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This project focuses on training a computer to seamlessly integrate images of Barack Obama in front of a pyramid. By leveraging existing datasets and segmentation algorithms, we aim to extract relevant features, discern the best background for placement, and achieve realistic positioning. Utilizing multiple segmentation techniques and a thorough evaluation of success criteria, we aim to minimize artifacts and ensure a believable composition. This work builds upon established research in image manipulation and object recognition, paving the way for improved computer-generated imagery.
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Let Computer Draw Qingyuan Kong
Goal • Give me a picture “Obama stands in front of a pyramid”
Goal Here you are!
Analogy knowledge Database/internet What does obama look like? Search obama Search pyramid What does pyramid look like?
Approach • let computer learn to extract “Obama” out • Training an extractor with Labelme • Use segmentation algorithm just on the obtained data set Reference: [1] Tomasz Malisiewicz and Alexei A. Efros. Improving spatial support for ob jects via multiple segmentations. BMVC, 2007. [2] Bryan C. Russell, Alexei A. Efros, Josef Sivic, William T. Freeman, and Andrew Zis- serman. Using multiple segmentations to discover ob jects and their extent in image collections. CVPR, 2006.
Approach • Find the best background to place “obama”
Approach • Find the best position to place “Obama” • Compare the marginal pixels with the background • Adjust Reference: • James Hays and Alexei A. Efros. Scene completion using millions of photographs. SIGGRAPH, 2007. • Ce Liu, Jenny Yuen, Antonio Torralba, Josef Sivic, and William T. Freeman. Sift flow: dense correspondence across difference scenes. ECCV, 2004.
Data sets • Labelme • google image • bing image
Evaluation of Success • make the computer put some classes of objects, such like people, cars, into a background, the compound of which looks real and with few artifacts.
milestone • make the computer be able to draw at least one class of objects in at least one class of background.