Incremental reconstruction of 3D scenes from multiple, complex images
Artificial Intelligence
Stereo Matching with Nonlinear Diffusion
International Journal of Computer Vision
A new region matching method for stereoscopic images
Pattern Recognition Letters
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Matching and Retrieval of Distorted and Occluded Shapes Using Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constraint Based Region Matching for Image Retrieval
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
A new regions matching for color stereo images
Pattern Recognition Letters
Region-Based Hierarchical Image Matching
International Journal of Computer Vision
Feature Extraction & Image Processing, Second Edition
Feature Extraction & Image Processing, Second Edition
Stereo by Intra- and Inter-Scanline Search Using Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
A collective-based adaptive symbiotic model for surface reconstruction in area-based stereo
IEEE Transactions on Evolutionary Computation
Hopfield network for stereo vision correspondence
IEEE Transactions on Neural Networks
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This work aims to define a new strategy for extracting and stereo matching of buildings using very high resolution multi spectral IKONOS images having a ratio base/height about 0.53, we do not have the intrinsic and extrinsic parameters of the images acquisition system. These images contain dense urban scenes including various kinds of roads, cars, vegetation and buildings. We are interested by buildings, some of them have different shapes or colours and others have close colours or shapes, so, they generate a lot of ''false matches''. To solve this issue, we propose in this paper an approach based on soft computing field in order to extract regions of interest (buildings) and to match them, it contains two main steps: region segmentation and thresholding step using a specific fuzzy thresholding algorithm and a neural Hopfield matching stage based on new constraints including geometric and photometric regions properties. The presented strategy is nearly all automatic, it is fast and simple and the results of its applied tests on several kinds of stereo dense urban images are satisfactory.