The Computation of Visible-Surface Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Stereopsis for 3-D Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Error Detection, Correction, and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surfaces from Stereo: Integrating Feature Matching, Disparity Estimation, and Contour Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Correspondence Through Feature Grouping and Maximal Cliques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo by Incremental Matching of Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accurate surface description from binocular stereo
Proceedings of a workshop on Image understanding workshop
ACM Computing Surveys (CSUR)
From Images to Surfaces: A Computational Study of the Human Early Visual System
From Images to Surfaces: A Computational Study of the Human Early Visual System
Automated stereo perception
Computational framework for multi-primitive hierarchical stereo analysis
Computational framework for multi-primitive hierarchical stereo analysis
Intensity- and Gradient-Based Stereo Matching Using Hierarchical Gaussian Basis Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Intelligent and Robotic Systems
International Journal of Computer Vision
An Area-Based Stereo Matching Using Adaptive Search Range and Window Size
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
Mutual information based registration of multimodal stereo videos for person tracking
Computer Vision and Image Understanding
A coarse-and-fine Bayesian belief propagation for correspondence problems in computer vision
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
| Hi-index | 0.14 |
This paper develops and demonstrates a new computational framework for an accurate, robust, and efficient stereo approach. In multi-primitive hierarchical (MPH) computational model, stereo analysis is performed in multiple stages, incorporating multiple primitives, utilizing a hierarchical control strategy. The MPH stereo system consists of three integrated subsystems: region-based analysis module; linear edge segment-based analysis module; and edgel-based stereo analysis module. Results of stereo analysis at higher levels of the hierarchy are used for guidance at the lower levels. The MPH stereo system does not overly rely on one type of primitive and therefore will reliably work on a wide range of scenes. The MPH stereo analysis results in the generation of several disparity maps of multiple abstraction. Disparity maps generated at each level can be fused to obtain an accurate and fine resolution disparity map. The MPH approach also provides the capability to selectively analyze image regions with varying detail. This provides the means for adaptively extracting range information of only sufficient resolution. Thus, a stereo system that utilizes primitives of different abstraction and a multilevel hierarchical computational strategy will be superior to a single-level, single-primitive system. Extensive experimentation is carried out on a wide array of scenes of varying complexity from two application domains to systematically evaluate the validity and performance of the MPH framework. The MPH stereo system is able to analyze images in most cases with 85%/spl sim/100% matching accuracy in under a minute of processing time and yield depth values typically within /spl plusmn/2% of the actual depth.