Image Processing Thesis

Image Processing Thesis-19
The image analogies framework supports many other novel image processing operations. Digital image processing is the use of computer algorithms to perform image processing on digital images.

The image analogies framework supports many other novel image processing operations. Digital image processing is the use of computer algorithms to perform image processing on digital images.

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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.

The first one, handwritten digit recognition, is analysed to see how much the unsupervised pretraining technique introduced with the Deep Belief Network (DBN) model improves the training of neural networks.

The second, detection and recognition of Sudoku in images, is evaluating the efficiency of DBN and Convolutional DBN (CDBN) models for classification of images of poor quality.

We begin with a painterly image processing algorithm inspired by painting with real physical media.

This method produces images with a much greater subjective impression of looking hand-made than do earlier methods.

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.

Finally, features are learned fully unsupervised from images for a keyword spotting task and are compared against well-known handcrafted features.

Moreover, the thesis was also oriented around a software engineering axis.

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