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|Title:||Adaptive bandwidth mode detection algorithm||Authors:||Dagher, Issam
|Affiliations:||Department of Computer Engineering||Issue Date:||2011||Part of:||Journal of IET image processing||Volume:||5||Issue:||8||Start page:||645||End page:||702||Abstract:||
In this study a new algorithm 'adaptive bandwidth mode detection (ABMD) algorithm has been developed to recover the correct density function without the need to either specify the correct number of Gaussians in the model or the correct bandwidth. The ABMD is employed in modelling visual features in applications such as image segmentation and real-time visual tracking. A simple type of model for these visual features are the Gaussian mixtures, where the number of Gaussian components is variable, thus, making it a flexible method for multimodal representation. This algorithm is used at initialisation for target modelling, where the target update will be done based on the mode propagation with adaptive bandwidth tracker method. It is based on an optimisation technique where a gradient ascent method is used and the optimal solution is selected based on a log-likelihood function. The mode detection ability of ABMD algorithm is compared with both the expectation maximisation and mean-shift algorithms. Furthermore, different video sequences have been employed to show how this approach has the ability to track an object regardless of whether the target model is corrupted with unwanted data at new frames.
|URI:||https://scholarhub.balamand.edu.lb/handle/uob/1590||Ezproxy URL:||Link to full text||Type:||Journal Article|
|Appears in Collections:||Department of Computer Engineering|
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