scholarly journals Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features

2009 ◽  
pp. 61-70 ◽  
Author(s):  
Timo Ahonen ◽  
Jiří Matas ◽  
Chu He ◽  
Matti Pietikäinen
Keyword(s):  
Local Binary Pattern ◽  
Image Description ◽  
Rotation Invariant ◽  
Pattern Histogram

scholarly journals HOLBP: Remote Sensing Image Registration Based on Histogram of Oriented Local Binary Pattern Descriptor

2021 ◽  
Vol 13 (12) ◽  
pp. 2328
Author(s):  
Yameng Hong ◽  
Chengcai Leng ◽  
Xinyue Zhang ◽  
Zhao Pei ◽  
Irene Cheng ◽  
...  
Keyword(s):  
Image Registration ◽  
Local Binary Pattern ◽  
Computational Time ◽  
Rotation Invariant ◽  
Edge Information ◽  
Important Research Topic ◽  
New Construction ◽  
Novel Method ◽  
Direction Information ◽  
Angle Calculation

Image registration has always been an important research topic. This paper proposes a novel method of constructing descriptors called the histogram of oriented local binary pattern descriptor (HOLBP) for fast and robust matching. There are three new components in our algorithm. First, we redefined the gradient and angle calculation template to make it more sensitive to edge information. Second, we proposed a new construction method of the HOLBP descriptor and improved the traditional local binary pattern (LBP) computation template. Third, the principle of uniform rotation-invariant LBP was applied to add 10-dimensional gradient direction information to form a 138-dimension HOLBP descriptor vector. The experimental results showed that our method is very stable in terms of accuracy and computational time for different test images.

Image Retrieval using Modified Multi Texton and Rotation Invariant Local Binary Pattern

2020 ◽  
Author(s):  
Fitri Bimantoro ◽  
Ashri Annisaak Aziz ◽  
Ario Yudo Husodo ◽  
Ahmad Musnansyah ◽  
Agus Eko Minarno ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Local Binary Pattern ◽  
Rotation Invariant

scholarly journals Unsupervised Local Binary Pattern Histogram Selection Scores for Color Texture Classification

2018 ◽  
Vol 4 (10) ◽  
pp. 112 ◽  
Author(s):  
Mariam Kalakech ◽  
Alice Porebski ◽  
Nicolas Vandenbroucke ◽  
Denis Hamad
Keyword(s):  
Local Binary Pattern ◽  
Color Image ◽  
Texture Classification ◽  
Classification Problems ◽  
Class Label ◽  
Laplacian Score ◽  
Pattern Histogram ◽  
Color Texture

These last few years, several supervised scores have been proposed in the literature to select histograms. Applied to color texture classification problems, these scores have improved the accuracy by selecting the most discriminant histograms among a set of available ones computed from a color image. In this paper, two new scores are proposed to select histograms: The adapted Variance score and the adapted Laplacian score. These new scores are computed without considering the class label of the images, contrary to what is done until now. Experiments, achieved on OuTex, USPTex, and BarkTex sets, show that these unsupervised scores give as good results as the supervised ones for LBP histogram selection.

Rotation-Invariant Image and Video Description With Local Binary Pattern Features

2012 ◽  
Vol 21 (4) ◽  
pp. 1465-1477 ◽  
Author(s):  
Guoying Zhao ◽  
T. Ahonen ◽  
J. Matas ◽  
M. Pietikainen
Keyword(s):  
Local Binary Pattern ◽  
Rotation Invariant ◽  
Video Description

Implementasi Webcam sebagai Pendeteksi Wajah pada Sistem Keamanan Perumahan menggunakan Image Processing

ELECTRICES ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 1-5
Author(s):  
Arba Abdul Syukur
Keyword(s):  
Image Processing ◽  
Local Binary Pattern ◽  
Raspberry Pi ◽  
Pattern Histogram ◽  
False Reject Rate

Pencurian yang sangat meresahkan masyarakat seringkali terjadi pada suatu ruangan atau lingkungan seperti gedung, kantor, lorong bahkan tempat ibadah juga menjadi sasaran para pencuri. Upaya yang dilakukan DKM (Dewan Kemakmuran Masjid) yaitu memberikan himbauan supaya tetap menjaga barang pentingnya masing-masing.  Masjid seharusnya menjadi tempat yang aman dan nyaman untuk dikunjungi. Oleh karena itu kami memiliki ide yang bertujuan untuk mengantisipasi pencurian di masjid atau tempattempat yang rawan pencurian. Penelitian ini merancangbangun sistem pengenalan wajah sebagai solusi untuk mengurangi tingkat pencurian. Sistem ini dilengkapi dengan perangkat keras Raspberry Pi 3 model B dan webcam A4Tech. Perangkat lunak database yang dapat menyimpan data pengguna. Tujuan penelitian untuk membandingkan 2 metode yang terbaik dalam pengenalan wajah yaitu metode LBPH (Local Binary Pattern Histogram) dan metode Eigenface. Penelitian dilakukan pada siang hari untuk mengambil citra wajah yang berbeda. Penelitian dilakukan dengan 3 kondisi yaitu siang hari luar ruangan, siang hari dalam ruangan dan malam hari dalam ruangan. Parameter yang digunakan untuk melihat hasil dari pengenalan wajah yaitu Akurasi, FAR (False Accept Rate) dan FRR (False Reject Rate). Hasil pengujian 2 metode tersebut yang memiliki tingkat rata-rata Akurasi tertinggi dan tingkat rata-rata FAR dan FRR terendah adalah metode  Eigenface. Kesimpulan dari hasil penelitian yaitu pencahayaan mempengaruhi pengenalan wajah dalam 2 metode tersebut.

scholarly journals Face Recognition in an Unconstrained and Real-Time Environment Using Novel BMC-LBPH Methods Incorporates with DJI Vision Sensor

2020 ◽  
Vol 9 (4) ◽  
pp. 54
Author(s):  
Md Manjurul Ahsan ◽  
Yueqing Li ◽  
Jing Zhang ◽  
Md Tanvir Ahad ◽  
Munshi Md. Shafwat Yazdan
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Local Binary Pattern ◽  
Computation Time ◽  
Threshold Value ◽  
Light Illumination ◽  
Rainy Weather ◽  
Face Datasets ◽  
Bad Weather ◽  
Pattern Histogram

Face recognition (FR) in an unconstrained environment, such as low light, illumination variations, and bad weather is very challenging and still needs intensive further study. Previously, numerous experiments on FR in an unconstrained environment have been assessed using Eigenface, Fisherface, and Local binary pattern histogram (LBPH) algorithms. The result indicates that LBPH FR is the optimal one compared to others due to its robustness in various lighting conditions. However, no specific experiment has been conducted to identify the best setting of four parameters of LBPH, radius, neighbors, grid, and the threshold value, for FR techniques in terms of accuracy and computation time. Additionally, the overall performance of LBPH in the unconstrained environments are usually underestimated. Therefore, in this work, an in-depth experiment is carried out to evaluate the four LBPH parameters using two face datasets: Lamar University data base (LUDB) and 5_celebrity dataset, and a novel Bilateral Median Convolution-Local binary pattern histogram (BMC-LBPH) method was proposed and examined in real-time in rainy weather using an unmanned aerial vehicle (UAV) incorporates with 4 vision sensors. The experimental results showed that the proposed BMC-LBPH FR techniques outperformed the traditional LBPH methods by achieving the accuracy of 65%, 98%, and 78% in 5_celebrity dataset, LU dataset, and rainy weather, respectively. Ultimately, the proposed method provides a promising solution for facial recognition using UAV.

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