scholarly journals A Generic Adaptive Fractal Filtering Algorithm for Identifying Work Piece Defects on Multiple Surfaces

2018 ◽  
Author(s):  
Liang Gong ◽  
Chenhui Lin ◽  
Zhuang Mo ◽  
Xiaoye Shen ◽  
Ke Lin ◽  
...  
Keyword(s):  
Image Processing ◽  
Fractal Dimension ◽  
Surface Defects ◽  
Multiple Scales ◽  
Object Identification ◽  
Image Features ◽  
Work Piece ◽  
Workpiece Surface ◽  
Filtering Algorithm ◽  
Fractal Filtering

In addition to image filtering in the spatial and frequency domains, fractal characteristics induced algorithms offers considerable flexibility in the design and implementations of image processing solutions in areas such as image enhancement, image restoration, image data compression and spectrum of applications of practical interests. Facing up to a real-world problem of identifying workpiece surface defects, a generic adaptive fractal filtering algorithm is proposed, which shows advantages on the problems of target recognition, feature extraction and image denoising at multiple scales. First, we reveal the physical principles underlying between signal SNR and its representative fractal dimension indicative parameters, validating that the fractal dimension can be used to adaptively obtain the image features. Second, an adaptive fractal filtering algorithm (Abbreviated as AFFA) is proposed according to the identified correlation between the image fractal dimensions and the scales of objects, and it is verified by a benchmarking image processing case study. Third, by using the proposed fractal filtering algorithm, surface defects on a flange workpiece are identified. Compared to conventional image processing algorithms, the proposed algorithm shows superior computing simplicity and better performance Numerical analysis and engineering case studies show that the fractal dimension is eligible for deriving an adaptive filtering algorithm for diverse-scale object identification, and the proposed AFFA is feasible for general application in workpiece surface defect detection. 

Analysis and Study of the Steel Plate Surface Defects Image Processing and the Fractal Dimension Characteristics

2012 ◽  
Vol 482-484 ◽  
pp. 1773-1776
Author(s):  
Xuan Wang ◽  
Wei Liu ◽  
Hui Cao ◽  
Dong Ping Ma
Keyword(s):  
Image Processing ◽  
Fractal Dimension ◽  
Surface Defect ◽  
Surface Defects ◽  
Steel Plate ◽  
Image Feature ◽  
Plate Surface ◽  
Surface Image ◽  
Surface Defect Detection ◽  
Image Pattern

Steel surface defect detection is the key point of this research. The paper mainly focuses on the image processing and image feature extraction of the steel plate surface. The paper also focuses on the calculating procedure and results of the fractal dimension in different defects images. It can be concluded from the results of the study, fractal dimension of the defect images becomes an important feature of the steel plate surface image pattern recognition.

scholarly journals Quantum Dilation and Erosion

2020 ◽  
Vol 10 (11) ◽  
pp. 4040 ◽  
Author(s):  
Shi-Yuan Ma ◽  
Ashraf Khalil ◽  
Hassan Hajjdiab ◽  
Hichem Eleuch
Keyword(s):  
Image Processing ◽  
Quantum Algorithms ◽  
Quantum Circuit ◽  
Object Identification ◽  
Image Features ◽  
Imaging Processing ◽  
Other Information ◽  
Potential Applications ◽  
Classical Image ◽  
Quantum Operators

The dilation and erosion operations are the first fundamental step in classical image processing. They are important in many image processing algorithms to extract basic image features, such as geometric shapes; such shapes are then fed to higher level algorithms for object identification and recognition. In this paper, we present an improved quantum method to realize dilation and erosion in imaging processing. Unlike in the classical way, in the quantum version of imaging processing, all of the information is stored in quantum bits (qubits). We use qubits to code the location and other information of each pixel of the images and apply quantum operators (or quantum gates) to accomplish specific functions. Because of quantum entanglement and other nonintuitive features in quantum mechanics, qubits have many advantages over classical bits, but their nature presents challenges in designing quantum algorithms. We first built the quantum circuit theoretically, and then ran it on the IBM Quantum Experience platform to test and process real images. With this algorithm, we are looking forward to more potential applications in quantum computation.

Research on Tool Wear Based on Texture Fractal Dimension

2011 ◽  
Vol 66-68 ◽  
pp. 1163-1166
Author(s):  
Mao Jun Chen ◽  
Zhong Jin Ni ◽  
Liang Fang
Keyword(s):  
Fractal Dimension ◽  
Tool Wear ◽  
Surface Texture ◽  
Manufacturing Systems ◽  
Experimental Results ◽  
Machining Process ◽  
Work Piece ◽  
Automated Manufacturing ◽  
Automated Manufacturing Systems ◽  
Wear Condition

In automated manufacturing systems, one of the most important issues is the detection of tool wear during the machining process. The Hausdorff-Besicovitch (HB) dimension is used to analyze the feature of the surface texture of work-piece in this paper. The value of the fractal dimension of the work-piece surface texture tends to decrease with the machining process, due to the texture becoming more complex and irregular, and the tool wear is also becoming more and more serious. That can describe the inherent relationship between work-piece surface texture and tool wear. The experimental results demonstrate the probability of using the fractal dimension of work-piece surface texture to monitor the tool wear condition.

scholarly journals Depth in convolutional neural networks solves scene segmentation

2019 ◽  
Author(s):  
N Seijdel ◽  
N Tsakmakidis ◽  
EHF De Haan ◽  
SM Bohte ◽  
HS Scholte
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Visual Processing ◽  
Human Performance ◽  
Object Identification ◽  
Image Features ◽  
Background Information ◽  
Natural Scenes ◽  
Scene Segmentation ◽  
Deep Convolutional Neural Networks

AbstractFeedforward deep convolutional neural networks (DCNNs) are, under specific conditions, matching and even surpassing human performance in object recognition in natural scenes. This performance suggests that the analysis of a loose collection of image features could support the recognition of natural object categories, without dedicated systems to solve specific visual subtasks. Research in humans however suggests that while feedforward activity may suffice for sparse scenes with isolated objects, additional visual operations (‘routines’) that aid the recognition process (e.g. segmentation or grouping) are needed for more complex scenes. Linking human visual processing to performance of DCNNs with increasing depth, we here explored if, how, and when object information is differentiated from the backgrounds they appear on. To this end, we controlled the information in both objects and backgrounds, as well as the relationship between them by adding noise, manipulating background congruence and systematically occluding parts of the image. Results indicate that with an increase in network depth, there is an increase in the distinction between object- and background information. For more shallow networks, results indicated a benefit of training on segmented objects. Overall, these results indicate that, de facto, scene segmentation can be performed by a network of sufficient depth. We conclude that the human brain could perform scene segmentation in the context of object identification without an explicit mechanism, by selecting or “binding” features that belong to the object and ignoring other features, in a manner similar to a very deep convolutional neural network.

scholarly journals Automatic Detection of Surface Defects in Industrial Materials Based on Image Processing

2018 ◽  
Vol 7 (3.34) ◽  
pp. 61
Author(s):  
R Srividhya ◽  
K Shanmugapriya ◽  
K Sindhu Priya
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Surface Defects ◽  
Processing Method ◽  
Automated Detection ◽  
Image Construction ◽  
Good Classification ◽  
Industrial Materials ◽  
Manual Calculation ◽  
Visible Defect

In the field of industry, corrosion and defects are amongst the most frequent operations. Industrial Materials have periodic defects that are difficult to detect during production even by experienced human inspectors. Defects are difficult to detect during production even by experienced human inspectors. Usually, the colour transfer process contains an image segmentation phase and an image construction phase. Therefore, we introduce an image processing method for automatically detecting the defects in surfaces. We show how barely visible defect can be optically enhanced to improve annual assessment as well as how descriptor-based image processing and machine learning can be used to allow automated detection. Image enhancement is performed by applying manual calculation. We implement this simulation using MATLAB R2013a. Results show that the proposed allows training both tested classifiers with good classification rates around 98.9%.  

scholarly journals Application of a Fractional Grey Prediction Model Based on a Filtering Algorithm in Image Processing

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Mingyu Tong ◽  
Kailiang Shao ◽  
Xilin Luo ◽  
Huiming Duan
Keyword(s):  
Image Processing ◽  
Prediction Model ◽  
Grey System Theory ◽  
Grey Prediction ◽  
Grey System ◽  
Middle Point ◽  
Filtering Algorithm ◽  
Grey Correlation ◽  
Grey Prediction Model ◽  
Model Based

Image filtering can change or enhance an image by emphasizing or removing certain features of the image. An image is a system in which some information is known and some information is unknown. Grey system theory is an important method for dealing with this kind of system, and grey correlation analysis and grey prediction modeling are important components of this method. In this paper, a fractional grey prediction model based on a filtering algorithm by combining a grey correlation model and a fractional prediction model is proposed. In this model, first, noise points are identified by comparing the grey correlation and the threshold value of each pixel in the filter window, and then, through the resolution coefficient of the important factor in image processing, a variety of grey correlation methods are compared. Second, the image noise points are used as the original sequence by the filter pane. The grey level of the middle point is predicted by the values of the surrounding pixel points combined with the fractional prediction model, replacing the original noise value to effectively eliminate the noise. Finally, an empirical analysis shows that the PSNR and MSE of the new model are approximately 27 and 140, respectively; these values are better than those of the comparison models and achieve good processing effects.

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