isprsannals II 3 W2 25 2013
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KEY WORDS: Traffic sign detection, Sliding window, Object Detection, Frequency
As shown in Figure 1, the framework consists of two stages: (1) determining preliminary position and attitude using built-in sensory data such as velocity and angular rates, and
These values are used as additional feature to support the classification when the road surface is occluded by static cars.. Our approach is evaluated on a dataset of airborne photos
Once the features of the basic pitched roof component are recognized (top left), the feature recognition will work on the grey (sub-surface) segment (top middle) with the
Then, LIDAR points that are close to the street slice (closer than 20 cm here) in the top view are kept to approximate the local surface. Points used to approximate the road are
In this paper we develop and compare two methods for scene classification in 3D object space, that is, not single image pixels get classified, but voxels which carry geometric,
From top left to bottom right: fac¸ade image, visualiza- tion of colour differences to neighbouring images only, visualiza- tion of colour differences to complete sequence,
