International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Coupling feature selection and machine learning methods for navigational query identification
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Interpretation of complex scenes using dynamic tree-structure Bayesian networks
Computer Vision and Image Understanding
On the computational rationale for generative models
Computer Vision and Image Understanding
Classification of biomedical high-resolution micro-CT images for direct volume rendering
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
A stochastic grammar of images
Foundations and Trends® in Computer Graphics and Vision
Unsupervised segmentation of natural images via lossy data compression
Computer Vision and Image Understanding
An eye localization, tracking and blink pattern recognition system: Algorithm and evaluation
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A Learning-Based Framework for Low Bit-Rate Image and Video Coding
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Statistical modeling and conceptualization of natural images
Pattern Recognition
Marked point process for vascular tree extraction on angiogram
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Image segmentation in a kernel-induced space
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
IEEE Transactions on Image Processing
Effective level set image segmentation with a kernel induced data term
IEEE Transactions on Image Processing
Document processing with Bayesian network and agent-based programming
SSIP'05 Proceedings of the 5th WSEAS international conference on Signal, speech and image processing
Dynamic background discrimination with a recurrent network
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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Natural images contain an overwhelming number of visual patterns generated by diverse stochastic processes. Defining and modeling these patterns is of fundamental importance for generic vision tasks, such as perceptual organization, segmentation, and recognition. The objective of this epistemological paper is to summarize various threads of research in the literature and to pursue a unified framework for conceptualization, modeling, learning, and computing visual patterns. This paper starts with reviewing four research streams: 1) the study of image statistics, 2) the analysis of image components, 3) the grouping of image elements, and 4) the modeling of visual patterns. The models from these research streams are then divided into four categories according to their semantic structures: 1) descriptive models, i.e., Markov random fields (MRF) or Gibbs, 2) variants of descriptive models (causal MRF and "pseudodescriptive" models), 3) generative models, and 4) discriminative models. The objectives, principles, theories, and typical models are reviewed in each category and the relationships between the four types of models are studied. Two central themes emerge from the relationship studies. 1) In representation, the integration of descriptive and generative models is the future direction for statistical modeling and should lead to richer and more advanced classes of vision models. 2) To make visual models computationally tractable, discriminative models are used as computational heuristics for inferring generative models. Thus, the roles of four types of models are clarified.