Image Processing Thesis

Image Processing Thesis-83
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.We are especially interested in evaluating how these features compare against handcrafted features.

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.We are especially interested in evaluating how these features compare against handcrafted features.

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I'm happy to say that I've finally put my thesis online and updated my Publications page.

I should have done that earlier but it slipped my mind, so there it is!

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.

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.We then present a new style of line art illustration for smooth 3D surfaces.This style is designed to clearly convey surface shape, even for surfaces without predefined material properties or hatching directions.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.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 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.Therefore, one objective of this thesis is also to compare the CRBM approach with the CAE approach.The scope of this work is defined by several machine learning tasks.

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