Multiplicative Local Binary Patterns (MuLBP)

2019 ◽  
Author(s):  
M. Mora ◽  
M. Silva-Ibarra ◽  
M. Acevedo-Letelier
Keyword(s):  
Local Binary Patterns

Local Binary Patterns with ENI features for Images classification and analysis

2011 ◽  
Vol 2 (1) ◽  
pp. 45-50
Author(s):  
E. Suresh Babu ◽  
S. Salma ◽  
A. Reshma ◽  
C. Nagaraju
Keyword(s):  
Local Binary Patterns

scholarly journals Deformable Models for Segmentation Based on Local Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Jimena Olveres ◽  
Erik Carbajal-Degante ◽  
Boris Escalante-Ramírez ◽  
Enrique Vallejo ◽  
Carla María García-Moreno
Keyword(s):  
Local Binary Patterns ◽  
Distance Measures ◽  
Local Analysis ◽  
Hermite Transform ◽  
Active Shape Models ◽  
Shape Models ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Image Modality ◽  
New Algorithms

Segmentation tasks in medical imaging represent an exhaustive challenge for scientists since the image acquisition nature yields issues that hamper the correct reconstruction and visualization processes. Depending on the specific image modality, we have to consider limitations such as the presence of noise, vanished edges, or high intensity differences, known, in most cases, as inhomogeneities. New algorithms in segmentation are required to provide a better performance. This paper presents a new unified approach to improve traditional segmentation methods as Active Shape Models and Chan-Vese model based on level set. The approach introduces a combination of local analysis implementations with classic segmentation algorithms that incorporates local texture information given by the Hermite transform and Local Binary Patterns. The mixture of both region-based methods and local descriptors highlights relevant regions by considering extra information which is helpful to delimit structures. We performed segmentation experiments on 2D images including midbrain in Magnetic Resonance Imaging and heart’s left ventricle endocardium in Computed Tomography. Quantitative evaluation was obtained with Dice coefficient and Hausdorff distance measures. Results display a substantial advantage over the original methods when we include our characterization schemes. We propose further research validation on different organ structures with promising results.

scholarly journals Monitoring of Flotation Systems by Use of Multivariate Froth Image Analysis

Minerals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 683
Author(s):  
Chris Aldrich ◽  
Xiu Liu
Keyword(s):  
Image Analysis ◽  
Process Monitoring ◽  
Chemical Species ◽  
Platinum Metal ◽  
Local Binary Patterns ◽  
Monitoring Methods ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Froth Stability ◽  
Froth Image

Froth image analysis has been considered widely in the identification of operational regimes in flotation circuits, the characterisation of froths in terms of bubble size distributions, froth stability and local froth velocity patterns, or as a basis for the development of inferential online sensors for chemical species in the froth. Relatively few studies have considered flotation froth image analysis in unsupervised process monitoring applications. In this study, it is shown that froth image analysis can be combined with traditional multivariate statistical process monitoring methods for reliable monitoring of industrial platinum metal group flotation plants. This can be accomplished with well-established methods of multivariate image analysis, such as the Haralick feature set derived from grey level co-occurrence matrices and local binary patterns that were considered in this investigation.

Local binary patterns on triangular meshes: Concept and applications

2015 ◽  
Vol 139 ◽  
pp. 161-177 ◽  
Author(s):  
Naoufel Werghi ◽  
Claudio Tortorici ◽  
Stefano Berretti ◽  
Alberto del Bimbo
Keyword(s):  
Local Binary Patterns ◽  
Triangular Meshes

Face Recognition Based on Local Binary Patterns with Threshold

2010 ◽  
Author(s):  
Jun Meng ◽  
Yumao Gao ◽  
Xiukun Wang ◽  
Tsauyoung Lin ◽  
Jianying Zhang
Keyword(s):  
Face Recognition ◽  
Local Binary Patterns
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