thesis
From uncertainty to adaptivity : multiscale edge detection and image segmentation
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Abstract
This thesis presents the research on two different tasks in computer vision: edge detection
and image segmentation (including texture segmentation and motion field segmentation).
The central issue of this thesis is the uncertainty of the joint space-frequency image
analysis, which motivates the design of the adaptive multiscale/multiresolution schemes
for edge detection and image segmentation. Edge detectors capture most of the local
features in an image, including the object boundaries and the details of surface textures.
Apart from these edge features, the region properties of surface textures and motion fields
are also important for segmenting an image into disjoint regions. The major theoretical
achievements of this thesis are twofold. First, a scale parameter for the local processing of
an image (e.g. edge detection) is proposed. The corresponding edge behaviour in the scale
space, referred to as Bounded Diffusion, is the basis of a multiscale edge detector where the
scale is adjusted adaptively according to the local noise level. Second, an adaptive multiresolution
clustering scheme is proposed for texture segmentation (referred to as Texture
Focusing) and motion field segmentation. In this scheme, the central regions of homogeneous
textures (motion fields) are analysed using coarse resolutions so as to achieve a
better estimation of the textural content (optical flow), and the border region of a texture
(motion field) is analysed using fine resolutions so as to achieve a better estimation of the
boundary between textures (moving objects). Both of the above two achievements are the
logical consequences of the uncertainty principle. Four algorithms, including a roof edge
detector, a multiscale step edge detector, a texture segmentation scheme and a motion
field segmentation scheme are proposed to address various aspects of edge detection and
image segmentation. These algorithms have been implemented and extensively evaluated