A real-time orientation feature descriptor for portable devices is introduced. The descriptor requires very low
computational resources and has 16 dimensions shorter than all existing methods. The patch of a candidate feature is
firstly segmented into polar arranged sub-regions, which enables us to achieve rotation invariance rapidly. Furthermore,
the principal orientation is used to describe each sub-region. The computations can be considerably accelerated by using
integral image. The descriptor is used for object tracking and achieves 25 fps frame rate on mobile phone. Experimental
results demonstrate that the proposed method offers sufficient matching performance.
For building detection from single very high spatial resolution (VHR) satellite images, we take advantage of visual saliency and Bayesian model to rapidly locate roof-top areas. We firstly generate saliency map of an image by a salient contrast filter using low-level feature. This filter distinguishes salient pixels if a pixel is visually different from its surroundings in color or texture. Secondly, a Bayesian model is proposed to generate all closed rectangular contours as mid-level content in the image. We suggest the area enclosed by contour corresponds to high saliency values. Finally, the roof-top areas are extracted by fusing different level information mentioned above. Experimental results demonstrate the effectiveness of our algorithm.
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