A multilevel GMRF-based approach to image segmentation and restoration
Signal Processing
Fractal image compression: theory and application
Fractal image compression: theory and application
Object Matching Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vehicle Segmentation and Classification Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Deformable Template Approach to Detecting Straight Edges in Radar Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACCV '95 Invited Session Papers from the Second Asian Conference on Computer Vision: Recent Developments in Computer Vision
Unsupervised Markovian Segmentation Of Sonar Images
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Nonparametric Multiscale Energy-Based Model and Its Application in Some Imagery Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Correntropy based matched filtering for classification in sidescan sonar imagery
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Multiple genetic snakes for bone segmentation
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
High-resolution sonars: what resolution do we need for target recognition?
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
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We present an original statistical classification method using a deformable template model to separate natural objects from man-made objects in an image provided by a high resolution sonar. A prior knowledge of the manufactured object shadow shape is captured by a prototype template, along with a set of admissible linear transformations, to take into account the shape variability. Then, the classification problem is defined as a two-step process. First, the detection problem of a region of interest in the input image is stated as the minimization of a cost function. Second, the value of this function at convergence allows one to determine whether the desired object is present or not in the sonar image. The energy minimization problem is tackled using relaxation techniques. In this context, we compare the results obtained with a deterministic relaxation technique (a gradient-based algorithm) and two stochastic relaxation methods: Simulated Annealing (SA) and a hybrid Genetic Algorithm (GA). This latter method has been successfully tested on real and synthetic sonar images, yielding very promising results.