Computer Vision Using Local Binary Patterns by Matti Pietikäinen

By Matti Pietikäinen

The contemporary emergence of neighborhood Binary styles (LBP) has resulted in major growth in using texture the right way to a variety of desktop imaginative and prescient difficulties and purposes. the point of interest of this study has broadened from 2nd textures to 3D textures and spatiotemporal (dynamic) textures. additionally, the place texture used to be applied for purposes akin to distant sensing, business inspection and biomedical photo research, the creation of LBP-based ways have supplied remarkable ends up in difficulties in relation to face and job research, with destiny scope for face and facial features acceptance, biometrics, visible surveillance and video analysis.

Computer imaginative and prescient utilizing neighborhood Binary Patterns offers an in depth description of the LBP tools and their versions either in spatial and spatiotemporal domain names. This finished reference additionally presents a very good review as to how texture tools can be used for fixing other kinds of machine imaginative and prescient and snapshot research difficulties. resource codes of the elemental LBP algorithms, demonstrations, a few databases and a entire LBP bibliography are available from an accompanying net site.

Topics include:

- neighborhood binary styles and their editions in spatial and spatiotemporal domains
- Texture type and segmentation, description of curiosity regions
- purposes in picture retrieval and 3D recognition
- popularity and segmentation of dynamic textures
- heritage subtraction, attractiveness of actions
- Face research utilizing nonetheless photographs and photograph sequences, visible speech recognition
- LBP in numerous applications

Written by means of pioneers of LBP, this booklet is a necessary source for researchers, expert engineers and graduate scholars in laptop imaginative and prescient, photograph research and trend reputation. The booklet may also be of curiosity to all those that paintings with particular functions of laptop vision.

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Extra info for Computer Vision Using Local Binary Patterns

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The uniform patterns allows to see the LBP method as a unifying approach to the traditionally divergent statistical and structural models of texture analysis [45]. Each pixel is labeled with the code of the texture primitive that best matches the local neighborhood. Thus each LBP code can be regarded as a micro-texton. Local primitives detected by the LBP include spots, flat areas, edges, edge ends, curves and so on. Some examples are shown in Fig. 3 with the LBP8,R operator. In the figure, ones are represented as black circles, and zeros are white.

First the Sobel edge detector is used and the LBPs are computed from the gradient magnitude images. They also applied Sobel-LBP on both the real and imaginary features of the Gabor filtered images. Li et al. [37] proposed an approach based on capturing the intrinsic structural information of face appearances with multi-scale heat kernel matrices. Heat kernels perform well in characterizing the topological structural information of face appearance. Histograms of local binary patterns computed for non-overlapping blocks are then used for face description.

Another operator related to CS-LBP is centralized binary pattern (CBP) proposed by Fu and Wei [14] for facial expression recognition. CBP considers the contribution of the center pixel by comparing its value to the average of all nine pixels in the neighborhood, and encodes this bit with the largest weight. Mu et al. developed LBP variants with an application in human detection in personal album [48]. They found that the original LBP does not suit so well for this problem due to its relatively high complexity and lack of semantic consistency.

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