Immature citrus fruit detection based on local binary pattern feature and hierarchical contour analysis

2018 ◽  
Vol 171 ◽  
pp. 78-90 ◽  
Author(s):  
Jun Lu ◽  
Won Suk Lee ◽  
Hao Gan ◽  
Xiuwen Hu
Keyword(s):  
Local Binary Pattern ◽  
Citrus Fruit ◽  
Contour Analysis ◽  
Local Binary Pattern Feature

Self-Adapting Patch Strategies for Face Recognition

2019 ◽  
Vol 34 (02) ◽  
pp. 2056002 ◽  
Author(s):  
Zhi-Ming Li ◽  
Wen-Juan Li ◽  
Jun Wang
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Greedy Algorithms ◽  
Discrete Wavelet ◽  
Projection Technique ◽  
Sparse Constraint ◽  
Integral Projection ◽  
New Methods ◽  
Information Combining ◽  
Local Binary Pattern Feature

In this paper, we propose two self-adapting patch strategies, which are obtained by employing the integral projection technique on images’ edge images, while the edge images are recovered by the two-dimensional discrete wavelet transform. The patch strategies are equipped with the advantage of considering the single image’s unique properties and maintaining the integrity of some particular local information. Combining the self-adapting patch strategies with local binary pattern feature extraction and the classifier of the forward and backward greedy algorithms under strong sparse constraint, we propose two new face recognition methods. Experiments are run on the Georgia Tech, LFW and AR face databases. The obtained numerical results show that the new methods outperform some related patch-based methods to a larger extent.

scholarly journals HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2045 ◽  
Author(s):  
Haris Khan ◽  
Sofiane Mihoubi ◽  
Benjamin Mathon ◽  
Jean-Baptiste Thomas ◽  
Jon Hardeberg
Keyword(s):  
Spectral Analysis ◽  
High Resolution ◽  
Matrix Factorization ◽  
Local Binary Pattern ◽  
Near Infrared ◽  
Texture Classification ◽  
Hyperspectral Images ◽  
Close Range ◽  
Local Binary Pattern Feature ◽  
Non Negative Matrix Factorization

We present a dataset of close range hyperspectral images of materials that span the visible and near infrared spectrums: HyTexiLa (Hyperspectral Texture images acquired in Laboratory). The data is intended to provide high spectral and spatial resolution reflectance images of 112 materials to study spatial and spectral textures. In this paper we discuss the calibration of the data and the method for addressing the distortions during image acquisition. We provide a spectral analysis based on non-negative matrix factorization to quantify the spectral complexity of the samples and extend local binary pattern operators to the hyperspectral texture analysis. The results demonstrate that although the spectral complexity of each of the textures is generally low, increasing the number of bands permits better texture classification, with the opponent band local binary pattern feature giving the best performance.

scholarly journals Facial Emotion Identification Based on Local Binary Pattern Feature Detector

2019 ◽  
Vol 12 (28) ◽  
pp. 1-6
Author(s):  
Sahar Zafar Jumani ◽  
Fayyaz Ali ◽  
Irfan Ali Kandhro ◽  
Qurban Ali Lakhan ◽  
Usman Ali ◽  
...  
Keyword(s):  
Local Binary Pattern ◽  
Facial Emotion ◽  
Feature Detector ◽  
Emotion Identification ◽  
Local Binary Pattern Feature
Sign in / Sign up

Export Citation Format

Share Document