Fake Image Detection Using DCT and Local Binary Pattern

2019 ◽  
Author(s):  
Ayah Kunbaz ◽  
Souzi Saghir ◽  
Mira Arar ◽  
Elena Battini Sonmez
Keyword(s):  
Local Binary Pattern ◽  
Image Detection

scholarly journals Variants of Local Binary Pattern: A Review

2021 ◽  
Author(s):  
Aditya Singh ◽  
Ramesh K. Sunkaria ◽  
Anterpreet Kaur Bedi
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
Information Processing ◽  
Image Retrieval ◽  
Everyday Life ◽  
Local Binary Pattern ◽  
Image Data ◽  
Image Detection ◽  
Classification Image ◽  
Binary Model

In everyday life, image data, such as information, processing and computer vision, play a significant role in many applications; such as image classification, image segmentation and image retrieval. A preferred attributes that has been applied in a lots of picture applications is texture. This was forming an image using an array of pixels. Texture played a significant part in image segmentation and image detection and retrieval. In addition ranked the classifications of the texture then local binary pattern are coming. The LBP method seemed to work very effectively in real time. In the LBP method, comparing the values of the central pixels with the values of the neighbouring pixels and to attribute the binary values on these values. In this paper providing an overview for the local binary model and their benefits.

USM Sharpening Image Detection Based on Local Binary Pattern Method

2019 ◽  
Vol 56 (12) ◽  
pp. 121002
Author(s):  
全永志 Yongzhi Quan ◽  
高树辉 Shuhui Gao ◽  
杨孟京 Mengjing Yang ◽  
姜晓佳 Xiaojia Jiang ◽  
何欣龙 Xinlong He
Keyword(s):  
Local Binary Pattern ◽  
Image Detection

scholarly journals Penerapan Local Binary Pattern untuk Mengukur Tingkat Kepuasan Pengunjung secara Otomatis

2019 ◽  
Vol 6 (4) ◽  
pp. 363
Author(s):  
Elke Cahya Putri ◽  
Bayu Priyambadha ◽  
Fajar Pradana
Keyword(s):  
Local Binary Pattern ◽  
Plant Conservation ◽  
Botanical Garden ◽  
Service Evaluation ◽  
Image Detection ◽  
Government Organization ◽  
Visitor Satisfaction ◽  
Human Object ◽  
Image Format

<p class="Judul2">Pusat Konservasi Tumbuhan Kebun Raya-LIPI merupakan organisasi pemerintah yang berfokus pada bidang konservasi tumbuhan yang sekaligus berperan sebagai pihak pengelola kebun raya Bogor. Pihak pengelola membutuhkan masukkan atau testimoni pengunjung sebagai bahan evaluasi pelayanan yang diberikan untuk pengunjung. Pada pelaksanaannya, selama ini pengunjung memberikan masukkannya pada laman <em>fanpage</em> facebook kebun raya Bogor. Cara ini dianggap kurang akurat untuk merepresentasikakn tingkat kepuasan pengunjung hanya berdasarkan tulisan yang di publikasikan. Dari permasalahan tersebut maka dikembangkan salah satu fitur pada sistem Layanan Online Kebun Raya Bogor untuk mempermudah pengunjung Kebun Raya Bogor memberikan masukkan atau testimoni kepada pihak pengelola. Testimoni yang di unggah tidak hanya dalam bentuk tulisan tetapi juga dalam format gambar yang terdapat objek manusia, gambar yang di unggah nantinya akan di deteksi dengan menggunakan <em>Local Binary Pattern</em> (LBP) untuk mendapatkan hasil klasifikasi dari ekspresi yang ditunjukkan. Hasil pendeteksian gambar dapat digunakan sebagai analisis tingkat kepuasan pengunjung terhadap pelayanan yang diberikan oleh pihak pengelola dari jumlah ekpresi pada satu lokasi objek wisata. Dari hasil penelitian yang dilakukan, didapatkan hasil akurasi pendeteksian gambar sebesar 0.884 terhadap pengklasifikasian ekspresi wajah yang di dapatkan.</p><p class="Judul2"><em><strong>Abstract</strong></em></p><p class="Judul2"><em>Plant Conservation Center Botanical Garden-LIPI is a government organization engaged in the conservation of plants and have a role as Kebun Raya Bogor organizer. The organizer requires visitor input or testimonial as a visitor service evaluation material. In its implementation, visitors gave their testimonial on the Kebun Raya Bogor Facebook fanpage. This method is considered less accurate to represent the level of visitor satisfaction based solely on the published texts. From that issue emerged an initiative to develop a feature on the Online Service system of Kebun Raya Bogor to facilitate Kebun Raya Bogor visitors to give testimonials to the organizer. Testimonials that are sent not only in the form of texts but also in the image format that contains the human object, the uploaded image will be detected by using the Local Binary Pattern (LBP) to get the result of the expression it captures. The results of image detection can be used to analyzed how the level of visitor satisfaction with the services provided by the organizer in a tourist attraction location. From this study, the result obtained an accurate image detection of 0.884 by using LBP for the classification of captured facial expression</em></p>

Face Recognition by Fuzzy Rule-Based Local Binary Pattern

2014 ◽  
Vol 8 (3) ◽  
pp. 31-34
Author(s):  
O. Rama Devi ◽  
◽  
L. S. S. Reddy ◽  
E. V. Prasad ◽  
◽  
...  
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Fuzzy Rule ◽  
Rule Based

Inappropriate thermal image detection system using machine learning

2020 ◽  
Vol 14 (2) ◽  
pp. 102-111
Author(s):  
S. W. Kwon ◽  
I. S. Song ◽  
S. W. Lee ◽  
J. S. Lee ◽  
J. H. Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Thermal Image ◽  
Image Detection

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.

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