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Markov Random Fields are widely used to model lightfield stereo matching problems. However, most previous approaches used fixed parameters and did not adapt to lightfield statistics. Instead, they explored explicit vision cues to provide local adaptability and thus enhanced depth quality. But such additional assumptions could end up confining their applicability, e.g. algorithms designed for dense...
This paper presents a probabilistic algorithm for lane shape estimation in an urban environment which is important for example for driver assistance systems and autonomous driving. For the first time, we bring together the so-called Partitioned Particle filter, an improvement of the traditional Particle filter, and the linear-parabolic lane model which alleviates many shortcomings of traditional lane...
Road recognition is one of the key technologies in the vision-based intelligent navigation system. In this paper, we present a novel non-parametric estimation model and a robust approach for the unstructured road recognition. The model keeps a set of sample for both road region and off-road region, and then estimates the probability of a newly pixel based on color information. For improving the real...
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