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Abstract - IMDSP6 |
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IMDSP6.1 |
Steerable Filters and Invariant Recognition in Spacetime R. Lenz (Linkoping University, Sweden) The groups which have received most attention in signal processing research are the affine groups and the Heisenberg-Weyl group related to wavelets and time-frequency methods. In low-level image processing the rotation-groups SO(2) and SO(3) were studied in detail. In this paper we argue that the Lorentz group SO(1; 2) provides a natural framework in the study of dynamic processes like the analysis of image sequences. We summarize the connection between the group SO(1; 2) and the groups SU(1; 1) and SL(2; R)and give an overview over their representations. We show that their representation theory is in parts similar to the corresponding theory for the three-dimensional rotation group. The main differences between the compact groups (like SO(2) and SO(3)) is however that the Fourier transforms for these groups involves infinite-dimensional representations and that the finite-dimensional represenstations are no longer unitary. In the signal processing context this means that the filter vectors computed by finite-dimensional steerable filter systems no longer transform as unitary vector transformations under the symmetry operations in SO(1; 2). |
| IMDSP6.2 |
Statistical Model and Genetic Optimization: Application to Pattern Detection in Sonar Images M. Mignotte, C. Collet (Groupe de Traitement du Signal, France); P. Perez, P. Bouthemy (IRISA/INRIA, France) We present a new classification method using deformable template model to separate natural objects from man made objects in an image given by a high resolution sonar. A prior knowledge of the manufactured object shadow shape is described by a prototype template and a set of admissible linear transformations to take into account the shape variability. Then, the classification problem is defined as a two step process; firstly the detection problem of a region of interest in the input image is stated in a Bayesian framework and is posed as an equivalent energy minimization problem of an objective function: in this paper, this energy minimization problem is solved by using a hybrid Genetic Algorithm. Secondly, the value of this function at convergence allows to determine the presence of the desired object in the sonar image. This method has been successfully tested on real and synthetic sonar images, yielding promissing results. |
| IMDSP6.3 |
Target Aspect Estimation from Single and Multi-Pass SAR Images R. Meth, R. Chellappa (University of Maryland, USA); S. Kuttikkad (Etak Inc., USA) A technique is presented for estimating the aspect of targets in SAR imagery for use in indexing, feature extraction and recognition. Aspect estimation is enhanced by combining multiple images of the same target. In order to properly combine the estimation of multiple passes, it is necessary to accurately register the images to a common coordinate frame. An algorithm for registering multiple high resolution SAR images, is presented. A global affine transformation derived from the sensor acquisition parameters is used to automatically register the images, followed by a refinement to correct for translational errors. The registered SAR images are used for improving the estimates of target orientation angles, detecting the presence of occlusion and indicating poor target segmentation. |
| IMDSP6.4 |
An Edge Detection by Using Self-Organization H. Nagai, Y. Miyanaga, K. Tochinai (Hokkaido University, Japan) This paper proposes a self-organized edge detection. In this method, several clusters are yielded and self-organized according to a gray scale level and the location of pixels. In addition, the comparison among these clusters results in estimated edge. However, after self-organization, clusters are classified into some group according to their properties. In this report, the method which represents thedetail distribution of each cluster is introduced. In addition, by using this method, it is shown that the proposed detection of edges can improve the accuracy in some experiments. |
| IMDSP6.5 |
A New Multiresolution Algorithm for Image Segmentation M. Saeed (MIT, USA); W. Karl (Boston University, USA); T. Nguyen (University of Wisconsin, USA); H. Rabiee (Intel Corporation, USA) We present here a novel multiresolution-based image segmentation algorithm. The proposed method extends and improves the Gaussian mixture model (GMM) paradigm by incorporating a multiscale correlation model of pixel dependence into the standard approach. In particular, the standard GMM is modified by introducing a multiscale neighborhood clique that incorporates the correlation between pixels in space and scale. We modify the log likelihood function of the image field by a penalization term that is derived from a multiscale neighborhood clique. Maximum Likelihood (ML) estimation via the Expectation Maximization (EM) algorithm is used to estimate the parameters of the new model. Then, utilizing the parameter estimates, the image field is segmented with a MAP classifier. It is demonstrated that the proposed algorithm provides superior segmentations of synthetic images, yet is computationally efficient. |
| IMDSP6.6 |
Unsupervised Image Segmentation S. Barker, P. Rayner (Cambridge University, UK) We present an unsupervised segmentation algorithm comprising an annealing process to select the maximum a posteriori (MAP) realization of a Hierarchical Markov Random Field (MRF) Model. The algorithm consists of a sampling framework which unifies the processes of model selection, parameter estimation and image segmentation, in a single Markov Chain. To achieve this, Reversible Jumps are incorporated into the Markov Chain to allow movement between model spaces.By using partial decoupling to segment the MRF it is possible to generate jump proposals efficiently while providing a mechanism for the use of deterministic methods, such as Gabor filtering, to speed up convergence. |
| IMDSP6.7 |
Unsupervised Multidimensional Hierarchical Clustering R. Dugad, N. Ahuja (University of Illinois, Urbana-Champaign, USA) A method for multidimensional hierarchical clustering that is invariant to monotonic transformations of the distance metric is presented. The method derives a tree of clusters organized according to the homogeneity of intracluster and interpoint distances. Higher levels correspond to coarser clusters. At any level the method can detect clusters of different densities, shapes and sizes. The number of clusters and the parameters for clustering are determined automatically and adaptively for a given data set which makes it unsupervised and non-parametric. The method is simple, noniterative and requires low computation. Results on various sample data sets are presented. |
| IMDSP6.8 |
Fast and Robust Level-set Segmentation of Deformable Structures H. Yahia, J. Berroir, G. Mazars (INRIA, France) Level-sets provide powerful methods for the segmentation of deformable structures. They are able to handle protrusions and specific topological effects. In this work a particle system formulation of level-sets is introduced. It keeps all the advantages of the level-set approach for the segmentation of deformable structures, while it overcomes some of its drawbacks. In this approach the level-sets are controlled by particles, which is of particular interest for interactive control. The particle system records the internal energy of the level-set, while the external force field comes from image data. The energy minimization process is fast, stable and robust. The use of skeleton techniques provide a reliable intialization of the particles, and it is coherent with simple affine motion. The paper is illustrated by examples coming from real image sequences. |
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