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National Repository of Grey Literature 2 records found  Search took 0.01 seconds. 
Image hashing using compressed sensing
 Kopec, Peter ; Číka, Petr (referee) ; Rajmic, Pavel (advisor)
This thesis is devoted to the analysis and implementation of image hashing based on the article "Robust image hashing with compressed sensing and ordinal measures"[3]. Image hashing uses so-called perceptual hashing methods. These methods have great applications in computer vision science, and the properties of these methods allow us to compare the similarity of hashed images and classify these images into groups. We can use this comparison, for example, to search images on the Internet for various reasons. In the theoretical part, we will talk more about the properties of these hashing methods and describe the hashing method according to the mentioned paper, we will focus most on what is compressive sampling, saliency map and how we achieve it. In the practical part, we will prepare a test dataset using Python scripting language and implement the hashing method according to the mentioned article. Then we test this hashing method on this dataset and finally compare it with another hashing method.
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Image comparison using eye movement simulation
 Veľk, Miroslav ; Bálek, Martin (advisor) ; Bílý, Tomáš (referee)
In the present work we study the biologically plausible and psychologically motivated model of human visual attention and explain the importance of similar models. We propose and implement methods to find salient locations in the image. We give detailed instructions on creating saliency maps, which contain information about saliency of every location in the explored scene. Using this maps we simulate shifts of visual attention (eye movement). A simulated scanpath representing this shifts is created and then analyzed. We especially focus on comparison of different scanpaths by different features. Finally practical use of our model is outlined.
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