matlab code for object detection using pso
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[b]matlab code for object detection using pso[/b]
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Image matching is a key component in almost any image analysis process. Image matching is crucial for a wide range of applications, such as navigation, orientation, automatic surveillance, robot vision and science assignment. Any automated system for placement of three-dimensional points must include a powerful procedure for image matching. Most biological vision systems have the talent to cope with the changing world. Computer vision systems have been developed in the same way. For a computer vision system, the ability to cope with moving and changing objects, changing lighting, and changing viewpoints is essential for many tasks. Object detection is required for surveillance applications, for autonomous vehicle orientation, for efficient video compression, for intelligent tracking of moving objects, for automatic target recognition (ATR) systems and for many other applications . Cross-correlation and related techniques have dominated the field since the early 1950s. 

The conventional template matching algorithm based on cross-correlation requires a complex calculation and a large time for object detection, making it difficult to use in real-time applications. The shortcomings of this class of image matching methods have caused a slowdown in the development of automated operational correlation systems. In the proposed work on particle swarm optimization and its algorithm based on variants is used for the detection of objects in the image. The implementation of this algorithm reduces the time required for object detection than the conventional template match algorithm. Algorithm can detect the object in less number of iteration and therefore less time and energy than the complexity of the matching of conventional templates. This feature makes the method to be able to implement in real time. In this thesis a study of the algorithm of optimization of Swarm of particles is made and then formulates the formulation of the algorithm for the detection of objects using PSO and its variants to validate their effectiveness.
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