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Stereo matching has been one of the most active areas in computer vision for decades. Many methods, ranging from similarity measures to local or global matching cost optimization algorithms, have been proposed. In this paper, we propose a novel similarity measure under log-euclidean metric for stereo matching. A generalized structure tensor is applied to describe a point and the similarity is measured...
Stereo matching has been one of the most active research areas in computer vision for decades. Many methods, ranging from similarity measures to local or global matching cost optimization algorithms, have been proposed. As we known, stereo matching can be formulated under the framework of Markov random field (MRF), and the global optimization in stereo matching can be approximated by inference procedure...
The detection performance on the sintering state of the rotary kiln is mostly dependent on the features used in the recognition process. So an optimization approach of sintering feature parameters based on fuzzy support vector machines (SVM) is proposed. This method firstly uses many feature parameters to describe an image, and then reduce some useless features by portfolio optimization algorithm...
Stereo matching has been one of the most active areas in computer vision for decades. Many methods, ranging from similarity measures to local or global matching cost optimization algorithms, have been proposed. In this paper, we propose a novel similarity measure under Riemannian metric. A generalized structure tensor is applied to describe a point and the similarity is measured by the distance between...
The minimum mean-square error (MMSE) is an important optimization criterion, which is widely applied to many fields in signal processing and others, such as waveforms estimation, signal detection and system identification. In many practical scenarios, the optimal solutions are usually expected to locate in some especial subspace. So, it is significant to study the MMSE detector or estimator with various...
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