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.

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.

A parameterized numerical method for generating discrete helical gear tooth surface allowing non-standard geometry

2008 ◽  
Vol 222 (6) ◽  
pp. 1033-1038 ◽  
Author(s):  
J Hedlund ◽  
A Lehtovaara
Keyword(s):  
Gear Tooth ◽  
Numerical Approach ◽  
Helical Gear ◽  
Tooth Surface ◽  
Tool Profile ◽  
Standard Geometry ◽  
Gear Manufacturing ◽  
Involute Gears ◽  
Gear Geometry ◽  
Tooth Flank

Gear analysis is typically performed using calculation based on gear standards. Standards provide a good basis in gear geometry calculation for involute gears, but these are unsatisfactory for handling geometry deviations such as tooth flank modifications. The efficient utilization of finite-element calculation also requires the geometry generation to be parameterized. A parameterized numerical approach was developed to create discrete helical gear geometry and contact line by simulating the gear manufacturing, i.e. the hobbing process. This method is based on coordinate transformations and a wide set of numerical calculation points and their synchronization, which permits deviations from common involute geometry. As an example, the model is applied to protuberance tool profile and grinding with tip relief. A fairly low number of calculation points are needed to create tooth flank profiles where error is <1 μm.

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.

An Improved FastICA Algorithm and its Application in Image Feature Extraction

2011 ◽  
Vol 204-210 ◽  
pp. 1485-1489
Author(s):  
Li Juan Chen ◽  
Xiang Jun Zou ◽  
Bing Bing Chen ◽  
Yan Chen ◽  
Jing Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Independent Component Analysis ◽  
Initial Point ◽  
Image Features ◽  
Image Feature ◽  
Higher Order Statistics ◽  
Image Feature Extraction ◽  
Fastica Algorithm ◽  
Litchi Fruit ◽  
Selection Of

There are most important features hiding in the higher-order statistics information of the images. They are extracted by classical fast-fixed independent component analysis (FastICA algorithm) which requires a large amount of calculation and it is sensitive on the selection of initial point. To overcome the two shortcomings, an improved FastICA algorithm is proposed and mathematical models are constructed. And they are applied to obtain the basic vectors from the images. Finally, take litchi fruit image in natural environment as an instance and experiment with Matlab software. The results show that there are less computation and stronger stability of the improved FashICA algorithm used to extract image features.

Co-Occurrence Matrix-Based Statistical Model for Texture Analysis from Images

2011 ◽  
Vol 103 ◽  
pp. 717-724
Author(s):  
Hossain Shahera ◽  
Serikawa Seiichi
Keyword(s):  
Texture Analysis ◽  
Vision System ◽  
Ground Truth ◽  
Image Features ◽  
Image Texture ◽  
Gray Level ◽  
Machine Vision System ◽  
Statistical Features ◽  
Texture Surface ◽  
Occurrence Matrix

Texture surface analysis is very important for machine vision system. We explore Gray Level Co-occurrence Matrix-based 2ndorder statistical features to understand image texture surface. We employed several features on our ground-truth dataset to understand its nature; and later employed it in a building dataset. Based on our experimental results, we can conclude that these image features can be useful for texture analysis and related fields.

Image Fusion Based on Adaptive Coefficient Gauss Wavelet

2014 ◽  
Vol 1049-1050 ◽  
pp. 1641-1644
Author(s):  
Yong Li ◽  
Zhuang Miao
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Prior Information ◽  
Image Features ◽  
Gray Level ◽  
Fusion Algorithm ◽  
Mr Image ◽  
Occurrence Matrix ◽  
Wavelet Algorithm ◽  
Valid Information

According to CT and MR image features, we presented the medical image fusion algorithm based on adaptive Gaussian wavelet, which uses gray level co-occurrence matrix to modify space coefficients adaptively. Compared with other wavelet algorithm by simulation, the results proved that the proposed algorithm obtained valid information in a highly automated manner, and without human intervention or prior information.

Prediction of wind turbine gear micropitting under variable load and speed conditions using ISO/TR 15144-1: 2010

2012 ◽  
Vol 227 (9) ◽  
pp. 1898-1914 ◽  
Author(s):  
Issa S Al-Tubi ◽  
Hui Long
Keyword(s):  
Film Thickness ◽  
Wind Turbine ◽  
Rotational Speed ◽  
Gear Tooth ◽  
Lubricant Film ◽  
Tooth Surface ◽  
Variable Load ◽  
Lubricant Film Thickness ◽  
Wind Turbine Gearbox ◽  
Tooth Flank

Wind turbine gearbox operates under a wide array of highly fluctuating and dynamic load conditions caused by the stochastic nature of wind and operational wind turbine controls. Micropitting damage is one of failure modes commonly observed in wind turbine gearboxes. This article investigates gear micropitting of high-speed stage gears of a wind turbine gearbox operating under nominal and varying load and speed conditions. Based on the ISO standard of gear micropitting (ISO/TR 15144-1:2010) and considering the operating load and speed conditions, a theoretical study is carried out to assess the risk of gear micropitting by determining the contact stress, sliding parameter, local contact temperature and lubricant film thickness along the line of action of gear tooth contact. The non-uniform distributions of temperature and lubricant film thickness over the tooth flank are observed due to the conditions of torque and rotational speed variations and sliding contact along the gear tooth flanks. The lubricant film thickness varies along the tooth flank and is at the lowest when the tip of the driving gear engages with the root of the driven gear. The lubricant film thickness increases with the increase of rotational speed and decreases as torque and sliding increase. It can be concluded that micropitting is most likely to initiate at the addendum of driving gear and the dedendum of driven gear. The lowest film thickness occurs when the torque is high and the rotational speed is at the lowest which may cause direct tooth surface contact. At the low-torque condition, the varying rotational speed condition may cause a considerable variation of lubricant film thickness thus interrupting the lubrication which may result in micropitting.

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