Nonconvex 3D array image data recovery and pattern recognition under tensor framework

2022 ◽  
Vol 122 ◽  
pp. 108311
Author(s):  
Ming Yang ◽  
Qilun Luo ◽  
Wen Li ◽  
Mingqing Xiao
Keyword(s):  
Pattern Recognition ◽  
Image Data ◽  
Data Recovery

scholarly journals Image Substance Extraction using Data Mining Clustering Method

2019 ◽  
Vol 9 (2) ◽  
pp. 2735-2739
Keyword(s):  
Data Mining ◽  
Accurate Result ◽  
Image Data ◽  
Data Recovery ◽  
Data Sets ◽  
Text Data ◽  
Data Set ◽  
Normal Text ◽  
Additional Burden ◽  
Using Data

Dater retrieval is one of the key challenging factor for today. Because of increasing the volume of data sets every year due to various factors. Information extraction in image data sets are too multifaceted compare with normal text data recovery. Image data set consist of different attributes those attribute sets are normalized before it extract from the stored data base. This required additional burden to the user who wish to extract any information from this data sets. This key challenges invite more researchers in the field of image data mining. Today many of the data sets in the form of image it gives more accurate result and more outputs. For extracting any image data image attributes are properly trained for better result. The proposed work based on grouping the data sets using image attributes. The entire process of this work divided into two major separate operations. Experiments dons against various data sets, and outputs verified proposed work gives more accurate results than the existing techniques.

THE APPLICATION OF PATTERN RECOGNITION AND MACHINE LEARNING TO DETERMINE CEMENT CHANNELING & BOND QUALITY FROM AZIMUTHAL CEMENT BOND LOGS

2021 ◽  
Author(s):  
Andrew Imrie ◽  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Input Data ◽  
Synthetic Data ◽  
Image Data ◽  
Automated Analysis ◽  
Comparison Method ◽  
Automated Classification ◽  
Bond Quality ◽  
Automated Identification

Cement bond log interpretation methods consist of human pattern recognition and evaluation of the quality of the downhole isolation. Typically, a log interpreter compares acquisition data to their predefined classifications of cement bond quality. This paper outlines a complementary technique of intelligent cement evaluation and the implementation of the analysis of cement evaluation data by utilizing automatic pattern matching and machine learning. The proposed method is capable of defining bond quality across multiple distinct subclassification through analysis of image data using pattern recognition. Libraries of real log responses are used as comparisons to input data, and additionally may be supplemented with synthetic data. Using machine learning and image-based pattern recognition, the bond quality is classified into succinct categories to determine the presence of channeling. Successful classifications of the input data can then be added to the libraries, thus improving future analysis through an iterative process. The system uses the outputs of a conventional azimuthal ultrasonic scanning cement evaluation log and 5-ft CBL waveform to conclude a cement bond interpretation. The 5-ft CBL waveform is an optional addition to the processand improves the interpretation. The system searches forsimilarities between the acquisition data and thatcontained in the library. These similarities are comparedto evaluate the bonding. The process is described in two parts: i) image collection and library classification and ii) pattern recognition and interpretation. The former is the process of generating a readable library of reference data from historical cement evaluation logs and laboratory measurements and the latter is the machine learning and comparison method. Example results are shown with good correlations between automated analysis and interpreter analysis. The system is shown to be particularly capable at the automated identification of channeling of varying sizes, something which would be a challenge when using only the scalar curve representation of azimuthal data. Previously published methodologies for automated classification of bond quality typically utilize scaler data whereas this approach utilizes image-based pattern recognition for automated, learning and intelligent cement evaluation (ALICE). A discussion is presented on the limitations and merits of the ALICE process which include quality control, the removal of analyst bias during interpretation, and the fact that such a system will continually improve in accuracy through supervised training.

scholarly journals Rotation Invariant Parallel Signal Processing Using a Diffractive Phase Element for Image Compression

2022 ◽  
Vol 12 (1) ◽  
pp. 439
Author(s):  
Habib Hamam
Keyword(s):  
Pattern Recognition ◽  
Signal Processing ◽  
Image Data ◽  
Rotation Invariant ◽  
Diffractive Element ◽  
Harmonic Decomposition ◽  
Focus Image ◽  
Diffractive Phase Element ◽  
Parallel Pattern ◽  
Parallel Signal Processing

We propose a new rotation invariant correlator using dimensionality reduction. A diffractive phase element is used to focus image data into a line which serves as input for a conventional correlator. The diffractive element sums information over each radius of the scene image and projects the result onto one point of a line located at a certain distance behind the image. The method is flexible, to a large extent, and might include parallel pattern recognition and classification as well as further geometrical invariance. Although the new technique is inspired from circular harmonic decomposition, it does not suffer from energy loss. A theoretical analysis, as well as examples, are given.

scholarly journals ВЫБОР МОДЕЛИ БИОЛОГИЧЕСКОЙ НЕЙРОННОЙ СЕТИ ДЛЯ СЕГМЕНТАЦИИ ИЗОБРАЖЕНИЯ БИОЖИДКОСТНОЙ ПОВЕРХНОСТИ

2019 ◽  
pp. 78-93
Author(s):  
М.Е. Семенов ◽  
Т.Ю. Заблоцкая
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Network Architecture ◽  
Real Life ◽  
Image Data ◽  
Network Models ◽  
Digital Processing ◽  
Neural Network Models ◽  
Biological Neural Network ◽  
Delay Differential

In the paper, the biological neural network models are analyzed with a purpose to solve the problems of segmentation and pattern recognition when applied to the bio-liquid facies obtained by the cuneiform dehydration method. The peculiarities of the facies’ patterns and the key steps of their digital processing are specified in the frame of the pattern recognition. Feasibility of neural network techniques for the different image data level digital processing is reviewed as well as for image segmentation. The real-life biological neural network architecture concept is described using the mechanisms of the electrical input-output membrane voltage and both induced and endogenic (spontaneous) activities of the neural clusters when spiking. The mechanism of spike initiation is described for metabotropic and ionotropic receptive clusters with the nature of environmental exciting impact specified. Also, the mathematical models of biological neural networks that comprise ot only functional nonlinearities but the hysteretic ones are analyzed and the reasons are given for preference of the mathematical model with delay differential equations is chosen providing its applicability for modeling a single neuron and neural network as well. В работе рассматривается применение моделей биологической нейронной сети для сегментации изображения фации биожидкости, полученной методом клиновидной дегидратации. Выделены основные характерные особенности, присущие паттернам фаций биожидкостей, а также основные этапы их цифровой обработки в рамках задачи распознавания образов. Проведен анализ использования искусственных нейронных сетей для цифровой обработки изображений для разных уровней представления данных; сделан обзор основных нейросетевых методов сегментации. Описан принцип построения биологически достоверных искусственных нейронных сетей, использующих механизмы изменения мембранного потенциала нейронов и учитывающих при генерации спайка как вызванную активность, так и эндогенную (спонтанную) активность нейронных кластеров. Описан механизм инициации спайка для метаботропных и ионотропных рецептивных кластеров с указанием природы запускающего внешнего воздействия. Проведен анализ существующих математических моделей биологических нейросетей, содержащих помимо обычных функциональных нелинейностей нелинейности гистерезисной природы. Сделан выбор в пользу математической модели, использующей дифференциальные уравнения с запаздыванием, которые могут быть применены как для описания отдельного биологического нейрона, так и для описания работы нейронной сети.

Automatic Detection of the Cracks on the Concrete Railway Sleepers

2019 ◽  
Vol 33 (09) ◽  
pp. 1955010
Author(s):  
Seyed Amir Hossein Tabatabaei ◽  
Ahmad Delforouzi ◽  
Muhammad Hassan Khan ◽  
Tim Wesener ◽  
Marcin Grzegorzek
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Intelligent Systems ◽  
Crack Detection ◽  
Image Data ◽  
Experimental Comparison ◽  
Pattern Recognition Techniques ◽  
Comparison Results ◽  
Recognition Systems ◽  
Computer Recognition

A vision-based method for detecting the cracks in the concrete sleepers of the railway tracks will be introduced in this paper. The method is able to detect and partially classify the cracks of the concrete sleepers in two successive steps based on the image processing and pattern recognition techniques. The method has been implemented on the acquired image data frames followed by the analysis, experimental, comparison results and evaluation. The presented results are reasonable which indicates the goodness of the introduced method. The preliminary results of this work have been presented in [A. Delforouzi, A. H. Tabatabaei, M. H. Khan and M. Grzegorzek, A vision-based method for automatic crack detection in railway sleepers, in Kurzynski, M., Wozniak, M., Burduk, R. (eds.), Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017, Polanica Zdroj, Poland. CORES 2017. Advances in Intelligent Systems and Computing, Vol. 578 (Springer, Cham, 2018), pp. 130–139, doi: 10.1007/978-3-319-59162-9_14].

Dynamical Scattering in Crystalline Biological Specimens. I. Criteria of Validity for Scattering Approximations

1975 ◽  
Vol 33 ◽  
pp. 192-193
Author(s):  
Robert M. Glaeser ◽  
Bing K. Jap
Keyword(s):  
Biological Sciences ◽  
Electron Microscopy ◽  
Electron Microscope ◽  
Electron Diffraction ◽  
Born Approximation ◽  
Materials Science ◽  
Image Data ◽  
Dynamical Effect ◽  
Crystallographic Structure ◽  
Electron Microscope Image

The dynamical scattering effect, which can be described as the failure of the first Born approximation, is perhaps the most important factor that has prevented the widespread use of electron diffraction intensities for crystallographic structure determination. It would seem to be quite certain that dynamical effects will also interfere with structure analysis based upon electron microscope image data, whenever the dynamical effect seriously perturbs the diffracted wave. While it is normally taken for granted that the dynamical effect must be taken into consideration in materials science applications of electron microscopy, very little attention has been given to this problem in the biological sciences.

Application of an image management system in microscopy

1992 ◽  
Vol 50 (2) ◽  
pp. 1052-1053
Author(s):  
Richard S. Chemock
Keyword(s):  
Data Storage ◽  
Cost Savings ◽  
Image Data ◽  
Analytical Techniques ◽  
Operational Mode ◽  
Fine Grained ◽  
Image Recording ◽  
Primary Output ◽  
Capacity Data ◽  
Image Management System

One of the most common tasks in a typical analysis lab is the recording of images. Many analytical techniques (TEM, SEM, and metallography for example) produce images as their primary output. Until recently, the most common method of recording images was by using film. Current PS/2R systems offer very large capacity data storage devices and high resolution displays, making it practical to work with analytical images on PS/2s, thereby sidestepping the traditional film and darkroom steps. This change in operational mode offers many benefits: cost savings, throughput, archiving and searching capabilities as well as direct incorporation of the image data into reports.The conventional way to record images involves film, either sheet film (with its associated wet chemistry) for TEM or PolaroidR film for SEM and light microscopy. Although film is inconvenient, it does have the highest quality of all available image recording techniques. The fine grained film used for TEM has a resolution that would exceed a 4096x4096x16 bit digital image.

Sign in / Sign up

Export Citation Format

Share Document