scholarly journals Advances in Computer Recognition, Image Processing and Communications

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 108
Author(s):  
Michał Choraś ◽  
Robert Burduk ◽  
Agata Giełczyk ◽  
Rafał Kozik ◽  
Tomasz Marciniak
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Pattern Recognition ◽  
Special Issue ◽  
High Quality ◽  
Recognition Pattern ◽  
Computer Recognition

This Special Issue aimed to gather high-quality advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing and machine learning (shallow and deep), including, in particular, novel implementations of these techniques in the areas of modern telecommunications and cybersecurity [...]

scholarly journals Special Issue “Machine Learning in Insurance”

Risks ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 54 ◽  
Author(s):  
Vali Asimit ◽  
Ioannis Kyriakou ◽  
Jens Perch Nielsen
Keyword(s):  
Machine Learning ◽  
Special Issue ◽  
High Quality ◽  
Avant Garde

It is our pleasure to prologue the special issue on “Machine Learning in Insurance”, which represents a compilation of ten high-quality articles discussing avant-garde developments or introducing new theoretical or practical advances in this field [...]

scholarly journals A Survey of Crowdsourcing in Medical Image Analysis

2020 ◽  
Vol 7 ◽  
pp. 1-26 ◽  
Author(s):  
Silas Nyboe Ørting ◽  
Andrew Doyle ◽  
Arno Van Hilten ◽  
Matthias Hirth ◽  
Oana Inel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Image Analysis ◽  
Medical Image ◽  
Large Scale ◽  
Medical Image Analysis ◽  
Machine Learning Algorithms ◽  
Imaging Analysis ◽  
High Quality ◽  
Comprehensive Literature Review

Rapid advances in image processing capabilities have been seen across many domains, fostered by the  application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part, due to the limited availability of large-scale, well-annotated datasets. One of the main reasons for this is the high cost often associated with producing large amounts of high-quality meta-data. Recently, there has been growing interest in the application of crowdsourcing for this purpose; a technique that has proven effective for creating large-scale datasets across a range of disciplines, from computer vision to astrophysics. Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis. In this survey, we review studies applying crowdsourcing to the analysis of medical images, published prior to July 2018. We identify common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach. Finally, we discuss future opportunities for development within this emerging domain.

Special Issue on Vision

1999 ◽  
Vol 11 (2) ◽  
pp. 87-87
Author(s):  
Shunichiro Oe ◽  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Pattern Recognition ◽  
Visual Processing ◽  
Research Work ◽  
Image Understanding ◽  
Normal Vector ◽  
Automatic Welding ◽  
Special Issue ◽  
Image Sensing

The widely used term <B>Computer Vision</B> applies to when computers are substituted for human visual information processing. As Real-world objects, except for characters, symbols, figures and photographs created by people, are 3-dimensional (3-D), their two-dimensional (2-D) images obtained by camera are produced by compressing 3-D information to 2-D. Many methods of 2-D image processing and pattern recognition have been developed and widely applied to industrial and medical processing, etc. Research work enabling computers to recognize 3-D objects by 3-D information extracted from 2-D images has been carried out in artificial intelligent robotics. Many techniques have been developed and some applied practically in scene analysis or 3-D measurement. These practical applications are based on image sensing, image processing, pattern recognition, image measurement, extraction of 3-D information, and image understanding. New techniques are constantly appearing. The title of this special issue is <B>Vision</B>, and it features 8 papers from basic computer vision theory to industrial applications. These papers include the following: Kohji Kamejima proposes a method to detect self-similarity in random image fields - the basis of human visual processing. Akio Nagasaka et al. developed a way to identify a real scene in real time using run-length encoding of video feature sequences. This technique will become a basis for active video recording and new robotic machine vision. Toshifumi Honda presents a method for visual inspection of solder joint by 3-D image analysis - a very important issue in the inspection of printed circuit boards. Saburo Okada et al. contribute a new technique on simultaneous measurement of shape and normal vector for specular objects. These methods are all useful for obtaining 3-D information. Masato Nakajima presents a human face identification method for security monitoring using 3-D gray-level information. Kenji Terada et al. propose a method of automatic counting passing people using image sensing. These two technologies are very useful in access control. Yoji. Ogawa presents a new image processing method for automatic welding in turbid water under a non-preparatory environment. Liu Wei et al. develop a method for detection and management of cutting-tool wear using visual sensors. We are certain that all of these papers will contribute greatly to the development of vision systems in robotics and mechatronics.

scholarly journals Recognition for lateral faces using Neural Networks

2021 ◽  
Vol 12 (3) ◽  
pp. 5589-5595
Author(s):  
Dr.M.Aruna Safali Et. al.
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Face Recognition ◽  
Lateral Face ◽  
Synthetic Dataset ◽  
Recognition Pattern ◽  
The Many

Face recognition is most difficult and complicated technique. Recognition of lateral faces is very difficult compare with normal face recognition. Pattern recognition is mostly used in this system to recognise the lateral face patterns (LFP).  Neural network is used to find the patterns and lateral face recognition can be done by this technique. After the many researches face recognition becomes difficulty for the various techniques based on their parameters. In this paper, the amalgamative lateral face recognition(ALFR) which is merged with machine learning and neural network features can be done by using synthetic dataset consists of 200 lateral faces. Performance shows the improved results of proposed technique.

Feature Extraction

2017 ◽  
pp. 48-105
Author(s):  
Anindita Das Bhattacharjee
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Extraction Process ◽  
Measured Data ◽  
Data Set ◽  
Content Domain ◽  
Accessibility Problem ◽  
The Common

Accessibility problem is relevant for audiovisual information, where enormous data has to be explored and processed. Most of the solutions for this specific type of problems point towards a regular need of extracting applicable information features for a given content domain. And feature extraction process deals with two complicated tasks first deciding and then extracting. There are certain properties expected from good features-Repeatability, Distinctiveness, Locality, Quantity, Accuracy, Efficiency, and Invariance. Different feature extraction techniques are described. The chapter concentrates of taking a survey on the topic of Feature extraction and Image formation. Here both image and video are considered to have their feature extracted. In machine learning, pattern recognition and in image processing has significant contribution. The feature extraction is one of the common mechanisms involved in these two techniques. Extracting feature initiates from an initial data set of measured data and constructs derived informative values which are non redundant in nature.

scholarly journals Machine Learning approaches for convergence of IoT and Blockchain

2020 ◽  
Vol 10 (1) ◽  
pp. 459-460
Author(s):  
Krishna Kant Singh ◽  
B. Balamurugan ◽  
Naveen Chilamkurti Smieee ◽  
Bharat S. Rawal Kshatriya
Keyword(s):  
Machine Learning ◽  
Data Handling ◽  
Learning Approaches ◽  
Special Issue ◽  
Fault Line ◽  
High Quality ◽  
High Quality Research ◽  
Quality Research ◽  
Public Networks ◽  
Iot Devices

AbstractBlockchain plays a significant role in providing security during data handling operations and defines how trusted transactions can be carried out addressing the solution for internet facing vulnerability problems. Blockchain solves the security fault line among AI and IoT, where most of the IoT devices are connected to each other through public networks. This special issue aims to publish a collection of high quality research articles in the area of blockchain, IoT and Machine Learning.

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