Image captured by two-dimensional camera contains no depth information. However in many applications we need depth information, for example such as in satellite imaging, robotic vision and target tracking. Stereo matc...
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ISBN:
(纸本)9781479968923
Image captured by two-dimensional camera contains no depth information. However in many applications we need depth information, for example such as in satellite imaging, robotic vision and target tracking. Stereo matching is used to extract depth information from images. The main aim of our project is to use stereo matching algorithms to plot the disparity map of segmented images which gives the depth information. Particle Swarm Optimization (PSO) algorithms are used for image segmentation. Our objective is to implement stereo matching algorithms on the segmented images and perform subjective analysis of reconstructed 3-D images. For some applications, such as image recognition or stereo vision, whole images cannot be processed, as it not only increases the computational complexity, but it also requires more memory. Thus, segmentation-based stereo matching algorithm should be used. This paper presents two novel methods for segmentation of images based on the Particle Swarm Optimization (PSO) and Darwinian Particle Swarm Optimization (DPSO).
The early diagnosis of some diseases is one of the most important reasons for success of treatment and ensuring healing. In this paper, we present blood vessel segmentation approach for extracting the vasculature on r...
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ISBN:
(纸本)9781509002764
The early diagnosis of some diseases is one of the most important reasons for success of treatment and ensuring healing. In this paper, we present blood vessel segmentation approach for extracting the vasculature on retinal fundus images. Novel segmentation approach of retinal images is used based on the Particle Swarm Optimization (PSO) in order to determine the n - 1 optimal n-level thresholds on retinal fundus images. The proposed approach is tested on the DRIVE datasets and its efficiency is compared with alternative methods. The obtained results show that the proposed approach is effective and achieves average accuracy of 97.75% and best accuracy of 98.857%.
In this work, a novel method for segmentation of Remote Sensing (RS) images based on the Darwinian Particle Swarm Optimization (DPSO) for determining the n-1 optimal n-level threshold on a given image is proposed. The...
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ISBN:
(纸本)9781467311601
In this work, a novel method for segmentation of Remote Sensing (RS) images based on the Darwinian Particle Swarm Optimization (DPSO) for determining the n-1 optimal n-level threshold on a given image is proposed. The efficiency of the proposed method is compared with the Particle Swarm Optimization (PSO) based segmentation method. Results show that DPSO-based image segmentation performs better than PSO-based method in a number of different measures.
A new spectral-spatial method for the classification of hyperspectral images is introduced. The proposed approach is based on two segmentation methods, Fractional-Order Darwinian Particle Swarm Optimization and Mean S...
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ISBN:
(纸本)9781479911127
A new spectral-spatial method for the classification of hyperspectral images is introduced. The proposed approach is based on two segmentation methods, Fractional-Order Darwinian Particle Swarm Optimization and Mean Shift segmentation and one clustering method, K-means. In parallel, the input data set is classified by Support Vector Machines (SVM). Furthermore, the result of the segmentation and clustering steps are combined with the result of SVM through majority voting within each object. The final classification map is made by using majority voting between three produced classification maps. Experimental results indicate that the proposed method can significantly improve SVM and other studied methods in terms of accuracies.
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