By Bin Fan, Zhenhua Wang, Fuchao Wu
This e-book covers quite a lot of neighborhood photograph descriptors, from the classical ones to the state-of-the-art, in addition to the burgeoning study issues in this region. The target of this attempt is to enable readers recognize what are the preferred and precious equipment within the present, what are the benefits and the risks of those equipment, which type of equipment is healthier appropriate for his or her difficulties or functions, and what's the way forward for this region. what's extra, hands-on exemplars provided during this booklet can be of serious curiosity to computing device imaginative and prescient engineers and practitioners, in addition to these are looking to start their study during this quarter. total, this e-book is appropriate for graduates, researchers and engineers within the similar parts either as a studying textual content and as a reference book.
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Additional resources for Local Image Descriptor: Modern Approaches
786–791 (2005) Chapter 3 Intensity Order-Based Local Descriptors Abstract 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. It has been used in both feature construction and feature pooling. We first discuss the most straightforward method using intensity order information in a local image patch, which creates a 2D histogram of intensity order and location for feature description.
Mach. Intell. 33(7), 1281–1298 (2012) 5. : The Pascal Visual Object Classes (VOC) challenge. Int. J. Comput. Vision 88(2), 303–338 (2010) 6. : Rotationally invariant descriptors using intensity order pooling. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 2031–2045 (2012) 7. : Robust order-based methods for feature description. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 334–341 (2010) 8. : Description of interest regions with local binary patterns. Pattern Recogn. 42, 425–436 (2009) 9.
Moreover, LIOP only uses 4 neighboring points instead of 8 neighboring points while achieving a better performance. Given 4 neighboring points x0 , x1 , x2 , x3 of a pixel x, their intensities are denoted by I(x0 ), I(x1 ), I(x2 ), I(x3 ). As there are 24 possible permutations for 4 intensities, each permutation can be defined as a local intensity order pattern. A local intensity order pattern can be further represented by a 24-dimensional vector, with one element being 1 and the others being 0.