | San Jose, California, United States
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