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In hyperspectral images, the creation of ground-truth data for supervised learning methods is costly in terms of computation cost and time. In addition, the number of labeled data and the quality of labeled training data affects the success of the classification. as a solution to this problem, a graph-based semi-supervised hyperspectral image classifier is proposed in this study. The system was developed...
In the classification of hyperspectral images with supervised methods, acquisition of ground-truth information for a hyperspectral image is a challenging process in terms of time and cost. Besides, amount of the labeled data also affects the performance of classifiers. In this study, as a solution to this problem, a hyperspectral image classifier is proposed with semi-supervised learning, support...
In the classification of hyperspectral images with supervised methods, the generation of ground-truth information for a hyperspectral image is a challenging process in terms of time and cost. Besides, amount of the labeled data affects the classifier performance. In this study, as a solution of this problem a hyperspectral image classifier is proposed with semi-supervised learning, support vector...
In this study, two different Ottoman and Turkish handwritten recognition systems have been developed using Hidden Markov Model (HMM) and Recurrent Neural Network (RNN). The systems are tested in both public use datasets and Civil Registration and Nationality (CRN) dataset. As public use datasets, IFN/ENIT dataset which is created for Arabic language, is used because of the similarity between Ottoman...
Classification of hyperspectral data is computationally complex and time consuming process due to the dimensionality of spectral signatures and high volume of data. In this study as a solution to this problem, an improved principal component analysis technique is proposed to extract features called as two directional-two dimensional principal component analysis (2D2PCA). For using 2D2PCA with hyperspectral...
In this work, a new human based video summarization system designed and implemented which receives video frames from surveillance cameras‥ For analyzing the human centric videos, optical flow used as motion descriptor and histogram of gradients used as feature extractor. During the video motion information of object has been made continuous by adding time information to histograms with gradient information...
Video summarization, which has a tremendous usage area that spreads from information retrieval to data compression, plays a crucial role in the multimedia understanding. In recent years, with the explosion of the number of videos and their area of use, video summarization became a must to signify. Therefore, this work introduces a novel approach for the summarization problem which is based on human...
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