Image Processing Thesis

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Media Research Laboratory Department of Computer Science Courant Institute of Mathematical Sciences New York University We describe new algorithms and tools for generating paintings, illustrations, and animation on a computer.

These algorithms are designed to produce visually appealing and expressive images that look hand-painted or hand-drawn.

Since images are defined over two dimensions (perhaps more) digital image processing may be modeled in the form of Multidimensional Systems.

If you are interested, send an email to Fred Hamprecht.

scanned from a hand-painted source), we can process new images with some approximation to the style of the painting.

In contrast to the first two approaches, this allows us to design styles without requiring an explicit technical definition of the style.As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal distortion during processing.The system allows as fine user control as desired: the user may interactively change the painting style, specify variations of style over an image, and/or add specific strokes to the painting.Finally, we describe a new framework for processing images by example, called ``image analogies.'' Given an example of a painting or drawing (e.g.By adjusting algorithm parameters, a variety of styles can be generated, such as styles inspired by the Impressionists and the Expressionists.This method is then extended to processing video, as demonstrated by painterly animations and an interactive installation.These models relaunched the Deep Learning interest of the last decade.During the time of this thesis, the auto-encoders approach, especially Convolutional Auto-Encoders (CAE) have been used more and more.Indeed, a complete machine learning framework was developed during this thesis to explore possible optimizations and possible algorithms in order to train the tested models as fast as possible.If you are interested, you can: I hope this will interest a few of you!


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