Abstract book (pdf) - ICPR 2010

13:30-13:50, Paper TuBT2.1
Local Rotation Invariant Patch Descriptors for 3D Vector Fields
Janis, Fehr, Univ. Freiburg
In this paper, we present two novel methods for the fast computation of local rotation invariant patch descriptors for 3D
vectorial data. Patch based algorithms have recently become very popular approach for a wide range of 2D computer
vision problems. Our local rotation invariant patch descriptors allow an extension of these methods to 3D vector fields.
Our approaches are based on a harmonic representation for local spherical 3D vector field patches, which enables us to
derive fast algorithms for the computation of rotation invariant power spectrum and bispectrum feature descriptors of such
patches.
13:50-14:10, Paper TuBT2.2
Anomaly Detection for Longwave Flir Imagery using Kernel wavelet-Rx
Mehmood, Asif, US Army Res. Lab.
Nasrabadi, Nasser, US Army Res. Lab.
This paper describes a new kernel wavelet-based anomaly detection technique for long-wave (LW) Forward Looking Infrared
(FLIR) imagery. The proposed approach called kernel wavelet-RX algorithm is essentially an extension of the
wavelet-RX algorithm (combination of wavelet transform and RX anomaly detector) to a high dimensional feature space
(possibly infinite) via a certain nonlinear mapping function of the input data. The wavelet-RX algorithm in this high dimensional
feature space can easily be implemented in terms of kernels that implicitly compute dot products in the feature
space (kernelizing the wavelet-RX algorithm). In our kernel wavelet-RX algorithm, a 2-D wavelet transform is first applied
to decompose the input image into uniform subbands. A number of significant subbands (high energy subbands) are concatenated
together to form a subband-image cube. The kernel RX algorithm is then applied to these subband-image cubes
obtained from wavelet decomposition of the LW database images. Experimental results are presented for the proposed
kernel wavelet-RX, wavelet-RX and the classical CFAR algorithm for detecting anomalies (targets) in a large database of
LW imagery. The ROC plots show that the proposed kernel wavelet-RX algorithm outperforms the wavelet-RX as well
as the classical CFAR detector.
14:10-14:30, Paper TuBT2.3
Detection of Salient Image Points using Principal Subspace Manifold Structure
Paiva, Antonio, Univ. of Utah
Tasdizen, Tolga, Univ. of Utah
This paper presents a method to find salient image points in images with regular patterns based on deviations from the
overall manifold structure. The two main contributions are that: (I) the features to extract salient point are derived directly
and in an unsupervised manner from image neighborhoods, and (ii) the manifold structure is utilized, thus avoiding the
assumption that data lies in clusters and the need to do density estimation. We illustrate the concept for the detection of
fingerprint minutiae, fabric defects, and interesting regions of seismic data.
14:30-14:50, Paper TuBT2.4
Triangle-Constraint for Finding More Good Features
Guo, Xiaojie, Tianjin Univ.
Cao, Xiaochun, Tianjin Univ.
We present a novel method for finding more good feature pairs between two sets of features. We first select matched features
by Bi-matching method as seed points, then organize these seed points by adopting the Delaunay triangulation algorithm.
Finally, we use Triangle-Constraint (T-C) to increase both number of correct matches and matching score (the ratio
between number of correct matches and total number of matches). The experimental evaluation shows that our method is
robust to most of geometric and photometric transformations including rotation, scale change, blur, viewpoint change,
JPEG compression and illumination change, and significantly improves both number of correct matches and matching
score.
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