IMDSP-6.1

An Algorithm for Centroid-Based Tracking of Moving Objects
Jacinto A Nascimento (IST), Arnaldo J Abrantes (ISEL), Jorge S Marques (IST/ISR)

This article addresses the problem of tracking moving objects using deformable models. A Kalman-based algorithm is presented, inspired on a new class of constrained clustering methods, recently proposed by Abrantes and Marques in the context of static shape estimation. A set of data centroids is tracked using intra-frame and inter-frame recursions. Centroids are computed as a weighted sum of the edge points belonging to the object boundary. The use of centroids introduces competitive learning mechanisms in the tracking algorithm leading to improved robustness with respect to occlusion and contour sliding. Experimental results with traffic sequences are provided.

IMDSP-6.2

AN INTELLIGENT AND ATTRACTABLE ACTIVE CONTOUR MODEL FOR BOUNDARY EXTRACTION
Lilian Ji, Hong Yan (The University of Sydney)

An intelligent and attractable active contour model for boundary extraction is presented in this paper. The proposed model is capable of driving any initial guess in the area of the evolving estimate towards the desired boundary, working against a constant image background, overcoming spurious edge-points and fitting into the object without any overrun. It is also capable of extracting both concave and convex boundaries while still being capable of bearing subjective boundaries with help of a synthetic convergent criterion and an adaptable interpolation scheme. Using additional two control parameters, it is possible to control the convergent properties of the new model, which provides a high degree of flexibility and adaptability. This robust model has been applied to real images with encouraging results.

IMDSP-6.3

Image Classification by a Two Dimensional Hidden Markov Model
Jia Li, Amir Najmi, Robert M. Gray (Information Systems Laboratory, EE Dept., Stanford University)

Traditional block-based image classification algorithms, such as CART and VQ based classification, ignore the statistical dependency among image blocks. Consequently, these algorithms often suffer from over-localization. In order to benefit from the inter-block dependency, an image classification algorithm based on a hidden Markov model (HMM) is developed. An HMM for image classification, a two dimensional extension from the one dimensional HMM used for speech recognition, has transition probabilities conditioned on the states of neighboring blocks from both directions. Thus, the dependency in two dimensions can be reflected simultaneously. The HMM parameters are estimated by the EM algorithm. A two dimensional version of the Viterbi algorithm is also developed to classify optimally an image based on the trained HMM. An application of the HMM algorithm to document image and aerial image segmentation shows that the algorithm performs better than CART.

IMDSP-6.4

Tracking of Moving Objects with Multiple Models Using Gaussian Mixtures
Jorge S Marques (ISR/IST), Jo�o M Lemos (IST/INESC)

This paper addresses the problem of tracking of objects with complex shape or motion dynamics. The approach followed relies on multiple models based on Gaussian mixtures and hidden Markov models. A tracking algorithm derived from Nonlinear Filtering is presented and illustrated in two situations. In the first, two points moving independently along a line are tracked, only one being observed at each time. In the second, two dimensional objects are tracked, under severe shape deformations. Unlike other multi-model approaches, the proposed method relies on parametric techniques providing efficient tools to update the shape and motion estimates.

IMDSP-6.5

A Constraint to Improve the Reliability of Stereo Matching Using the Rank Transform
Jasmine E Banks (Space Centre for Satellite Navigation, Queensland University of Technology / Cooperative Research Centre for Mining Technology and Equipment), Mohammed Bennamoun, Kurt Kubik (Space Centre for Satellite Navigation, Queensland University of Technology), Peter Corke (CSIRO Manufacturing Science and Technology / Cooperative Research Centre for Mining Technology and Equipment)

The rank transform is a non-parametric technique which has been recently proposed for the stereo matching problem. The motivation behind its application to the matching problem is its invariance to certain types of image distortion and noise, as well as its amenability to real-time implementation. This paper derives an analytic expression for the process of matching using the rank transform, and then goes on to derive one constraint which must be satisfied for a correct match. This has been dubbed the rank order constraint or simply the rank constraint. Experimental work has shown that this constraint is capable of resolving ambiguous matches, thereby improving matching reliability. This constraint was incorporated into a new algorithm for matching using the rank transform. This modified algorithm resulted in an increased proportion of correct matches, for all test imagery used.

IMDSP-6.6

REAL-TIME OBJECT RECOGNITION BASED ON ACTIVE VISION AND SEQUENTIAL ANALYSIS
Valerij Ortmann, Rolf Eckmiller (Department of Computer Science VI, Neuroinformatik, University of Bonn)

Image processing system for the real-time object detection and recognition was designed on the principles of Active Vision and Sequential Analysis. The real-world visual tasks can be solved due to predictive control of the vision sensor. The Sequential Analysis allows real-time implementation of the system on the low cost DSP hardware. The system was implemented on the DSP TMS320C50 and requires 18-30 ms for the detection and recognition of the object.

IMDSP-6.7

Auxiliary Functions and Optimal Scanning for Road Detection by Dynamic Programming
Nicolas Merlet (The Hebrew University of Jerusalem, Israel), Josiane Zerubia (INRIA Sophia Antipolis, France)

Shape information is useful for road detection to improve the correctness and smoothness of the results. Within the frame of dynamic programming, the proposed method stores in an auxiliary image the global direction V(M) followed in the current shortest path. The potential is a function of this image, so that pixels prolongating the current shortest path are favored. The auxiliary image is updated recursively at the same time as the energy, during the optimization. A variant of this method stores in the auxiliary image the center of the circle tangent to the current shortest path. Another application presented herein computes the average of the potential instead of its sum. The optimality principle is not verified anymore with the auxiliary functions but they give smoother results without increasing the complexity. Furthermore, several improvements w.r.t. the scanning allow gains of up to 50 % for the computational time.

IMDSP-6.8

Feature Matching and Target Recognition in Synthetic Aperture Radar Imagery
Reuven Meth, Rama Chellappa (University of Maryland at College Park)

An approach for target matching in Synthetic aperture radar (SAR) imagery is presented. The method is feature based where feature points in a target candidate are matched against those from an exemplar database. Matching is formulated as a non-linear optimization problem that encourages matches while minimizing the distance between the matched features. The formulation allows for missing, spurious and shifted feature points. A non-linear function is used to convexify the search space to enhance the search for the minimum objective cost. Extensions are presented for the use of two different feature types in the matching. Registration of the images is computed during the matching process in an iterative manner. Matching results are presented for simulated XPATCH and real MSTAR SAR target imagery.

IMDSP-6.9

Dynamic Object Identification and Verification Using Video
Baoxin Li, Rama Chellappa, Qinfen Zheng (University of Maryland at College Park), Sandor Der (Army Research Lab)

In the paper, we introduce the concepts of dynamic object identification and verification using video. A generalized Hausdorff metric, which is more robust to noise and allows a confidence interpretation, is suggested for the identification and verification problem. Parameters from sensor motion compensation procedure are incorporated into the search step such that the Hausdorff metric based matching can be achieved efficiently under more complex transformation groups. An algorithm is proposed for identification/verification based on edge map matching using the generalized Hausdorff metric. Experiments on infrared video sequences are provided.

IMDSP-6.10

PHYSICS-BASED CLASSIFICATION OF TARGETS IN SAR IMAGERY USING SUBAPERTURE SEQUENCES
Lawrence Carin, Gary Ybarra, Priya Bharadwaj, Paul Runkle (Department of Electrical and Computer Engineering, Duke University)

It is well known that radar scattering from an illuminated object is often highly aspect dependent. We have developed a multi-aspect target classification technique for SAR imagery that incorporates matching-pursuits feature extraction from each of a sequence of subaperture images, in conjunction with a hidden Markov model that explicitly incorporates the target-sensor motion represented by the image sequence. This approach exploits the aspect dependence of the signal features to facilitate maximum-likelihood identification. We consider SAR imagery containing targets concealed by foliage.

< IMDSP-5 IMDSP-7 >

Last Update: February 4, 1999 Ingo Höntsch