Recovering Intrinsic Images from a Single Image

We present an algorithm that uses multiple cues to recover shading and reflectance intrinsic images from a single image. Using both color information and a classifier trained to recognize gray-scale patterns, each image derivative is classified as being caused by shading or a change in the surface's reflectance. Generalized Belief Propagation is then used to propagate information from areas where the correct classification is clear to areas where it is ambiguous. We also show results on real images

Playing with Puffball: Simple Scale-Invariant Inflation for Use in Vision and Graphics

We describe how inflation, the act of mapping a 2D silhouette to a 3D region, can be applied in two disparate problems to offer insight and improvement: silhouette part segmentation and image-based material transfer. To demonstrate this, we introduce Puffball, a novel inflation technique, which achieves similar results to existing inflation approaches -- including smoothness, robustness, and scale and shift-invariance -- through an exceedingly simple and accessible formulation. The part segmentation algorithm avoids many of the pitfalls of previous approaches by finding part boundaries on a canonical 3-D shape rather than in the contour of the 2-D shape; the algorithm gives reliable and intuitive boundaries, even in cases where traditional approaches based on the 2D Minima Rule are misled. To demonstrate its effectiveness, we present data in which subjects prefer Puffball's segmentations to more traditional Minima Rule-based segmentations across several categories of silhouettes. The texture transfer algorithm utilizes Puffball's estimated shape information to produce visually pleasing and realistically synthesized surface textures with no explicit knowledge of either underlying shape.National Eye Institute (Special Training Grant

Learning Gaussian Conditional Random Fields for Low-Level Vision

Markov Random Field (MRF) models are a popular tool for vision and image processing. Gaussian MRF models are particularly convenient to work with because they can be implemented using matrix and linear algebra routines. However, recent research has focused on on discrete-valued and non-convex MRF models because Gaussian models tend to over-smooth images and blur edges. In this paper, we show how to train a Gaussian Conditional Random Field (GCRF) model that overcomes this weakness and can outperform the non-convex Field of Experts model on the task of denoising images. A key advantage of the GCRF model is that the parameters of the model can be optimized efficiently on relatively large images. The competitive performance of the GCRF model and the ease of optimizing its parameters make the GCRF model an attractive option for vision and image processing applications. ©2007 IEEE

Joint Inference in Weakly-Annotated Image Datasets via Dense Correspondence

We present a principled framework for inferring pixel labels in weakly-annotated image datasets. Most previous, example-based approaches to computer vision rely on a large corpus of densely labeled images. However, for large, modern image datasets, such labels are expensive to obtain and are often unavailable. We establish a large-scale graphical model spanning all labeled and unlabeled images, then solve it to infer pixel labels jointly for all images in the dataset while enforcing consistent annotations over similar visual patterns. This model requires significantly less labeled data and assists in resolving ambiguities by propagating inferred annotations from images with stronger local visual evidences to images with weaker local evidences. We apply our proposed framework to two computer vision problems, namely image annotation with semantic segmentation, and object discovery and co-segmentation (segmenting multiple images containing a common object). Extensive numerical evaluations and comparisons show that our method consistently outperforms the state-of-the-art in automatic annotation and semantic labeling, while requiring significantly less labeled data. In contrast to previous co-segmentation techniques, our method manages to discover and segment objects well even in the presence of substantial amounts of noise images (images not containing the common object), as typical for datasets collected from Internet search

Fundamental Strategies For Solving Low-Level Vision Problems

Low-level vision encompasses a wide variety of problems and solutions. Solutions to low-level problems can be broadly group according to how they propagate local information to global representations. Understanding these categorizations is useful because they offer guidance on how tools like machine learning can be implemented in these systems. © 2011 Information Processing Society of Japan

Utilizing variational optimization to learn markov random fields, in: CVPR 2007

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Utilizing Variational Optimization To Learn Markov Random Fields

Markov Random Field, or MRF, models are a powerful tool for modeling images. While much progress has been made in algorithms for inference in MRFs, learning the parameters of an MRF is still a challenging problem. In this paper, we show how variational optimization can be used to learn the parameters of an MRF This method for learning, which we refer to as Variational Mode Learning, finds the MRF parameters by minimizing a loss function that penalizes the difference between ground-truth images and an approximate, variational solution to the MRF. In particular, we focus on learning parameters for the Field of Experts model of Roth and Black. In addition to demonstrating the effectiveness of this method, we show that a model based on derivative filters performs quite similarly to the Field of Experts model. This suggests that the Field of Experts model, which is difficult to interpret, can be understood as imposing piecewise continuity on the image. © 2007 IEEE

Utilizing Variational Optimization to Learn Markov Random Fields

Markov Random Field, or MRF, models are a powerful tool for modeling images. While much progress has been made in algorithms for inference in MRFs, learning the parameters of an MRF is still a challenging problem. In this paper, we show how variational optimization can be used to learn the parameters of an MRF This method for learning, which we refer to as Variational Mode Learning, finds the MRF parameters by minimizing a loss function that penalizes the difference between ground-truth images and an approximate, variational solution to the MRF. In particular, we focus on learning parameters for the Field of Experts model of Roth and Black. In addition to demonstrating the effectiveness of this method, we show that a model based on derivative filters performs quite similarly to the Field of Experts model. This suggests that the Field of Experts model, which is difficult to interpret, can be understood as imposing piecewise continuity on the image. © 2007 IEEE

Recovering Shape from a Single Image of a Mirrored Surface from Curvature Constraints

This paper presents models and algorithms for estimating the shape of a mirrored surface from a single image of that surface, rendered under an unknown, natural illumination. While the unconstrained nature of this problem seems to make shape recovery impossible, the curvature of the surface cause characteristic image patterns to appear. These image patterns can be used to estimate how the surface curves in different directions. We show how these estimates can be used to produce constraints that can be used to estimate the shape of the surface. This approach is demonstrated on simple surfaces rendered under both natural and synthetic illuminations