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https://www.mdpi.com/2073-8994/11/1/97/htm
In order to identify the orientation or recognize the attitude of small symmetric magnetic anomaly objects at shallow depth, we propose a method of extracting local binary pattern (LBP) features from denoised magnetic anomaly signals and classifying symmetric magnetic objects that have different orientations based on support vector machine (SVM).Author: Jianyong Zheng, Hongbo Fan, Zhining Li, Qi Zhang
https://www-nlpir.nist.gov/projects/tvpubs/tv6.papers/nii.pdf
Concept Detection Using Local Binary Patterns and SVM Duy-Dinh Le1 and Shin’ichi Satoh1 National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japan 101-8430 [email protected], [email protected] Abstract. We introduce a method for detecting concepts by using only visual information. In this method, firstly the feature vector ...
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5903299/
Apr 03, 2018 · Feature computation processes the multiblock uniform local binary pattern (ULBP) to calculate each pixel of lung regions and count on the histogram. Finally, support vector machines train and classify normal lungs from abnormal lungs using ULBP features.Cited by: 1
https://ieeexplore.ieee.org/document/8095449/
Sep 20, 2017 · This paper presents insulator condition using local binary patterns combined with support vector machines. Experiments show an improved performance when local binary patterns is used as a feature extraction method over gray level co-occurrence matrix combined with support vector machines. Results obtained are presented and discussed.
https://upcommons.upc.edu/bitstream/handle/2099.1/10772/PFC_LauraSanchez_(LBP_applied_to_FaceDetection%26Recognition).pdf
by exploring the Local Binary Patterns operator, motivated by the following reasons. On one hand, it can be applied to face detection and recognition and on the other hand due to its robustness to pose and illumination changes. Local Binary Patterns were first used in …
https://www.sciencedirect.com/science/article/pii/S1877050917309730
Silhouette-Based Gender Recognition in Smart Environments Using Fuzzy Local Binary Patterns and Support Vector Machines. ... ANT 2017 Silhouette-Based Gender Recognition in Smart Environments Using Fuzzy Local Binary Patterns and Support Vector Machines El-Sayed M. El-Alfy∗, Amer G. Binsaadoon Information and Computer Science Department ...Cited by: 3
https://www.worldscientific.com/doi/abs/10.1142/S0129065707001317
Following the idea of local binary pattern, we propose a new feature extraction approach called multi-resolution LBP, which can retain both fine and coarse local micro-patterns and spatial information of facial images. The classification tasks in this work are performed by support vector machines (SVMs).Cited by: 90
https://www.researchgate.net/publication/325482063_Effective_Pneumothorax_Detection_for_Chest_X-Ray_Images_Using_Local_Binary_Pattern_and_Support_Vector_Machine
Effective Pneumothorax Detection for Chest X-Ray Images Using Local Binary Pattern and Support Vector Machine Article (PDF Available) in Journal of Healthcare Engineering 2018 · April 2018 with ...
https://ieeexplore.ieee.org/abstract/document/7830848/
Dec 11, 2016 · Our system is designed to reduce and eliminate the influence of the factors above. This article first used Local Binary Patterns (LBP) to detect face and cut out the face region. Then, based on the collected and pretreated face database, the target face could be recognized utilizing Support Vector Machine (SVM).
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4402446/
Images were first pre-processed and enhanced, and Local Binary Pattern Histograms were then extracted from 2 × 2 blocks of each image. A Support Vector Machine (SVM) was trained using stratified cross validation with randomised sampling. The process was repeated 15 times and in each round 100 images were randomly selected.Cited by: 5
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