Perceptual organization and the representation of natural form
Artificial Intelligence
Perceptual Organization and Curve Partitioning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
Using Perceptual Organization to Extract 3D Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision, Graphics, and Image Processing
Introduction to algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Descriptions of complex objects from incomplete and imperfect data
Proceedings of a workshop on Image understanding workshop
A Parallel Technique for Signal-Level Perceptual Organization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Contour Decomposition Using a Constant Curvature Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal infinite impulse response zero crossing based edge detectors
CVGIP: Image Understanding
Human and machine vision: computing perceptual organisation
Human and machine vision: computing perceptual organisation
Perceptual Organization for Scene Segmentation and Description
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
A perceptually-supported sketch editor
UIST '94 Proceedings of the 7th annual ACM symposium on User interface software and technology
Robust and Efficient Detection of Salient Convex Groups
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation based on oscillatory correlation
Neural Computation
A Framework for Performance Characterization of Intermediate-Level Grouping Modules
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating Vision Modules: Stereo, Shading, Grouping, and Line Labeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual organization based computational model for robust segmentation of moving objects
Computer Vision and Image Understanding
A Variational Approach to Recovering a Manifold from Sample Points
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
2-D Shape Decomposition into Overlapping Parts
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Application of the Tensor Voting Technique for Perceptual Grouping to Grey-Level Images
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Segmentation of Spontaneously Splitting Figures into Overlapping Layers
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
Design Considerations for Generic Grouping in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Perceptually Closed Paths in Sketches and Drawings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Some General Grouping Principles: Line Perception from Points as an Example
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Combining local belief from low-level primitives for perceptual grouping
Pattern Recognition
On considering uncertainty and alternatives in low-level vision
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
| Hi-index | 0.14 |
The formalism of Bayesian networks provides a very elegant solution, in a probabilistic framework, to the problem of integrating top-down and bottom-up visual processes, as well serving as a knowledge base. The formalism is modified to handle spatial data, and thus the application of Bayesian networks is extended to visual processing. The modified form is called the perceptual inference network (PIN). The theoretical background of a PIN is presented, and its viability is demonstrated in the context of perceptual organization. Perceptual organization imparts robustness, efficiency, and a qualitative and holistic nature to vision. Thus far, the approaches to the problem of perceptual organization have been purely bottom up, without much top-down knowledge-base influence, and are therefore entirely dependent on the inputs, which are obviously imperfect. The knowledge base, besides coping with such input imperfection, also makes it possible to integrate multiple organizations and form a composite organization hypothesis. The PIN imparts an active inferential and integrating nature to perceptual organization in an elegant probabilistic framework.