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Computer Vision Metrics: Chapter Six (Part G)

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For Part F of Chapter Six, please click here.

Bibliography references are set off with brackets, i.e. "[XXX]". For the corresponding bibliography entries, please click here.


Local Gradient Pattern

A variation of the LBP approach, the local gradient pattern (LGP) [204] uses local region gradients instead of local image intensity pair comparison to form the binary descriptor. The 3x3 gradient of each pixel in the local region is computed, then each gradient magnitude is compared to the mean value of all the local region gradients, and the binary bit value of 1 is assigned if the value is greater, and 0 otherwise. The authors claim accuracy and discrimination improvements over the basic LBP in face-recognition algorithms, including a reduction in false positives. However, the compute requirements are greatly increased due to the local region gradient computations.

LGP Summary Taxonomy

Spectra: Local region gradient comparisons between center pixel and local region gradients

Feature shape: Square

Feature pattern: Every pixel 3x3 kernel region

Feature density: Dense in 3x3 region

Search method: Sliding window

Distance function: Hamming

Robustness: 3 (illumination, scale, rotation)

Local Phase Quantization

The local phase quantization (LPQ) descriptor [166–168] was designed to be robust to image blur, and it leverages the blur insensitive property of Fourier phase information. Since the Fourier transform is required to compute phase, there is some compute overhead; however, integer DFT methods can be used for acceleration. LPQ is reported to provide robustness for uniform blur, as well as uniform illumination changes. LPQ is reported to provide equal or slightly better accuracy on nonblurred images than LBP and Gabor filter bank methods. While mainly used for texture description, LPQ can also be used for local feature description to add blur invariance by combining LPQ with another descriptor method such as SIFT.

To compute...