Modeling the Importance of Faces in Natural Images
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Statistical Parametric Mapping (SPM) is a widely deployed tool for detecting and analyzing brain activity from fMRI data. One of SPM's main features is smoothing the data by a Gaussian filter to increase the SNR. The subsequent statistical inference is bas ...
Detection of visually salient image regions is useful for applications like object segmentation, adaptive compression, and object recognition. Recently, full-resolution salient maps that retain well-defined boundaries have attracted attention. In these map ...
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Detection of visually salient image regions is useful for applications like object segmentation, adaptive compression, and object recognition. In this paper, we introduce a method for salient region detection that outputs full resolution saliency maps with ...
This thesis is dedicated to the study of a new framework linking image segmentation and shape recognition processes in the image processing domain. Image segmentation process aims at separating an image into homogenous regions, the homogeneity measure depe ...
Spectral prediction models for halftone prints generally assume homogeneously thick and sharply edged ink dots, i.e., bilevel halftones. In real prints, the ink thickness often decreases at the boundaries of the ink dots, thereby forming continuous-level h ...
Functional magnetic resonance imaging (fMRI) is a recent, non-invasive technique that allows the measurement of brain metabolism while a subject is performing specific motor or cognitive tasks. The practical difficulty is that the signal changes are very s ...
Visual behavior recognition is currently a highly active research area. This is due both to the scientific challenge posed by the complexity of the task, and to the growing interest in its applications, such as automated visual surveillance, human-computer ...
We develop a statistical model to describe the spatially varying behavior of local neighborhoods of coefficients in a multi- scale image representation. Neighborhoods are modeled as samples of a multivariate Gaussian density that are modulated and rotated ...
In this paper, the steady-state performance of the distributed least mean-squares (dLMS) algorithm within an incremental network is evaluated without the restriction of Gaussian distributed inputs. Computer simulations are presented to verify the derived p ...
A new method of image segmentation based on building up a hierarchical tree through non-linear scale-space is proposed. Non-linear scale space creates from the original image the whole family of blurred images but keeps sharp region boundaries. This charac ...
