1 - 6 of 6 Chapters
[Local image descriptor is a kind of vector (either in float type or binary type) used as the signature of a local image. The aim of this representation is to make the local image as distinctive as possible while maintaining robustness to various kinds of image transformations, both photometric...
[Classical local descriptors refer to those were proposed many years ago but have a profound influence on the development of local image description as well as related applications. Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Feature (SURF) are the two widely used descriptors...
[In this chapter, we discuss the local descriptors which exploit intensity order information. The good property of intensity order is that it is invariant to monotonic brightness change, which has been well studied in recent years, leading to the state-of-the-art descriptors for image matching....
[Binary descriptor requires much less storage than that of traditional descriptors, i.e., float vector descriptors. Meanwhile, the Hamming distance used for evaluating similarity between binary descriptors can be computed much faster than computing Euclidean distance between float vector...
[Local image descriptors have been widely used in many computer vision applications such as 3D reconstruction, object detection and recognition, image stitch, image retrieval, and localization etc. to name a few. In this chapter, we will introduce some of them, and show how a robust and...
[In this chapter, we will first briefly introduce several benchmarks used for evaluating local image descriptors. They are organized based on different visual applications. Finally, to conclude this book, we would like to draw some remarks about the current status of this area, and describe some...
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