926 research outputs found
Mesh saliency via spectral processing
We propose a novel method for detecting mesh saliency, a perceptuallybased
measure of the importance of a local region on a 3D surface mesh.
Our method incorporates global considerations by making use of spectral
attributes of the mesh, unlike most existing methods which are typically
based on local geometric cues. We first consider the properties of the log-
Laplacian spectrum of the mesh. Those frequencies which show differences
from expected behaviour capture saliency in the frequency domain. Information
about these frequencies is considered in the spatial domain at multiple
spatial scales to localise the salient features and give the final salient
areas. The effectiveness and robustness of our approach are demonstrated
by comparisons to previous approaches on a range of test models. The benefits
of the proposed method are further evaluated in applications such as
mesh simplification, mesh segmentation and scan integration, where we
show how incorporating mesh saliency can provide improved results
Quantitative Analysis of Saliency Models
Previous saliency detection research required the reader to evaluate
performance qualitatively, based on renderings of saliency maps on a few
shapes. This qualitative approach meant it was unclear which saliency models
were better, or how well they compared to human perception. This paper provides
a quantitative evaluation framework that addresses this issue. In the first
quantitative analysis of 3D computational saliency models, we evaluate four
computational saliency models and two baseline models against ground-truth
saliency collected in previous work.Comment: 10 page
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
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