scholarly journals Machine Learning Guided 3D Image Recognition for Carbonate Pore and Mineral Volumes Determination

2021 ◽  
Author(s):  
Omar Alfarisi ◽  
Aikifa Raza ◽  
Hongtao Zhang ◽  
Mohamed Sassi ◽  
TieJun Zhang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Image Recognition ◽  
Reference Values ◽  
Carbonate Rock ◽  
Image Resolution ◽  
Blind Test ◽  
Thin Sections ◽  
Clastic Rocks

<p>Automated image processing algorithms can improve the quality, efficiency, and consistency of classifying the morphology of heterogeneous carbonate rock and can deal with a massive amount of data and images seamlessly. Geoscientists and petroleum engineers face difficulties in setting the direction of the optimum method for determining petrophysical properties from core plug images of optical thin-sections, Micro-Computed Tomography (μCT), or Magnetic Resonance Imaging (MRI). Most of the successful work is from the homogeneous and clastic rocks focusing on 2D images with less focus on 3D and requiring numerical simulation. Currently, image analysis methods converge to three approaches: image processing, artificial intelligence, and combined image processing with artificial intelligence. In this work, we propose two methods to determine the porosity from 3D μCT and MRI images: an image processing method with Image Resolution Optimized Gaussian Algorithm (IROGA); advanced image recognition method enabled by Machine Learning Difference of Gaussian Random Forest (MLDGRF).</p><p>Meanwhile, we have built reference 3D micro models and collected images for calibration of the IROGA and MLDGRF methods. To evaluate the predictive capability of these calibrated approaches, we ran them on 3D μCT and MRI images of natural heterogeneous carbonate rock. We also measured the porosity and lithology of the carbonate rock using three and two industry-standard ways, respectively, as reference values. Notably, IROGA and MLDGRF have produced porosity results with an accuracy of 96.2% and 97.1% on the training set and 91.7% and 94.4% on blind test validation, respectively, in comparison with the three experimental measurements. We measured limestone and pyrite reference values using two methods, X-ray powder diffraction, and grain density measurements. MLDGRF has produced lithology (limestone and pyrite) volume fractions with an accuracy of 97.7% in comparison to reference measurements.</p>

scholarly journals Machine Learning Guided 3D Image Recognition for Carbonate Pore and Mineral Volumes Determination

2021 ◽  
Author(s):  
Omar Alfarisi ◽  
Aikifa Raza ◽  
Hongtao Zhang ◽  
Mohamed Sassi ◽  
TieJun Zhang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Image Recognition ◽  
Reference Values ◽  
Carbonate Rock ◽  
Image Resolution ◽  
Blind Test ◽  
Thin Sections ◽  
Clastic Rocks

<p>Automated image processing algorithms can improve the quality, efficiency, and consistency of classifying the morphology of heterogeneous carbonate rock and can deal with a massive amount of data and images seamlessly. Geoscientists and petroleum engineers face difficulties in setting the direction of the optimum method for determining petrophysical properties from core plug images of optical thin-sections, Micro-Computed Tomography (μCT), or Magnetic Resonance Imaging (MRI). Most of the successful work is from the homogeneous and clastic rocks focusing on 2D images with less focus on 3D and requiring numerical simulation. Currently, image analysis methods converge to three approaches: image processing, artificial intelligence, and combined image processing with artificial intelligence. In this work, we propose two methods to determine the porosity from 3D μCT and MRI images: an image processing method with Image Resolution Optimized Gaussian Algorithm (IROGA); advanced image recognition method enabled by Machine Learning Difference of Gaussian Random Forest (MLDGRF).</p><p>Meanwhile, we have built reference 3D micro models and collected images for calibration of the IROGA and MLDGRF methods. To evaluate the predictive capability of these calibrated approaches, we ran them on 3D μCT and MRI images of natural heterogeneous carbonate rock. We also measured the porosity and lithology of the carbonate rock using three and two industry-standard ways, respectively, as reference values. Notably, IROGA and MLDGRF have produced porosity results with an accuracy of 96.2% and 97.1% on the training set and 91.7% and 94.4% on blind test validation, respectively, in comparison with the three experimental measurements. We measured limestone and pyrite reference values using two methods, X-ray powder diffraction, and grain density measurements. MLDGRF has produced lithology (limestone and pyrite) volume fractions with an accuracy of 97.7% in comparison to reference measurements.</p>

scholarly journals Research on the Method of Machine Learning Algorithms in Ecological Construction

2021 ◽  
Vol 2136 (1) ◽  
pp. 012061
Author(s):  
Binjie Xia
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Support Vector Machine ◽  
Deep Learning ◽  
Image Recognition ◽  
Rapid Development ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Ecological Construction

Abstract In the rapid development of modern artificial intelligence, for the development of ecological construction, how to rationally use machine learning to promote the development of agricultural economy has become a focus of practice and scientific research. This paper takes ecological image recognition as an example to analyze how to use support vector machine in image processing technology and machine learning in deep learning to conduct in-depth research, and to optimize and improve the algorithm to build an ecological image recognition model.

scholarly journals Interaction between Model Based Signal and Image Processing, Machine Learning and Artificial Intelligence

Proceedings ◽  
2019 ◽  
Vol 33 (1) ◽  
pp. 16
Author(s):  
Ali Mohammad-Djafari
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Bayesian Inference ◽  
Missing Values ◽  
General Purpose ◽  
Parametric Models ◽  
Great Success ◽  
Iterative Regularization ◽  
Model Based

Signale and image processing has always been the main tools in many area and in particular in Medical and Biomedical applications. Nowadays, there are great number of toolboxes, general purpose and very specialized, in which classical techniques are implemented and can be used: all the transformation based methods (Fourier, Wavelets, ...) as well as model based and iterative regularization methods. Statistical methods have also shown their success in some area when parametric models are available. Bayesian inference based methods had great success, in particular, when the data are noisy, uncertain, incomplete (missing values) or with outliers and where there is a need to quantify uncertainties. In some applications, nowadays, we have more and more data. To use these “Big Data” to extract more knowledge, the Machine Learning and Artificial Intelligence tools have shown success and became mandatory. However, even if in many domains of Machine Learning such as classification and clustering these methods have shown success, their use in real scientific problems are limited. The main reasons are twofold: First, the users of these tools cannot explain the reasons when the are successful and when they are not. The second is that, in general, these tools can not quantify the remaining uncertainties. Model based and Bayesian inference approach have been very successful in linear inverse problems. However, adjusting the hyper parameters is complex and the cost of the computation is high. The Convolutional Neural Networks (CNN) and Deep Learning (DL) tools can be useful for pushing farther these limits. At the other side, the Model based methods can be helpful for the selection of the structure of CNN and DL which are crucial in ML success. In this work, I first provide an overview and then a survey of the aforementioned methods and explore the possible interactions between them.

Recent Advances in Medical Image Processing

2020 ◽  
pp. 1-14
Author(s):  
Zhen Huang ◽  
Qiang Li ◽  
Ju Lu ◽  
Junlin Feng ◽  
Jiajia Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Image Processing ◽  
Convolution Neural Network ◽  
Artificial Intelligence Technology ◽  
Deep Convolution Neural Network

<b><i>Background:</i></b> Application and development of the artificial intelligence technology have generated a profound impact in the field of medical imaging. It helps medical personnel to make an early and more accurate diagnosis. Recently, the deep convolution neural network is emerging as a principal machine learning method in computer vision and has received significant attention in medical imaging. <b><i>Key Message:</i></b> In this paper, we will review recent advances in artificial intelligence, machine learning, and deep convolution neural network, focusing on their applications in medical image processing. To illustrate with a concrete example, we discuss in detail the architecture of a convolution neural network through visualization to help understand its internal working mechanism. <b><i>Summary:</i></b> This review discusses several open questions, current trends, and critical challenges faced by medical image processing and artificial intelligence technology.

Machine Learning for 3D Image Recognition to Determine Porosity and Lithology of Heterogeneous Carbonate Rock

2019 ◽  
Author(s):  
Omar Al-Farisi ◽  
Hongtao Zhang ◽  
Aikifa Raza ◽  
Djamel Ozzane ◽  
Mohamed Sassi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Recognition ◽  
Carbonate Rock ◽  
3D Image

scholarly journals Towards Autonomous Driving Using Vision Based Intelligent Systems

2021 ◽  
Vol 8 (2) ◽  
pp. 1-2
Author(s):  
Julkar Nine
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Intelligent Systems ◽  
Autonomous Driving ◽  
Current Generation ◽  
Transfer Data ◽  
Safety Features ◽  
Artificial Intelligence Systems

Vision Based systems have become an integral part when it comes to autonomous driving. The autonomous industry has seen a made large progress in the perception of environment as a result of the improvements done towards vision based systems. As the industry moves up the ladder of automation, safety features are coming more and more into the focus. Different safety measurements have to be taken into consideration based on different driving situations. One of the major concerns of the highest level of autonomy is to obtain the ability of understanding both internal and external situations. Most of the research made on vision based systems are focused on image processing and artificial intelligence systems like machine learning and deep learning. Due to the current generation of technology being the generation of “Connected World”, there is no lack of data any more. As a result of the introduction of internet of things, most of these connected devices are able to share and transfer data. Vision based techniques are techniques that are hugely depended on these vision based data.

scholarly journals Dual Inference for Machine Learning

2017 ◽  
Author(s):  
Yingce Xia ◽  
Jiang Bian ◽  
Tao Qin ◽  
Nenghai Yu ◽  
Tie-Yan Liu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Machine Translation ◽  
General Framework ◽  
Rapid Development ◽  
Empirical Studies ◽  
Image Generation ◽  
Dual Tasks ◽  
Individual Task

Recent years have witnessed the rapid development of machine learning in solving artificial intelligence (AI) tasks in many domains, including translation, speech, image, etc. Within these domains, AI tasks are usually not independent. As a specific type of relationship, structural duality does exist between many pairs of AI tasks, such as translation from one language to another vs. its opposite direction, speech recognition vs. speech synthetization, image classification vs. image generation, etc. The importance of such duality has been magnified by some recent studies, which revealed that it can boost the learning of two tasks in the dual form. However, there has been little investigation on how to leverage this invaluable relationship into the inference stage of AI tasks. In this paper, we propose a general framework of dual inference which can take advantage of both existing models from two dual tasks, without re-training, to conduct inference for one individual task. Empirical studies on three pairs of specific dual tasks, including machine translation, sentiment analysis, and image processing have illustrated that dual inference can significantly improve the performance of each of individual tasks.

scholarly journals Fuzzy Logic Using in Images Retrieval Depending on Their Features

2019 ◽  
Vol 54 (5) ◽  
Author(s):  
Faiez Musa Lahmood Alrufaye ◽  
Mohammed Muanis I. Al-Sagheer ◽  
Marwah Thamer Ali
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Fuzzy Logic ◽  
Computer Science ◽  
Image Recognition ◽  
Correct Result ◽  
Image Information ◽  
Fuzzy Logic Algorithm ◽  
Image Characteristics ◽  
Content Information

Image processing has become one of the most important branches of computer science, especially after entering into several areas of life such as medicine, engineering and various sciences. In our current research, we have developed a system of image recognition based on image characteristics and some content information using the most important artificial intelligence algorithms, a fuzzy logic algorithm, to obtain complete image information using small values ranging from 0 to 1. The program was executed on a set of standard database called the WANG database. It holds the contents of 1000 images from the Corel stock photo database, in JPEG format. The system was evaluated using the recall method. This method calculates the proportion of correct results identified by the system as correct results with correct result identified by the classic system.

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