• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.502-507
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• 2009

We present a real-time photomosaics method for small image set optimized by feature selection method. Photomosaics is an image that is divided into cells (usually rectangular grids), each of which is replaced with another image of appropriate color, shape and texture pattern. This method needs large set of tile images which have various types of image pattern. But large amount of photo images requires high cost for pattern searching and large space for saving the images. These requirements can cause problems in the application to a real-time domain or mobile devices with limited resources. Our approach is a genetic feature selection method for building an optimized image set to accelerate pattern searching speed and minimize the memory cost.

• Journal of Korea Game Society
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• v.11 no.1
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• pp.139-146
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• 2011

We propose a photomosaic method using PCA(Principal Component Analysis), which uses PCA results to find the most similar candidate fast and correctly. When two images are projected onto a certain principal component, if their coefficients are similar, they are also likely to be similar. Thus our photomosaic method using PCA can take care of both colors and shapes of images. Our method using coefficient comparison is faster than the one using all color comparison and more correct than the one using average comparison. Our hardware accelerated photomosaic algorithm can handle video images in real-time.