Open AccessJournal Article
Local Absolute Binary Patterns as Image Preprocessing for Grip-Pattern Recognition in Smart Gun
Xiaoxin Shang,Raymond N.J. Veldhuis,Kevin W. Bowyer,Patrick J. Flynn,Venu Govindaraju,Nalini K. Ratha +5 more
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TLDR
This work proposes a novel preprocessing technique, Local Absolute Binary Patterns, prior to grip-pattern classification, and shows that this technique can both reduce the variation of pressure distribution, and extract information of the hand shape in the image.About:
This article is published in Biosensors and Bioelectronics.The article was published on 2007-01-01 and is currently open access. It has received 2 citations till now. The article focuses on the topics: Feature (machine learning) & Pattern recognition (psychology).read more
Citations
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Local Binary Patterns as an Image Preprocessing for Face Authentication.
TL;DR: This work presents a new preprocessing algorithm based on local binary patterns (LBP): a texture representation is derived from the input face image before being forwarded to the classifier.
References
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Fundamentals of digital image processing
TL;DR: This chapter discusses two Dimensional Systems and Mathematical Preliminaries and their applications in Image Analysis and Computer Vision, as well as image reconstruction from Projections and image enhancement.
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A comparative study of texture measures with classification based on featured distributions
TL;DR: This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently.
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Local binary patterns variants as texture descriptors for medical image analysis
TL;DR: The results show that the novel variant named elongated quinary patterns (EQP) is a very performing method among those proposed in this work for extracting information from a texture in all the tested datasets.
Likelihood-ratio-based biometric verification
TL;DR: It is proved theoretically that, for multi-user verification, the use of the likelihood ratio is optimal in terms of average error rates and it is shown that error rates below 10/sup -3/ can be achieved when using multiple fingerprints for template construction.
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A simple method for improving local binary patterns by considering non-uniform patterns
TL;DR: It is concluded that non-uniform patterns improve classifier performance and are explored using random subspace, well-known to work well with noise and correlated features, to train features based also on non- uniform patterns.