International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
A Generative Sketch Model for Human Hair Analysis and Synthesis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Two-Level Generative Model for Cloth Representation and Shape from Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering Surface Layout from an Image
International Journal of Computer Vision
Perceptual Scale-Space and Its Applications
International Journal of Computer Vision
An efficient garment visual search based on shape context
WSEAS Transactions on Computers
Hierarchical segmentation-based image coding using hybrid quad-binary trees
IEEE Transactions on Image Processing
Interactive rotoscoping: extracting and tracking object sketch
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Learning to segment images using region-based perceptual features
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Direct energy minimization for super-resolution on nonlinear manifolds
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Object categorization with sketch representation and generalized samples
Pattern Recognition
Geometrically Guided Exemplar-Based Inpainting
SIAM Journal on Imaging Sciences
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In this paper, we present a mathematical theory for Marr'sprimal sketch. We first conduct a theoretical study ofthe descriptive Markov random field model and the generativewavelet/sparse coding model from the perspectiveof entropy and complexity. The competition between thetwo types of models defines the concept of "sketchability",which divides image into texture and geometry. We then proposea primal sketch model that integrates the two modelsand, in addition, a Gestalt field model for spatial organization.We also propose a sketching pursuit process that coordinatesthe competition between two pursuit algorithms:the matching pursuit [8] and the filter pursuit [12], that seekto explain the image by bases and filters respectively. Themodel can be used to learn a dictionary of image primitives,or textons in Julesz's language, for natural images.The primal sketch model is not only parsimonious for imagerepresentation, but produces meaningful sketches overa large number of generic images.