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In this study, the authors present a new area‐based stereo matching algorithm that computes dense disparity maps for a real‐time vision system. Although many stereo matching algorithms have been proposed in recent years, correlation‐based algorithms still have an edge because of speed and less memory requirements. The selection of appropriate shape and size of the matching window is a difficult problem for correlation‐based algorithms. In the proposed approach, two correlation windows are used to improve the performance of the algorithm while maintaining its real‐time suitability. The CPU implementation of the proposed algorithm computes more than 10 frame/s. Unlike other area‐based stereo matching algorithms, this method works very well at disparity boundaries as well as in low textured image areas and computes a dense and sharp disparity map. Evaluations on the benchmark Middlebury stereo datasets have been performed to demonstrate the qualitative and quantitative performance of the proposed algorithm.

A new approach for measuring shape and surface of an object for engineering measurement is proposed. The proposed methodology is a neural network based Shape from Shading (SFS) technique. In this SFS approach, the physical parameters of the reflectivity under different lighting conditions are interpreted by the neural network weights. The proposed technique optimises a proper reflectance model by an effective neural learning algorithm. The depth of the object surface is recovered by using a simple SFS recursive algorithm. Experimental results are demonstrated that the proposed technique exhibits high efficiency and accuracy of measuring an object surface for the manufacturing industry.

The shape from shading (SFS) problem refers to the well-known fact that most real images usually contain specular components and are affected by unknown reflectivity. In this paper, these limitations are addressed and a new neural-based three-dimensional (3-D) shape reconstruction model is proposed. The idea behind this approach is to optimize a proper reflectance model by learning the parameters of the proposed neural reflectance model. In order to do this, new neural-based reflectance models are presented. The FNN model is able to generalize the diffuse term, while the RBF model is able to generalize the specular term. A hybrid structure of FNN-based and RBF-based models is also presented because most real surfaces are usually neither Lambertian models nor ideally specular models. Experimental results, including synthetic and real images, are presented to demonstrate the performance of our approach given different specular effects, unknown illuminate conditions, and different noise environments. Index Terms--Neural-based reflectance model, shape from shading (SFS), specular reflection.

In this study, the authors present a new area-based stereo matching algorithm that computes dense disparity maps for a real-time vision system. Although many stereo matching algorithms have been proposed in recent years, correlation-based algorithms still have an edge because of speed and less memory requirements. The selection of appropriate shape and size of the matching window is a difficult problem for correlation-based algorithms. In the proposed approach, two correlation windows are used to improve the performance of the algorithm while maintaining its real-time suitability. The CPU implementation of the proposed algorithm computes more than 10 frame/s. Unlike other area-based stereo matching algorithms, this method works very well at disparity boundaries as well as in low textured image areas and computes a dense and sharp disparity map. Evaluations on the benchmark Middlebury stereo datasets have been performed to demonstrate the qualitative and quantitative performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]