A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vector quantization and signal compression
Vector quantization and signal compression
Pyramid-based texture analysis/synthesis
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Local Scale Control for Edge Detection and Blur Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Equivalence of Julesz Ensembles and FRAME Models
International Journal of Computer Vision - Special issue on Genomic Signal Processing
The Problem of Sparse Image Coding
Journal of Mathematical Imaging and Vision
Image Parsing: Unifying Segmentation, Detection, and Recognition
International Journal of Computer Vision
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
Minimax Entropy Principle and Its Application to Texture Modeling
Neural Computation
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Contour tracking based on marginalized likelihood ratios
Image and Vision Computing
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Guest Editorial: Generative model based vision
Computer Vision and Image Understanding
On the computational rationale for generative models
Computer Vision and Image Understanding
A Two-Level Generative Model for Cloth Representation and Shape from Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual Scale-Space and Its Applications
International Journal of Computer Vision
Shape Extraction through Region-Contour Stitching
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
From image parsing to painterly rendering
ACM Transactions on Graphics (TOG)
Learning explicit and implicit visual manifolds by information projection
Pattern Recognition Letters
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
NPAR '10 Proceedings of the 8th International Symposium on Non-Photorealistic Animation and Rendering
Hierarchical 3D perception from a single image
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A Numerical Study of the Bottom-Up and Top-Down Inference Processes in And-Or Graphs
International Journal of Computer Vision
Automatic image inpainting by heuristic texture and structure completion
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Learning a generative model of images by factoring appearance and shape
Neural Computation
Scale selection for supervised image segmentation
Image and Vision Computing
Painting by feature: texture boundaries for example-based image creation
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Accurate Junction Detection and Characterization in Natural Images
International Journal of Computer Vision
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This article proposes a generative image model, which is called ''primal sketch,'' following Marr's insight and terminology. This model combines two prominent classes of generative models, namely, sparse coding model and Markov random field model, for representing geometric structures and stochastic textures, respectively. Specifically, the image lattice is divided into structure domain and texture domain. The sparse coding model is used to represent image intensities on the structure domain, where edge and ridge segments are modeled by image coding functions with explicit geometric and photometric parameters. The edge and ridge segments form a sketch graph whose nodes are corners and junctions. The sketch graph is governed by a simple spatial prior model. The Markov random field model is used to summarize image intensities on the texture domain, where the texture patterns are characterized by feature statistics in the form of marginal histograms of responses from a set of linear filters. The Markov random fields in-paint the texture domain while interpolating the structure domain seamlessly. A sketch pursuit algorithm is proposed for model fitting. A number of experiments on real images are shown to demonstrate the model and the algorithm.