Abstract book (pdf) - ICPR 2010

In this paper, an anisotropic diffusion model with a generalized diffusion coefficient function is presented for defect detection
in low-contrast surface images and, especially, aims at material surfaces found in liquid crystal display (LCD)
manufacturing. A defect embedded in a low-contrast surface image is extremely difficult to detect because the intensity
difference between unevenly-illuminated background and defective regions are hardly observable. The proposed anisotropic
diffusion model provides a generalized diffusion mechanism that can flexibly change the curve of the diffusion coefficient
function. It adaptively carries out a smoothing process for faultless areas and performs a sharpening process for defect
areas in an image. An entropy criterion is proposed as the performance measure of the diffused image and then a stochastic
evolutionary computation algorithm, particle swarm optimization (PSO), is applied to automatically determine the best
parameter values of the generalized diffusion coefficient function. Experimental results have shown that the proposed
method can effectively and efficiently detect small defects in low-contrast surface images.
13:30-16:30, Paper ThBCT9.4
Impact of Vector Ordering Strategies on Morphological Unmixing of Remotely Sensed Hyperspectral Images
Plaza, Antonio, Univ. of Extremadura
Hyper spectral imaging is a new technique in remote sensing that generates hundreds of images, corresponding to different
wavelength channels, for the same area on the surface of the Earth. In previous work, we have explored the application of
morphological operations to integrate both spatial and spectral responses in hyper spectral data analysis. These operations
rely on ordering pixel vectors in spectral space, but there is no unambiguous means of defining the minimum and maximum
values between two vectors of more than one dimension. Our original contribution in this paper is to examine the impact
of different vector ordering strategies on the definition of multi-channel morphological operations. Our focus is on morphological
unmixing, which decomposes each pixel vector in the hyper spectral scene into a combination of pure spectral
signatures (called end members) and their associated abundance fractions, allowing sub-pixel characterization. Experiments
are conducted using real hyper spectral data sets collected by NASA/JPL’s Airborne Visible Infra-Red Imaging Spectrometer
(AVIRIS) system.
13:30-16:30, Paper ThBCT9.5
A Recursive and Model-Constrained Region Splitting Algorithm for Cell Clump Decomposition
Xiong, Wei, Inst. for Infocomm Res. A-STAR
Ong, Sim Heng, National Univ. of Singapore
Lim, Joo-Hwee, Inst. for Infocomm Res.
Decomposition of cells in clumps is a difficult segmentation task requiring region splitting techniques. Techniques that do
not employ prior shape constraints usually fail to achieve accurate segmentation. Those using shape constraints are unable
to cope with large clumps and occlusions. In this work, we propose a model-constrained region splitting algorithm for cell
clump decomposition. We build the cell model using joint probability distribution of invariant shape features. The shape
model, the contour smoothness and the gradient information along the cut are used to optimize the splitting in a recursive
manner. The short cut rule is also adopted as a strategy to speed up the process. The algorithm performs well in validation
experiments using 60 images with 4516 cells and 520 clumps.
13:30-16:30, Paper ThBCT9.6
Bounding-Box based Segmentation with Single Min-Cut using Distant Pixel Similarity
Pham, Viet-Quoc, The Univ. of Tokyo
Takahashi, Keita, The Univ. of Tokyo
Naemura, Takeshi, The Univ. of Tokyo
This paper addresses the problem of interactive image segmentation with a user-supplied object bounding box. The underlying
problem is the classification of pixels into foreground and background, where only background information is
provided with sample pixels. Many approaches treat appearance models as an unknown variable and optimize the segmentation
and appearance alternatively, in an expectation maximization manner. In this paper, we describe a novel approach
to this problem: the objective function is expressed purely in terms of the unknown segmentation and can be optimized
using only one minimum cut calculation. We aim to optimize the trade-off of making the foreground layer as large as possible
while keeping the similarity between the foreground and background layers as small as possible. This similarity is
formulated using the similarities of distant pixel pairs. We evaluated our algorithm on the GrabCut dataset and demonstrated
that high-quality segmentations were attained at a fast calculation speed.
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