Titlebook: Computer Vision Using Local Binary Patterns; Matti Pietikäinen,Abdenour Hadid,Timo Ahonen Book 2011 Springer-Verlag London Limited 2011 Co

Mucosa

Sappho and the Wordsworth Probleml findings which state that facial movements can provide valuable information to face analysis, this chapter investigates the use of spatiotemporal LBP for combining facial appearance (the shape of the face) and motion (the way a person is talking and moving his/her facial features) for face, facial expression and gender recognition from videos.

triptans

Local Binary Patterns for Still Imageson problems and applications has inspired much new research on different variants. The basic LBP has also some problems that need to be addressed. Therefore, several extensions and modifications of LBP have been proposed to increase its robustness and discriminative power.

Outshine

Description of Interest Regionscriptor and the LBP operator. It performed better than SIFT in image matching experiments especially for image pairs having illumination variations and about equally well in image categorization experiments.

PANT

Filibuster

Local Binary Patterns for Still Imagesltiscale versions are introduced. The use of complementary contrast information is also discussed. The success of LBP methods in various computer vision problems and applications has inspired much new research on different variants. The basic LBP has also some problems that need to be addressed. The

Myelin

拖债

STAT

Description of Interest Regionsest region description using center-symmetric local binary patterns (CS-LBP). The CS-LBP descriptor combines the advantages of the well-known SIFT descriptor and the LBP operator. It performed better than SIFT in image matching experiments especially for image pairs having illumination variations an

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conspicuous

Recognition and Segmentation of Dynamic Texturesent recognition results are obtained for different test databases providing state-of-the-art performance. The segmentation method extends the unsupervised segmentation method presented in Chap. . into spatiotemporal domain. It provides very promising results with less computational complexity than m