scholarly journals Coal quality prediction based on multi-feature fusion of flotation foam images

2020 ◽  
Author(s):  
Yateng Bai ◽  
Xiaoping Ma
Keyword(s):  
Real Time ◽  
Feature Fusion ◽  
Image Features ◽  
Image Feature ◽  
Coal Preparation ◽  
Coal Quality ◽  
Coal Flotation ◽  
Edge Operator ◽  
Occurrence Matrix

Abstract Coal flotation monitoring cannot provide real-time feedback on the yield and ash of coal preparation products because it is influenced by the subjective nature of artificial judgment of coal preparation status and the lag of product quality testing of coal preparation. This paper aims to extract the texture, colour and shape features of floating foam images using various image processing methods, such as colour space, wavelet transform, greyscale co-occurrence matrix and edge operator, and to quantify the characterisation of various characteristic parameters on the basis of the indicative effect of floating foam characteristics on the quality of coal preparation products. The correlation between image features and the yield and ash of flotation products is studied, and a regression prediction model of coal preparation yield and ash was established by combining various image feature parameters using machine learning methods. Experimental results show that the proposed method can realise the real-time monitoring of coal mine flotation and effectively predict coal quality.

scholarly journals Coal quality prediction based on multi-feature fusion of flotation foam images

2020 ◽  
Author(s):  
yateng bai ◽  
xiaoping ma
Keyword(s):  
Real Time ◽  
Feature Fusion ◽  
Image Features ◽  
Image Feature ◽  
Coal Preparation ◽  
Coal Quality ◽  
Coal Flotation ◽  
Edge Operator ◽  
Occurrence Matrix

Abstract Coal flotation monitoring cannot provide real-time feedback on the yield and ash of coal preparation products because it is influenced by the subjective nature of artificial judgment of coal preparation status and the lag of product quality testing of coal preparation. This paper aims to extract the texture, colour and shape features of floating foam images using various image processing methods, such as colour space, wavelet transform, greyscale co-occurrence matrix and edge operator, and to quantify the characterisation of various characteristic parameters on the basis of the indicative effect of floating foam characteristics on the quality of coal preparation products. The correlation between image features and the yield and ash of flotation products is studied, and a regression prediction model of coal preparation yield and ash was established by combining various image feature parameters using machine learning methods. Experimental results show that the proposed method can realise the real-time monitoring of coal mine flotation and effectively predict coal quality.

scholarly journals Impacts of stochastic models on real-time 3D UAV mapping

2014 ◽  
Vol XL-1 ◽  
pp. 35-44
Author(s):  
M. Alqurashi ◽  
J. Wang
Keyword(s):  
Stochastic Model ◽  
Real Time ◽  
Stochastic Models ◽  
Feature Matching ◽  
Critical Role ◽  
Image Feature ◽  
Mapping Process ◽  
Camera Orientation ◽  
Camera Position

In UAV mapping using direct geo-referencing, the formation of stochastic model generally takes into the account the different types of measurements required to estimate the 3D coordinates of the feature points. Such measurements include image tie point coordinate measurements, camera position measurements and camera orientation measurements. In the commonly used stochastic model, it is commonly assumed that all tie point measurements have the same variance. In fact, these assumptions are not always realistic and thus, can lead to biased 3D feature coordinates. Tie point measurements for different image feature objects may not have the same accuracy due to the facts that the geometric distribution of features, particularly their feature matching conditions are different. More importantly, the accuracies of the geo-referencing measurements should also be considered into the mapping process. In this paper, impacts of typical stochastic models on the UAV mapping are investigated. It has been demonstrated that the quality of the geo-referencing measurements plays a critical role in real-time UAV mapping scenarios.

scholarly journals Robotic Ultrasound Scanning With Real-Time Image-Based Force Adjustment: Quick Response for Enabling Physical Distancing During the COVID-19 Pandemic

2021 ◽  
Vol 8 ◽  
Author(s):  
Mojtaba Akbari ◽  
Jay Carriere ◽  
Tyler Meyer ◽  
Ron Sloboda ◽  
Siraj Husain ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Real Time ◽  
Close Contact ◽  
Image Features ◽  
Svm Classifier ◽  
Quick Response ◽  
Robot Arm ◽  
Noise Characteristics ◽  
The Us

During an ultrasound (US) scan, the sonographer is in close contact with the patient, which puts them at risk of COVID-19 transmission. In this paper, we propose a robot-assisted system that automatically scans tissue, increasing sonographer/patient distance and decreasing contact duration between them. This method is developed as a quick response to the COVID-19 pandemic. It considers the preferences of the sonographers in terms of how US scanning is done and can be trained quickly for different applications. Our proposed system automatically scans the tissue using a dexterous robot arm that holds US probe. The system assesses the quality of the acquired US images in real-time. This US image feedback will be used to automatically adjust the US probe contact force based on the quality of the image frame. The quality assessment algorithm is based on three US image features: correlation, compression and noise characteristics. These US image features are input to the SVM classifier, and the robot arm will adjust the US scanning force based on the SVM output. The proposed system enables the sonographer to maintain a distance from the patient because the sonographer does not have to be holding the probe and pressing against the patient's body for any prolonged time. The SVM was trained using bovine and porcine biological tissue, the system was then tested experimentally on plastisol phantom tissue. The result of the experiments shows us that our proposed quality assessment algorithm successfully maintains US image quality and is fast enough for use in a robotic control loop.

An Explainable Framework for Diagnosis of COVID-19 Pneumonia via Transfer Learning and Discriminant Correlation Analysis

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-16
Author(s):  
Siyuan Lu ◽  
Di Wu ◽  
Zheng Zhang ◽  
Shui-Hua Wang
Keyword(s):  
Correlation Analysis ◽  
Feature Fusion ◽  
Classification Performance ◽  
Image Features ◽  
Image Feature ◽  
Functional Link ◽  
Image Representations ◽  
Learning Machine ◽  
Lung Window ◽  
Fold Cross Validation

The new coronavirus COVID-19 has been spreading all over the world in the last six months, and the death toll is still rising. The accurate diagnosis of COVID-19 is an emergent task as to stop the spreading of the virus. In this paper, we proposed to leverage image feature fusion for the diagnosis of COVID-19 in lung window computed tomography (CT). Initially, ResNet-18 and ResNet-50 were selected as the backbone deep networks to generate corresponding image representations from the CT images. Second, the representative information extracted from the two networks was fused by discriminant correlation analysis to obtain refined image features. Third, three randomized neural networks (RNNs): extreme learning machine, Schmidt neural network and random vector functional-link net, were trained using the refined features, and the predictions of the three RNNs were ensembled to get a more robust classification performance. Experiment results based on five-fold cross validation suggested that our method outperformed state-of-the-art algorithms in the diagnosis of COVID-19.

scholarly journals Key Frame Extraction of Multi-Resolution Remote Sensing Images Under Quality Constraint

Open Physics ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 871-878
Author(s):  
Yijun Liu ◽  
Ziwen Zhang ◽  
Feng Li
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Image Features ◽  
Image Feature ◽  
Cluster Center ◽  
Remote Sensing Images ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Quality Constraint

Abstract In key frame extraction of multi-resolution remote sensing image using traditional key frame image feature extraction method, only the feature information of remote sensing images, rather than cluster operation of the remote sensing images is considered, which leads to low efficiency and poor quality of extraction results. To this end, the key frame extraction algorithm of multi-resolution remote sensing image under quality constraint was proposed. Through similarity between image features and the selected image frame, rough key frame can be extracted. On this basis, the key frame extraction of multi resolution remote sensing image based on quality constraints was used to perform clustering operation for multi-resolution remote sensing image corresponding to rough key frame, which shortened the time length for retrieval of key frame image. According to the clustering results, multi-resolution remote sensing images were divided into several clusters. The key frame of each cluster can be obtained by calculating the distance between remote sensing image and cluster center. For key frames that had been determined, their quality was evaluated to meet standard, so as to realize effective extraction of key frame of multi-resolution remote sensing images. The experimental results show that the proposed method can significantly improve the quality of key frame extraction of multi-resolution remote sensing images.

Image Color Transfer Approach by Analogy with Taylor Expansion

2013 ◽  
Vol 2 (2) ◽  
pp. 43-54
Author(s):  
Hongbo Liu ◽  
Ye Ji ◽  
Aboul Ella Hassanien
Keyword(s):  
Time Series ◽  
Taylor Expansion ◽  
Image Features ◽  
Second Order ◽  
Image Feature ◽  
Order Information ◽  
Color Transfer ◽  
First Order ◽  
Occurrence Matrix ◽  
Image Expansion

The Taylor expansion has shown in many fields to be an extremely powerful tool. In this paper, the authors investigated image features and their relationships by analogy with Taylor expansion. The kind of expansion could be helpful for analyzing image feature and engraftment, such as transferring color between images. By analogy with Taylor expansion, the image color transfer algorithm is designed by the first and second-order information. The luminance histogram represents the first-order information of image, and the co-occurrence matrix represents the second-order information of image. Some results illustrate the proposed algorithm is effective. In this study, each polynomial in the Taylor analogy expansion of images is considered as one of image features which help in re-understanding images and its features. By using the proposed technique, the features of image, such as color, texture, dimension, time series, would be not isolated but mutual relational based on image expansion.

scholarly journals Sensitivity of Image Features to Noise in Conventional and Respiratory-Gated PET/CT Images of Lung Cancer: Uncorrelated Noise Effects

2016 ◽  
Vol 16 (5) ◽  
pp. 595-608 ◽  
Author(s):  
Jasmine A. Oliver ◽  
Mikalai Budzevich ◽  
Dylan Hunt ◽  
Eduardo G. Moros ◽  
Kujtim Latifi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Positron Emission Tomography ◽  
Image Features ◽  
Image Feature ◽  
Emission Tomography ◽  
Gray Level ◽  
Uncorrelated Noise ◽  
Computed Tomography Images ◽  
Positron Emission ◽  
Occurrence Matrix

The effect of noise on image features has yet to be studied in depth. Our objective was to explore how significantly image features are affected by the addition of uncorrelated noise to an image. The signal-to-noise ratio and noise power spectrum were calculated for a positron emission tomography/computed tomography scanner using a Ge-68 phantom. The conventional and respiratory-gated positron emission tomography/computed tomography images of 31 patients with lung cancer were retrospectively examined. Multiple sets of noise images were created for each original image by adding Gaussian noise of varying standard deviation equal to 2.5%, 4.0%, and 6.0% of the maximum intensity for positron emission tomography images and 10, 20, 50, 80, and 120 Hounsfield units for computed tomography images. Image features were extracted from all images, and percentage differences between the original image and the noise image feature values were calculated. These features were then categorized according to the noise sensitivity. The contour-dependent shape descriptors averaged below 4% difference in positron emission tomography and below 13% difference in computed tomography between noise and original images. Gray level size zone matrix features were the most sensitive to uncorrelated noise exhibiting average differences >200% for conventional and respiratory-gated images in computed tomography and 90% in positron emission tomography. Image feature differences increased as the noise level increased for shape, intensity, and gray-level co-occurrence matrix features in positron emission tomography and for gray-level co-occurrence matrix and gray-level size zone matrix features in conventional computed tomography. Investigators should be aware of the noise effects on image features.

Extraction of Image Features on Tooth Flank Pitting and Gluing

2013 ◽  
Vol 760-762 ◽  
pp. 1505-1509
Author(s):  
Peng Fei Cheng ◽  
Guang Hua Nie
Keyword(s):  
Gear Tooth ◽  
Morphological Characteristics ◽  
Image Features ◽  
Image Feature ◽  
Tooth Surface ◽  
Gray Level ◽  
Median Filtering ◽  
Image Feature Extraction ◽  
Occurrence Matrix ◽  
Tooth Flank

Tooth flank pitting and gluing are principal forms of gear defect. The purpose of this research is to extract the image feature of the gear in the different defects by means of image processing technology. Firstly, the image was carried out denoising processing by median filtering and segmentation processing by use of OSTU method. Then, the pixel area was extracted as a feature to distinguish normal gear, tooth surface pitting and gluing, the inertia was extracted as image feature to detect pitting and gluing by Gray level co-occurrence matrix, and the morphological characteristics of the image were extracted. Image feature extraction of different defect form will help to establish an effective image recognition model.

High-resolution electron microscopy of the atomic structure of some grain boundaries in Au

1986 ◽  
Vol 44 ◽  
pp. 414-415
Author(s):  
W. Krakow ◽  
D. A. Smith
Keyword(s):  
Atomic Structure ◽  
High Resolution Electron Microscopy ◽  
Image Features ◽  
Image Feature ◽  
Resolution Electron ◽  
Tilt Boundaries ◽  
Processing Techniques ◽  
Atom Position ◽  
Structure Configuration

The successful determination of the atomic structure of [110] tilt boundaries in Au stems from the investigation of microscope performance at intermediate accelerating voltages (200 and 400kV) as well as a detailed understanding of how grain boundary image features depend on dynamical diffraction processes variation with specimen and beam orientations. This success is also facilitated by improving image quality by digital image processing techniques to the point where a structure image is obtained and each atom position is represented by a resolved image feature. Figure 1 shows an example of a low angle (∼10°) Σ = 129/[110] tilt boundary in a ∼250Å Au film, taken under tilted beam brightfield imaging conditions, to illustrate the steps necessary to obtain the atomic structure configuration from the image. The original image of Fig. 1a shows the regular arrangement of strain-field images associated with the cores of ½ [10] primary dislocations which are separated by ∼15Å.

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