Crowd counting system by facial recognition using Histogram of Oriented Gradients, Completed Local Binary Pattern, Gray-Level Co-Occurrence Matrix and Unmanned Aerial Vehicle

2018 ◽  
Author(s):  
Jessie R. Balbin ◽  
Ramon G. Garcia ◽  
Kaira Emi D. Fernandez ◽  
Nicolo Paolo G. Golosinda ◽  
Karyl Denise G. Magpayo ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Local Binary Pattern ◽  
Facial Recognition ◽  
Gray Level ◽  
Histogram Of Oriented Gradients ◽  
Counting System ◽  
Crowd Counting ◽  
Aerial Vehicle ◽  
Occurrence Matrix

scholarly journals Design of facial recognition system implemented in an unmanned aerial vehicle for citizen security in Latin America

2019 ◽  
Vol 27 ◽  
pp. 04002
Author(s):  
Diego Herrera ◽  
Hiroki Imamura
Keyword(s):  
Neural Network ◽  
Latin America ◽  
Deep Learning ◽  
Mobile Devices ◽  
Unmanned Aerial Vehicle ◽  
Facial Recognition ◽  
Recognition System ◽  
Criminal Records ◽  
Aerial Vehicle ◽  
Facial Recognition System

In the new technological era, facial recognition has become a central issue for a great number of engineers. Currently, there are a great number of techniques for facial recognition, but in this research, we focus on the use of deep learning. The problems with current facial recognition convection systems are that they are developed in non-mobile devices. This research intends to develop a Facial Recognition System implemented in an unmanned aerial vehicle of the quadcopter type. While it is true, there are quadcopters capable of detecting faces and/or shapes and following them, but most are for fun and entertainment. This research focuses on the facial recognition of people with criminal records, for which a neural network is trained. The Caffe framework is used for the training of a convolutional neural network. The system is developed on the NVIDIA Jetson TX2 motherboard. The design and construction of the quadcopter are done from scratch because we need the UAV for adapt to our requirements. This research aims to reduce violence and crime in Latin America.

scholarly journals LBP and GLCM Based Image Forgery Recognition

2018 ◽  
Vol 7 (4.6) ◽  
pp. 217
Author(s):  
D. Vaishnavi ◽  
T. S. Subashini ◽  
G. N. Balaji ◽  
D. Mahalakshmi
Keyword(s):  
Neural Network ◽  
Local Binary Pattern ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Gray Level ◽  
Image Splicing ◽  
Naked Eye ◽  
Occurrence Matrix

The forgery of digital images became very easy and it’s very difficult to ascertain the authenticity of such images by naked eye. Among the various kinds of image forgeries, image splicing is a frequent and widely used technique. Even though various methods are available to detect image splicing forgery, authors have attempted to provide a novel hybrid method which can yield greater accuracy, sensitivity and specificity. In this method, gray level co-occurrence matrix (GLCM) features are extracted using local binary pattern (LBP) operator on the image and the detection of the splicing forged images among the authentic images is done using the popular pattern recognition algorithms such as combined k-NN (Comb-KNN), back propagation neural network (BPNN) and support vector machine (SVM). The recorded results are also compared with the existing results of the previous studies to ascertain the quality of the results.  

scholarly journals Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features

2021 ◽  
Vol 11 ◽  
Author(s):  
Yan Hao ◽  
Shichang Qiao ◽  
Li Zhang ◽  
Ting Xu ◽  
Yanping Bai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Local Binary Pattern ◽  
Recognition Accuracy ◽  
Pathological Examination ◽  
Support Vector ◽  
Gray Level ◽  
Histopathological Images ◽  
Occurrence Matrix ◽  
Low Dimensional ◽  
Images Recognition

Breast cancer (BC) is the primary threat to women’s health, and early diagnosis of breast cancer is imperative. Although there are many ways to diagnose breast cancer, the gold standard is still pathological examination. In this paper, a low dimensional three-channel features based breast cancer histopathological images recognition method is proposed to achieve fast and accurate breast cancer benign and malignant recognition. Three-channel features of 10 descriptors were extracted, which are gray level co-occurrence matrix on one direction (GLCM1), gray level co-occurrence matrix on four directions (GLCM4), average pixel value of each channel (APVEC), Hu invariant moment (HIM), wavelet features, Tamura, completed local binary pattern (CLBP), local binary pattern (LBP), Gabor, histogram of oriented gradient (Hog), respectively. Then support vector machine (SVM) was used to assess their performance. Experiments on BreaKHis dataset show that GLCM1, GLCM4 and APVEC achieved the recognition accuracy of 90.2%-94.97% at the image level and 89.18%-94.24% at the patient level, which is better than many state-of-the-art methods, including many deep learning frameworks. The experimental results show that the breast cancer recognition based on high dimensional features will increase the recognition time, but the recognition accuracy is not greatly improved. Three-channel features will enhance the recognizability of the image, so as to achieve higher recognition accuracy than gray-level features.

scholarly journals Assessment of Image-Texture Improvement Applied to Unmanned Aerial Vehicle Imagery for the Identification of Biotic Stress in Espeletia. Case Study: Moorlands of Chingaza (Colombia)

2019 ◽  
Vol 30 (1) ◽  
pp. 27-44 ◽  
Author(s):  
Laura Daniela Martin ◽  
Javier Medina ◽  
Erika Upegui
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Biotic Stress ◽  
Kappa Coefficient ◽  
Image Texture ◽  
Support Vector ◽  
Bhattacharyya Distance ◽  
Image Texture Analysis ◽  
Vector Machines ◽  
Aerial Vehicle ◽  
Occurrence Matrix

Espeletia is one of the most representative endemic species of moorland ecosystems, and is currently being affected by biotic stress. Meanwhile, the analysis of images obtained by means of unmanned aerial vehicle imagery has proved its usefulness in environmental monitoring activities. The present work is aimed at establishing whether image-texture analysis applied to unmanned aerial vehicle imagery from Moorlands of Chingaza (Colombia) allows the identification of biotic stress in Espeletia. To this end, this study makes use of occurrence analysis, gray-level co-occurrence matrix, and Fourier transform. Identification of healthy/unhealthy Espeletia is conducted using maximum likelihood tests and support vector machines. The results are assessed based on overall accuracy, the kappa coefficient and bhattacharyya distance. By combining spectral and image-texture information, it is shown that classification accuracy increases, reaching kappa coefficient values of 0,9824 and overall accuracy values of 99,51%. 

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