scholarly journals Insulator Contamination Grade Recognition Using the Deep Learning of Color Information of Images

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6662
Author(s):  
Da Zhang ◽  
Shuailin Chen
Keyword(s):  
Deep Learning ◽  
Color Space ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
Contact Detection ◽  
Assessment Methodology ◽  
Color Spaces ◽  
Color Information ◽  
Color Features ◽  
Hsi Color Space

To implement the non-contact detection of contamination on insulators, a contamination severity assessment methodology using the deep learning of the colored image information of insulators can be used. For the insulator images taken at the substation site, a mathematical morphology-improved optimal entropic threshold (OET) method is utilized to extract the insulator from the background. By performing feature calculations of insulator images in RGB and HSI color spaces, sixty-six color features are obtained. By fusing the features of the two color spaces using kernel principal component analysis (KPCA), fused features are obtained. The recognition of contamination grades is then accomplished with a deep belief network (DBN) that consists of a three-layered restricted Boltzmann machine. The experimental results of the images taken on-site show that the fused features obtained by the KPCA can fully reflect the contamination state of the insulators. Compared with the identification obtained using RGB or HSI color-space features alone, accuracy is significantly improved, and insulator contamination grades can be effectively identified. The research provides a new method for the accurate, efficient, and non-contact detection of insulator contamination grades.

scholarly journals The Comparison of Fusion Methods for HSRRSI Considering the Effectiveness of Land Cover (Features) Object Recognition Based on Deep Learning

2019 ◽  
Vol 11 (12) ◽  
pp. 1435 ◽  
Author(s):  
Shiran Song ◽  
Jianhua Liu ◽  
Heng Pu ◽  
Yuan Liu ◽  
Jingyan Luo
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Object Recognition ◽  
Land Cover ◽  
Feature Recognition ◽  
Color Space ◽  
Principal Component ◽  
Remote Sensing Images ◽  
Visual Effect ◽  
Fusion Methods

The efficient and accurate application of deep learning in the remote sensing field largely depends on the pre-processing technology of remote sensing images. Particularly, image fusion is the essential way to achieve the complementarity of the panchromatic band and multispectral bands in high spatial resolution remote sensing images. In this paper, we not only pay attention to the visual effect of fused images, but also focus on the subsequent application effectiveness of information extraction and feature recognition based on fused images. Based on the WorldView-3 images of Tongzhou District of Beijing, we apply the fusion results to conduct the experiments of object recognition of typical urban features based on deep learning. Furthermore, we perform a quantitative analysis for the existing pixel-based mainstream fusion methods of IHS (Intensity-Hue Saturation), PCS (Principal Component Substitution), GS (Gram Schmidt), ELS (Ehlers), HPF (High-Pass Filtering), and HCS (Hyper spherical Color Space) from the perspectives of spectrum, geometric features, and recognition accuracy. The results show that there are apparent differences in visual effect and quantitative index among different fusion methods, and the PCS fusion method has the most satisfying comprehensive effectiveness in the object recognition of land cover (features) based on deep learning.

scholarly journals Evaluasi Performasi Ruang Warna pada Klasifikasi Diabetic Retinophaty Menggunakan Convolution Neural Network

2021 ◽  
Vol 8 (3) ◽  
pp. 619
Author(s):  
Candra Dewi ◽  
Andri Santoso ◽  
Indriati Indriati ◽  
Nadia Artha Dewi ◽  
Yoke Kusuma Arbawa
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Color Space ◽  
Training Data ◽  
Color Spaces ◽  
Deep Learning Algorithm ◽  
Optimal Accuracy

<p>Semakin meningkatnya jumlah penderita diabetes menjadi salah satu faktor penyebab semakin tingginya penderita penyakit <em>diabetic retinophaty</em>. Salah satu citra yang digunakan oleh dokter mata untuk mengidentifikasi <em>diabetic retinophaty</em> adalah foto retina. Dalam penelitian ini dilakukan pengenalan penyakit diabetic retinophaty secara otomatis menggunakan citra <em>fundus</em> retina dan algoritme <em>Convolutional Neural Network</em> (CNN) yang merupakan variasi dari algoritme Deep Learning. Kendala yang ditemukan dalam proses pengenalan adalah warna retina yang cenderung merah kekuningan sehingga ruang warna RGB tidak menghasilkan akurasi yang optimal. Oleh karena itu, dalam penelitian ini dilakukan pengujian pada berbagai ruang warna untuk mendapatkan hasil yang lebih baik. Dari hasil uji coba menggunakan 1000 data pada ruang warna RGB, HSI, YUV dan L*a*b* memberikan hasil yang kurang optimal pada data seimbang dimana akurasi terbaik masih dibawah 50%. Namun pada data tidak seimbang menghasilkan akurasi yang cukup tinggi yaitu 83,53% pada ruang warna YUV dengan pengujian pada data latih dan akurasi 74,40% dengan data uji pada semua ruang warna.</p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Increasing the number of people with diabetes is one of the factors causing the high number of people with diabetic retinopathy. One of the images used by ophthalmologists to identify diabetic retinopathy is a retinal photo. In this research, the identification of diabetic retinopathy is done automatically using retinal fundus images and the Convolutional Neural Network (CNN) algorithm, which is a variation of the Deep Learning algorithm. The obstacle found in the recognition process is the color of the retina which tends to be yellowish red so that the RGB color space does not produce optimal accuracy. Therefore, in this research, various color spaces were tested to get better results. From the results of trials using 1000 images data in the color space of RGB, HSI, YUV and L * a * b * give suboptimal results on balanced data where the best accuracy is still below 50%. However, the unbalanced data gives a fairly high accuracy of 83.53% with training data on the YUV color space and 74,40% with testing data on all color spaces.</em></p><p><em><strong><br /></strong></em></p>

Extraction of Tobacco Leaves Color Features Based on HSI Color Space

2014 ◽  
Vol 651-653 ◽  
pp. 2424-2429 ◽  
Author(s):  
Jiang Tao Ji ◽  
Ming Li Deng ◽  
Zhi Tao He ◽  
Shi Tong Jia ◽  
Xin Wu Du ◽  
...  
Keyword(s):  
Statistical Method ◽  
Color Space ◽  
Tobacco Leaves ◽  
Growth Status ◽  
Average Value ◽  
Color Features ◽  
Quality Grade ◽  
Hsi Color Space ◽  
Different Levels ◽  
Color Components

Tobacco is widely planted worldwide as an important economic crop. The differences of planting environment and growth status in different regions lead to different levels of tobacco quality, while color is one of the important indexes to evaluate the quality grade of tobacco leaves. Even for those that are planted in the same area, colors of different grades of tobacco leaves vary greatly, so the color of tobacco leaves is often used as the main evaluating index in tobacco leaves grading. In this paper color extraction of standard tobacco leaves based on HSI color space was thoroughly studied. H, S and I component would be quantified and extracted by using color histogram, then the average value corresponding to every color component was calculated. After extraction and calculation on the same level of standard tobacco leaves, the ranges of three color components could be obtained in HSI color space by using statistical method, and provide data information for tobacco leaves grading.

scholarly journals Empirical Examination of Color Spaces in Deep Convolution Networks

2020 ◽  
Vol 9 (2) ◽  
pp. 1011-1018
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Color Space ◽  
Classical Problem ◽  
Recognition Task ◽  
Test Accuracy ◽  
Color Spaces ◽  
Empirical Examination ◽  
Data Set ◽  
Different Color

In this paper we present an empirical examination of deep convolution neural network (DCNN) performance in different color spaces for the classical problem of image recognition/classification. Most such deep learning architectures or networks are applied on RGB color space image data set, so our objective is to study DCNNs performance in other color spaces. We describe the design of our novel experiment and present results on whether deep learning networks for image recognition task is invariant to color spaces or not. In this study, we have analyzed the performance of 3 popular DCNNs (VGGNet, ResNet, GoogleNet) by providing input images in 5 different color spaces(RGB, normalized RGB, YCbCr, HSV , CIE-Lab) and compared performance in terms of test accuracy, test loss, and validation loss. All these combination of networks and color spaces are investigated on two datasets- CIFAR 10 and LINNAEUS 5. Our experimental results show that CNNs are variant to color spaces as different color spaces have different performance results for image classification task.

scholarly journals Deep Restricted Kernel Machines Using Conjugate Feature Duality

2017 ◽  
Vol 29 (8) ◽  
pp. 2123-2163 ◽  
Author(s):  
Johan A. K. Suykens
Keyword(s):  
Deep Learning ◽  
Least Squares ◽  
Principal Component ◽  
Feedforward Neural Networks ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Kernel Machines ◽  
Continuous Variables ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines

The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

COMPARATIVE ASSESSMENT OF CONTENT-BASED FACE IMAGE RETRIEVAL IN DIFFERENT COLOR SPACES

2005 ◽  
Vol 19 (07) ◽  
pp. 873-893 ◽  
Author(s):  
PEICHUNG SHIH ◽  
CHENGJUN LIU
Keyword(s):  
Face Recognition ◽  
Image Retrieval ◽  
Color Space ◽  
Principal Component ◽  
Face Image ◽  
Color Images ◽  
Grand Challenge ◽  
Color Spaces ◽  
The Face ◽  
Different Color

Content-based face image retrieval is concerned with computer retrieval of face images (of a given subject) based on the geometric or statistical features automatically derived from these images. It is well known that color spaces provide powerful information for image indexing and retrieval by means of color invariants, color histogram, color texture, etc. This paper assesses comparatively the performance of content-based face image retrieval in different color spaces using a standard algorithm, the Principal Component Analysis (PCA), which has become a popular algorithm in the face recognition community. In particular, we comparatively assess 12 color spaces (RGB, HSV, YUV, YCbCr, XYZ, YIQ, L*a*b*, U*V*W*, L*u*v*, I1I2I3, HSI, and rgb) by evaluating seven color configurations for every single color space. A color configuration is defined by an individual or a combination of color component images. Take the RGB color space as an example, possible color configurations are R, G, B, RG, RB, GB and RGB. Experimental results using 600 FERET color images corresponding to 200 subjects and 456 FRGC (Face Recognition Grand Challenge) color images of 152 subjects show that some color configurations, such as YV in the YUV color space and YI in the YIQ color space, help improve face retrieval performance.

scholarly journals Estimation of percentage of impurities in coffee using a computer vision system

2022 ◽  
Vol 26 (2) ◽  
pp. 142-148
Author(s):  
Anderson G. Costa ◽  
Eudócio R. O. da Silva ◽  
Murilo M. de Barros ◽  
Jonatthan A. Fagundes
Keyword(s):  
Linear Models ◽  
Digital Images ◽  
Vision System ◽  
Color Space ◽  
Principal Component Regression ◽  
Principal Component ◽  
Coffee Beans ◽  
Hsi Color Space ◽  
Pcr Method ◽  
Data Group

ABSTRACT The quality and price of coffee drinks can be affected by contamination with impurities during roasting and grinding. Methods that enable quality control of marketed products are important to meet the standards required by consumers and the industry. The purpose of this study was to estimate the percentage of impurities contained in coffee using textural and colorimetric descriptors obtained from digital images. Arabica coffee beans (Coffea arabica L.) at 100% purity were subjected to roasting and grinding processes, and the initially pure ground coffee was gradually contaminated with impurities. Digital images were collected from coffee samples with 0, 10, 30, 50, and 70% impurities. From the images, textural descriptors of the histograms (mean, standard deviation, entropy, uniformity, and third moment) and colorimetric descriptors (RGB color space and HSI color space) were obtained. The principal component regression (PCR) method was applied to the data group of textural and colorimetric descriptors for the development of linear models to estimate coffee impurities. The selected models for the textural descriptors data group and the colorimetric descriptors data group were composed of two and three principal components, respectively. The model from the colorimetric descriptors showed a greater capacity to estimate the percentage of impurities in coffee when compared to the model from the textural descriptors.

scholarly journals An Efficient Color Space for Deep-Learning Based Traffic Light Recognition

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Hyun-Koo Kim ◽  
Ju H. Park ◽  
Ho-Youl Jung
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
High Performance ◽  
Detection System ◽  
Color Space ◽  
Network Models ◽  
Recognition System ◽  
Color Spaces ◽  
Traffic Light ◽  
Light Detection

Traffic light recognition is an essential task for an advanced driving assistance system (ADAS) as well as for autonomous vehicles. Recently, deep-learning has become increasingly popular in vision-based object recognition owing to its high performance of classification. In this study, we investigate how to design a deep-learning based high-performance traffic light detection system. Two main components of the recognition system are investigated: the color space of the input video and the network model of deep learning. We apply six color spaces (RGB, normalized RGB, Ruta’s RYG, YCbCr, HSV, and CIE Lab) and three types of network models (based on the Faster R-CNN and R-FCN models). All combinations of color spaces and network models are implemented and tested on a traffic light dataset with 1280×720 resolution. Our simulations show that the best performance is achieved with the combination of RGB color space and Faster R-CNN model. These results can provide a comprehensive guideline for designing a traffic light detection system.

Color Space Transformation using Neural Networks

2019 ◽  
Vol 2019 (1) ◽  
pp. 153-158
Author(s):  
Lindsay MacDonald
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Color Space ◽  
Reflectance Spectra ◽  
Network Architectures ◽  
Color Spaces ◽  
Natural Materials ◽  
Space Transformation ◽  
Color Space Transformation

We investigated how well a multilayer neural network could implement the mapping between two trichromatic color spaces, specifically from camera R,G,B to tristimulus X,Y,Z. For training the network, a set of 800,000 synthetic reflectance spectra was generated. For testing the network, a set of 8,714 real reflectance spectra was collated from instrumental measurements on textiles, paints and natural materials. Various network architectures were tested, with both linear and sigmoidal activations. Results show that over 85% of all test samples had color errors of less than 1.0 ΔE2000 units, much more accurate than could be achieved by regression.

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