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Finding corresponding image points is a challenging computer vision problem, especially for confusing scenes with surfaces of low textures or repeated patterns. Despite the well-known challenges of extracting conceptually meaningful high-level matching primitives, many recent works describe high-level image features such as edge groups, lines and regions, which are more distinctive than traditional...
Many well-known existing image matching methods are based on local texture analysis, and consequently have difficulty handling low-textured 3D objects, such as those man-made buildings and road networks in urban scenes. In this paper, we propose our urban images matching method utilizing multiple novel clues. Specifically, we explore robust image features generated by interest regions and edge groups...
A new feature descriptor is presented for object and scene recognition. The new approach, called CDIKP, uniquely combines the scale-invariant feature detection with a robust projection kernel technique to produce highly efficient feature representation. The produced feature descriptors are highly-compact in comparisons to the state-of-the-art, do not require any pretraining step, and show superior...
This paper proposes a general feature selection approach for real-time image matching systems. To demonstrate the idea??s effectiveness, we focus on the issue of rotational invariance. Most current image matching methods compute and align local image patches to a uniform dominant orientation, which are either too computationally expensive for real-time systems or insufficiently robust. In contrast...
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