Motion and Structure From Two Perspective Views: Algorithms, Error Analysis, and Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Performance Evaluation of Scene Registration and Stereo Matching for Artographic Feature Extraction
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
A locally adaptive window for signal matching
International Journal of Computer Vision
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Non-parametric local transforms for computing visual correspondence
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
Plenoptic modeling: an image-based rendering system
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
A maximum likelihood stereo algorithm
Computer Vision and Image Understanding
Stereo matching based on the self-organizing feature-mapping algorithm
Pattern Recognition Letters
International Journal of Computer Vision
A genetic aggregate stereo algorithm for 3-D classification of occluded shapes
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Symmetric Sub-Pixel Stereo Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Stereo Matching with Segmentation-Based Cooperation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Stereo matching and occlusion detection with integrity and illusion sensitivity
Pattern Recognition Letters
Disparity Component Matching for Visual Correspondence
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Neural adaptive stereo matching
Pattern Recognition Letters
High-accuracy stereo depth maps using structured light
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Structural Descriptions and Inexact Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereopsis by constraint learning feed-forward neural networks
IEEE Transactions on Neural Networks
Dense Two-Frame Stereo Correspondence by Self-organizing Neural Network
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
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This work aims at defining a new method for matching correspondences in stereoscopic image analysis. A representation of occlusions drives the overall matching process. Based on the taxonomy proposed by Scharstein and Szelinsky (2002, IJCV, 47, 7-42), the dense stereo matching process is divided into three tasks: matching cost computation, aggregation of local evidence and computation of disparity values. Within the second and third phases new strategies are introduced in an attempt to improve the reliability of results. Aggregation is based on a new local matching measure, and neural techniques compute disparities adaptively. Two experimental studies were conducted to evaluate and compare the solutions proposed. The first uses a standard well-known dataset including data with true disparity maps; the second study was conducted on complex real images acquired by a scanning electron microscope (SEM).