29-12-2012, 06:07 PM
Spatiotemporal Gabor filters: a new method for dynamic texture recognition
Spatiotemporal Gabor filters a new method for dynamic texture recognition.pdf (Size: 941.49 KB / Downloads: 28)
Abstract
This paper presents a new method for dynamic texture
recognition based on spatiotemporal Gabor filters. Dynamic
textures have emerged as a new field of investigation
that extends the concept of self-similarity of texture image
to the spatiotemporal domain. To model a dynamic texture,
we convolve the sequence of images to a bank of spatiotemporal
Gabor filters. For each response, a feature vector
is built by calculating the energy statistic. As far as the
authors know, this paper is the first to report an effective
method for dynamic texture recognition using spatiotemporal
Gabor filters. We evaluate the proposed method on two
challenging databases and the experimental results indicate
that the proposed method is a robust approach for dynamic
texture recognition.
Introduction
The vision of animals provides a large amount of information
that improves the perception of the world. This information
is processed into different dimensions, including
color, shape, illumination, and motion. While most of the
features provide information about the static world, the motion
provides essential information for interaction with external
environment. In recent decades, the perception and
interpretation of motion have attracted a significant interest
in computer vision community [14, 16, 1, 9] motivated
by the importance in both scientific and industrial communities.
Despite significant advances, the motion characterization
is still an open problem.
Spatiotemporal Gabor Filters
Gabor filters are based on the important finding made
by Hubel and Wiesel in the beginning of the 1960s. They
found that the neurons of the primary visual cortex respond
to lines or edges of a certain orientation in different positions
of the visual field. Following this discovery, computational
models were proposed for modeling the function of
this neurons and the Gabor functions proved to be suited for
this purpose in many works.
Response Analysis of Spatiotemporal Gabor
Filters
Here, we analyze the speed and direction properties of
the spatiotemporal Gabor filters in synthetic sequence of
images. In Figure 3, we present the response of spatiotemporal
Gabor filters to bars moving at the same speed but in
different direction . The filters and the moving bars have
preference for the same speed v = 1. The response has the
highest magnitude when the direction of the filter matches
the direction of the moving bar. For instance, when = 0, a
vertical bar moving rightwards evokes higher response than
bars with other direction of movement.