A Comparative of 3D Surface Extraction Methods for Potential Metrology Applications

2014 ◽  
Vol 615 ◽  
pp. 15-21
Author(s):  
Sinué Ontiveros-Zepeda ◽  
José Antonio Yagüe-Fabra ◽  
Roberto Jiménez Pacheco ◽  
Francisco Javier Brosed-Dueso
Keyword(s):  
Computer Vision ◽  
Edge Detection ◽  
Extraction Methods ◽  
Point Of View ◽  
Detection Methods ◽  
Volume Data ◽  
Surface Extraction ◽  
Advantages And Disadvantages ◽  
Factors Influencing ◽  
Number Of Factors

The number of factors influencing the CT process for metrology applications increases its complexity and cause the loss of accuracy during CT measurements. One of the most critical is the edge detection also called surface extraction or image segmentation, which is the process of surface formation from the CT`s volume data. This paper presents different edge detection methods commonly used in areas like machine and computer vision and they are analyzed as an alternative to the commonly and commercially used for CT metrology applications. Each method is described and analyzed separately in order to highlight its advantages and disadvantages from a metrological point of view. An experimental comparative between two of them is also shown.

scholarly journals The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection

2021 ◽  
Author(s):  
Paul Bergmann ◽  
Kilian Batzner ◽  
Michael Fauser ◽  
David Sattlegger ◽  
Carsten Steger
Keyword(s):  
Computer Vision ◽  
Anomaly Detection ◽  
Structural Changes ◽  
Performance Metrics ◽  
Generative Models ◽  
Detection Methods ◽  
Generative Adversarial Networks ◽  
Natural Image ◽  
Advantages And Disadvantages ◽  
Unsupervised Anomaly Detection

AbstractThe detection of anomalous structures in natural image data is of utmost importance for numerous tasks in the field of computer vision. The development of methods for unsupervised anomaly detection requires data on which to train and evaluate new approaches and ideas. We introduce the MVTec anomaly detection dataset containing 5354 high-resolution color images of different object and texture categories. It contains normal, i.e., defect-free images intended for training and images with anomalies intended for testing. The anomalies manifest themselves in the form of over 70 different types of defects such as scratches, dents, contaminations, and various structural changes. In addition, we provide pixel-precise ground truth annotations for all anomalies. We conduct a thorough evaluation of current state-of-the-art unsupervised anomaly detection methods based on deep architectures such as convolutional autoencoders, generative adversarial networks, and feature descriptors using pretrained convolutional neural networks, as well as classical computer vision methods. We highlight the advantages and disadvantages of multiple performance metrics as well as threshold estimation techniques. This benchmark indicates that methods that leverage descriptors of pretrained networks outperform all other approaches and deep-learning-based generative models show considerable room for improvement.

scholarly journals Recent Advancement in Anomaly Detection in Surveillance Videos

2019 ◽  
Vol 8 (2) ◽  
pp. 964-970
Keyword(s):  
Computer Vision ◽  
Anomaly Detection ◽  
Video Data ◽  
Detection Methods ◽  
Future Research ◽  
Detection Techniques ◽  
Advantages And Disadvantages ◽  
Recent Computer ◽  
Banking Education ◽  
To Come

Detection of Anomaly is of a notable and emergent problem into many diverse fields like information theory, deep learning, computer vision, machine learning, and statistics that have been researched within the various application from diverse domains including agriculture, health care, banking, education, and transport anomaly detection. Newly, numbers of important anomaly detection techniques along with diverseness of sort have been watched. The main aim of this paper to come up with a broad summary of the present development on detection of an anomaly, exclusively for video data with mixed types and high dimensionalities, where identifying the anomalous behaviors and event or anomalous patterns is a significant task. The paper expresses the advantages and disadvantages of the detection methods the experiments tried on the publically available benchmark dataset to assess numerous popular and classical methods and models. The objective of this analysis is to furnish an understanding of recent computer vision and machine algorithms methods and also state-of-the-art deep learnings techniques to detect anomalies for researchers. At last, the paper delivered roughly directions for future research on an anomalies detection.

scholarly journals Analysis of Surface Extraction Methods Based on Gradient Operators for Computed Tomography in Metrology Applications

Materials ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1461 ◽  
Author(s):  
Sinué Ontiveros ◽  
Roberto Jiménez ◽  
José Yagüe-Fabra ◽  
Marta Torralba
Keyword(s):  
Computed Tomography ◽  
Extraction Process ◽  
Extraction Methods ◽  
Regions Of Interest ◽  
Surface Extraction ◽  
Flat Surfaces ◽  
Multiple Factors ◽  
Factors Influencing ◽  
Canny Algorithm ◽  
Definition Of

Among the multiple factors influencing the accuracy of Computed Tomography measurements, the surface extraction process is a significant contributor. The location of the surface for metrological applications is generally based on the definition of a gray value as a characteristic of similarity to define the regions of interest. A different approach is to perform the detection or location of the surface based on the discontinuity or gradient. In this paper, an adapted 3D Deriche algorithm based on gradient information is presented and compared with a previously developed adapted Canny algorithm for different surface types. Both algorithms have been applied to nine calibrated workpieces with different geometries and materials. Both the systematic error and measurement uncertainty have been determined. The results show a significant reduction of the deviations obtained with the Deriche-based algorithm in the dimensions defined by flat surfaces.

scholarly journals Review of Weed Detection Methods Based on Computer Vision

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3647
Author(s):  
Zhangnan Wu ◽  
Yajun Chen ◽  
Bo Zhao ◽  
Xiaobing Kang ◽  
Yuanyuan Ding
Keyword(s):  
Computer Vision ◽  
Agricultural Production ◽  
Development Trend ◽  
Detection Methods ◽  
Future Research ◽  
Weed Detection ◽  
Factors Affecting ◽  
Advantages And Disadvantages ◽  
Related Plant ◽  
The Development Trend

Weeds are one of the most important factors affecting agricultural production. The waste and pollution of farmland ecological environment caused by full-coverage chemical herbicide spraying are becoming increasingly evident. With the continuous improvement in the agricultural production level, accurately distinguishing crops from weeds and achieving precise spraying only for weeds are important. However, precise spraying depends on accurately identifying and locating weeds and crops. In recent years, some scholars have used various computer vision methods to achieve this purpose. This review elaborates the two aspects of using traditional image-processing methods and deep learning-based methods to solve weed detection problems. It provides an overview of various methods for weed detection in recent years, analyzes the advantages and disadvantages of existing methods, and introduces several related plant leaves, weed datasets, and weeding machinery. Lastly, the problems and difficulties of the existing weed detection methods are analyzed, and the development trend of future research is prospected.

A REVIEW OF WAVELET-BASED EDGE DETECTION METHODS

2012 ◽  
Vol 26 (07) ◽  
pp. 1255011 ◽  
Author(s):  
P. S. P. WANG ◽  
JIANWEI YANG
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Pattern Recognition ◽  
Edge Detection ◽  
Main Idea ◽  
Detection Methods ◽  
Local Regularity ◽  
The Past ◽  
Different Types ◽  
Key Issues

Edges are prominent features in images. The detection and analysis of edges are key issues in image processing, computer vision and pattern recognition. Wavelet provides a powerful tool to analyze the local regularity of signals. Wavelet transform has been successfully applied to the analysis and detection of edges. A great number of wavelet-based edge detection methods have been proposed over the past years. The objective of this paper is to give a brief review of these methods, and encourage the research of this topic. In practice, an image is usually of multistructure edge, the identification of different edges, such as steps, curves and junctions play an important role in pattern recognition. In this paper, more attention is paid on the identification of different types of edges. We present the main idea and the properties of these methods.

scholarly journals Veterinary Drug Residues in Animal-Derived Foods: Sample Preparation and Analytical Methods

Foods ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 555
Author(s):  
Bo Wang ◽  
Kaizhou Xie ◽  
Kiho Lee
Keyword(s):  
Analytical Methods ◽  
Extraction Methods ◽  
Veterinary Drugs ◽  
Detection Methods ◽  
Multi Drug Resistance ◽  
Veterinary Drug ◽  
Extraction Techniques ◽  
Veterinary Drug Residues ◽  
Drug Residues ◽  
Advantages And Disadvantages

Veterinary drugs are used to treat livestock and aquatic diseases and thus are introduced into animal-derived foods, endangering consumer health and safety. Antibiotic resistance is rapidly becoming a major worldwide problem, and there has been a steady increase in the number of pathogens that show multi-drug resistance. Illegal and excessive use of veterinary drugs in animals and aquaculture has serious adverse effects on humans and on all other environmental organisms. It is necessary to develop simple extraction methods and fast analytical methods to effectively detect veterinary drug residues in animal-derived foods. This review summarizes the application of various sample extraction techniques and detection and quantification methods for veterinary drug residues reported in the last decade (2010-2020). This review compares the advantages and disadvantages of various extraction techniques and detection methods and describes advanced methods, such as those that use electrochemical biosensors, piezoelectric biosensors, optical biosensors, and molecularly imprinted polymer biosensors. Finally, the future prospects and trends related to extraction methods, detection methods and advanced methods for the analysis of veterinary drug residues in animal-derived foods are summarized.

Stock Market Prediction using Data Mining Techniques

2020 ◽  
pp. 310-313
Author(s):  
Karunesh Makker ◽  
Prince Patel ◽  
Hrishikesh Roy ◽  
Sonali Borse
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Prediction Model ◽  
Efficient Market Hypothesis ◽  
Deterministic System ◽  
Vast Number ◽  
Factors Influencing ◽  
Number Of Factors ◽  
Using Data ◽  
Robust Prediction

Stock market is a very volatile in-deterministic system with vast number of factors influencing the direction of trend on varying scales and multiple layers. Efficient Market Hypothesis (EMH) states that the market is unbeatable. This makes predicting the uptrend or downtrend a very challenging task. This research aims to combine multiple existing techniques into a much more robust prediction model which can handle various scenarios in which investment can be beneficial. Existing techniques like sentiment analysis or neural network techniques can be too narrow in their approach and can lead to erroneous outcomes for varying scenarios. By combing both techniques, this prediction model can provide more accurate and flexible recommendations. Embedding Technical indicators will guide the investor to minimize the risk and reap better returns.

Comparative Analysis of Edge Detection Methods and its Implementation using UTLP Kit

2016 ◽  
Vol 3 (2) ◽  
pp. 26
Author(s):  
HEMALATHA R. ◽  
SANTHIYAKUMARI N. ◽  
MADHESWARAN M. ◽  
SURESH S. ◽  
◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Edge Detection ◽  
Detection Methods

ESSENTIAL CHARACTERISTICS OF THE PHENOMENA “AESTHETIC COMPETENCE OF PERSONALITY”

2019 ◽  
pp. 81-85
Author(s):  
А. Karam
Keyword(s):  
Cultural Studies ◽  
Sensory Experience ◽  
Point Of View ◽  
Factors Influencing ◽  
Full Capacity ◽  
Specific Meaning ◽  
Psychology Pedagogy

In the article it is revealed the essence of interpretation of the phenomenon of «aesthetic competence» from the point of view of philosophy, psychology, pedagogy, sociology, and cultural studies. Emphasis is placed on the interconnection of synonymous terms «readiness» and «preparedness»: «readiness» is a concept broader than competence and preparedness, which may be single, fragmented, that is, not to provide the full capacity to perform the functions of an activity. The essence of the outlined phenomenon is analyzed through its separate concepts, taking into account their relation: «aesthetic competence» with the concepts «competence», aesthetics «. Artistic and aesthetic competence is defined as a system of internal means of regulation of artistic and aesthetic actions, which includes artistic and aesthetic knowledge, social guidelines, skills and experience, aesthetic orientation, based on knowledge and sensory experience, free possession of artistic and aesthetic means and perception of artistic and aesthetic situation. The essence and features of aesthetic competence are revealed. The modern approaches to defining the concept of «aesthetic competence» are highlighted. The components of aesthetic competence are revealed. Specific features and factors influencing the development of aesthetic competence are highlighted. In conclusion, it is noted that the concept under study, aesthetic competence, should be differentiated into such varieties as aesthetic and artistic competence, while each of them, for a particular artistic profession, will at the same time have a general and specific meaning.

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