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We describe a learning-based method for low-level vision problems—estimating scenes from images. We generate a synthetic world of scenes and their corresponding rendered images, modeling their relationships with a Markov network. Bayesian belief propagation allows us to efficiently find a local maximum of the posterior probability for the scene, given an(More)
The Problem: Pixel representations for images do not have resolution independence. When we zoom into a bitmapped image, we get a blurred image. Figure 1 shows the problem for a teapot image, rich with real-world detail. We know the teapot's features should remain sharp as we zoom in on them, yet standard pixel interpolation methods, such as pixel(More)
Image-based models for computer graphics lack resolution independence: they cannot be zoomed much beyond the pixel resolution they were sampled at without a degradation of quality. Interpolating images usually results in a blurring of edges and image details. We describe image interpolation algorithms which use a database of training images to create(More)
The Hyperscore graphical computer-assisted composition system for users with limited or no musical training takes freehand drawing as input, letting users literally sketch their pieces. Designing an intelligent, intuitive system that enables novices-particularly children-to compose music is a difficult task. We can view the problem as a spectrum of tasks(More)
We address the super-resolution problem: how to estimate missing high spatial frequency components of a static image. From a training set of full-and low-resolution images, we build a database of patches of corrsponding high-and low-frequency image information. Given a new low-resolution image to enhance, we select from the training data a set of 10(More)
We present an example-based system for translating line drawings into different styles. The system is given a training set of many different lines, each drawn by an artist in various styles, which is used to translate new lines made by a user into a particular desired style with a it K-nearest neighbor algorithm. This algorithm fits each input line as a(More)