scholarly journals Implementation of Intelligent Monitoring for the Marshalling Yard

2020 ◽  
Vol 17 (4) ◽  
pp. 98-110
Author(s):  
S. M. Kovalyov ◽  
A. V. Sukhanov
Keyword(s):  
Computer Vision ◽  
Image Data ◽  
Freight Transportation ◽  
Central Place ◽  
Streaming Video ◽  
Technological Chain ◽  
Intelligent Analysis ◽  
Software Product ◽  
Marshalling Yard ◽  
Dynamic Objects

The railway marshalling station occupies a central place in the technological chain of freight transportation processes, since the speed of processing trains at marshalling yards determines the volume and cost of transportation. Therefore, development of automation and computerization of sorting processes results in growing efficiency of freight transportation in general. The objective of the study is to formalize the problem of cars’ monitoring within the railway marshalling yard and to develop a method for solving it with the use of algorithms of recognizing and positioning of dynamic objects through the intelligent data analysis of streaming video. The article presents a new approach to solution of the problem of monitoring moving units in the hump (sorting) yard of marshalling stations. The article suggests core criteria for identifying speed and positioning of the railway wagons when they are running after been separated at the hump. The article specifies that monitoring of moving units at hump yard is less automated in comparison with the monitoring at the hump itself, and that confirms the relevance of the research. To get the problem of the automation monitoring of moving units in the hump yard solved, the authors have suggested an algorithm that is based on the image data intelligent analysis, that is on computer vision, and have described the model of its implementation at a station. The methods used are based on the theory of computer vision and are aimed at recognizing key dynamic objects in streaming video and at their subsequent positioning. The study has resulted in substantiation of acceptability of the use of computer vision in the process of separation and formation of trains. It is planned to proceed with further improvement of the presented approach to develop a software product allowing to objectify information about hump yard in order to increase the efficiency of targeted braking at the hump.

FEASIBILITY ANALYSIS OF AN AUTOMATED DETECTION OF PHYSICAL DEFECT VIA COMPUTER VISION

2015 ◽  
Vol 76 (12) ◽  
Author(s):  
Por Jing Zhao ◽  
Shafriza Nisha Basah ◽  
Shazmin Aniza Abdul Shukor
Keyword(s):  
Computer Vision ◽  
Low Cost ◽  
Image Data ◽  
Automated Detection ◽  
Building Construction ◽  
High Demand ◽  
Major City ◽  
Cost System ◽  
Kinect Camera ◽  
Wall Condition

High demand of building construction has been taking places in the major city of Malaysia. However, despite this magnificent development, the lack of proper maintenance has caused a large portion of these properties deteriorated over time. The implementation of the project - Automated Detection of Physical Defect via Computer Vision - is a low cost system that helps to inspect the wall condition using Kinect camera. The system is able to classify the types of physical defects -crack and hole - and state its level of severity.The system uses artificial neural network as the image classifier due to its reliability and consistency. The validity of the system is shown using experiments on synthetic and real image data. This automated physical defect detection could detect building defect early, quickly, and easily, which results in cost saving and extending building life span. 

scholarly journals PlantCV v2: Image analysis software for high-throughput plant phenotyping

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4088 ◽  
Author(s):  
Malia A. Gehan ◽  
Noah Fahlgren ◽  
Arash Abbasi ◽  
Jeffrey C. Berry ◽  
Steven T. Callen ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Image Data ◽  
Plant Phenotyping ◽  
Analysis Software ◽  
Computational Tools ◽  
Landmark Identification ◽  
Plant Image ◽  
Leaf Segmentation

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

scholarly journals Use of Computer Vision for Analysis of Image Data Sets from High Temperature Plasma Experiments

2021 ◽  
Author(s):  
Pawel Kozlowski ◽  
Yong Kim ◽  
Brian Haines ◽  
Thomas Day ◽  
Thomas Murphy ◽  
...  
Keyword(s):  
Computer Vision ◽  
High Temperature ◽  
Image Data ◽  
Data Sets ◽  
Temperature Plasma ◽  
High Temperature Plasma

Image Capture Board for The PC

1995 ◽  
Vol 32 (3) ◽  
pp. 235-255
Author(s):  
T. David Binnie ◽  
I. Reading
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Personal Computer ◽  
Low Cost ◽  
Random Access ◽  
Image Data ◽  
Image Capture ◽  
Design And Implementation

Image capture board for the PC We report the design and implementation of a low cost, image capture board for an IBM type personal computer. The board is particularly suited to computer vision education. The board provides: image capture at video rate, random access to xy addressable image data, and options for on-board image processing hardware.

scholarly journals An Image Detection Method Based on Parameter Optimization of Support Vector Machine

2021 ◽  
Vol 15 ◽  
pp. 306-314
Author(s):  
Zhanshen Feng
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Support Vector Machine ◽  
Parameter Optimization ◽  
Visual Processing ◽  
Image Data ◽  
Image Features ◽  
Classification Model ◽  
Support Vector ◽  
Image Detection

With the progress and development of multimedia image processing technology, and the rapid growth of image data, how to efficiently extract the interesting and valuable information from the huge image data, and effectively filter out the redundant data, these have become an urgent problem in the field of image processing and computer vision. In recent years, as one of the important branches of computer vision, image detection can assist and improve a series of visual processing tasks. It has been widely used in many fields, such as scene classification, visual tracking, object redirection, semantic segmentation and so on. Intelligent algorithms have strong non-linear mapping capability, data processing capacity and generalization ability. Support vector machine (SVM) by using the structural risk minimization principle constructs the optimal classification hyper-plane in the attribute space to make the classifier get the global optimum and has the expected risk meet a certain upper bound at a certain probability in the entire sample space. This paper combines SVM and artificial fish swarm algorithm (AFSA) for parameter optimization, builds AFSA-SVM classification model to achieve the intelligent identification of image features, and provides reliable technological means to accelerate sensing technology. The experiment result proves that AFSA-SVM has better classification accuracy and indicates that the algorithm of this paper can effectively realize the intelligent identification of image features.

scholarly journals Frontiers of Computer Vision Technologies on Real Estate Property Photographs and Floorplans

2021 ◽  
pp. 325-337
Author(s):  
Yoji Kiyota
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Deep Learning ◽  
Real Estate ◽  
Image Data ◽  
Learning Technologies ◽  
Data Sets ◽  
Academic Communities ◽  
Estate Property ◽  
Extract Information

AbstractThis article describes frontier efforts to apply deep learning technologies, which is the greatest innovation of research on artificial intelligence and computer vision, to image data such as real estate property photographs and floorplans. Specifically, attempts to detect property photographs that violate regulations or were misclassified, or to extract information that can be used as new recommendation features from property photographs, were mentioned. Besides, this article introduces an innovation created by providing data sets for academic communities.

scholarly journals CNN-Based Illumination Estimation with Semantic Information

2020 ◽  
Vol 10 (14) ◽  
pp. 4806 ◽  
Author(s):  
Ho-Hyoung Choi ◽  
Hyun-Soo Kang ◽  
Byoung-Ju Yun
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Semantic Information ◽  
Color Constancy ◽  
Noisy Data ◽  
Image Data ◽  
Input Image ◽  
The Novel ◽  
Image Patches ◽  
Wide Range

For more than a decade, both academia and industry have focused attention on the computer vision and in particular the computational color constancy (CVCC). The CVCC is used as a fundamental preprocessing task in a wide range of computer vision applications. While our human visual system (HVS) has the innate ability to perceive constant surface colors of objects under varying illumination spectra, the computer vision is facing the color constancy challenge in nature. Accordingly, this article proposes novel convolutional neural network (CNN) architecture based on the residual neural network which consists of pre-activation, atrous or dilated convolution and batch normalization. The proposed network can automatically decide what to learn from input image data and how to pool without supervision. When receiving input image data, the proposed network crops each image into image patches prior to training. Once the network begins learning, local semantic information is automatically extracted from the image patches and fed to its novel pooling layer. As a result of the semantic pooling, a weighted map or a mask is generated. Simultaneously, the extracted information is estimated and combined to form global information during training. The use of the novel pooling layer enables the proposed network to distinguish between useful data and noisy data, and thus efficiently remove noisy data during learning and evaluating. The main contribution of the proposed network is taking CVCC to higher accuracy and efficiency by adopting the novel pooling method. The experimental results demonstrate that the proposed network outperforms its conventional counterparts in estimation accuracy.

Methods and Applications for Segmenting 3D Medical Image Data

2011 ◽  
pp. 303-320
Author(s):  
Hong Shen
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
General Problem ◽  
Real World ◽  
Medical Image ◽  
Image Data ◽  
Medical Image Segmentation ◽  
Knowledge Based ◽  
Typical Sample ◽  
Medical Image Data

In this chapter, we will give an intuitive introduction to the general problem of 3D medical image segmentation. We will give an overview of the popular and relevant methods that may be applicable, with a discussion about their advantages and limits. Specifically, we will discuss the issue of incorporating prior knowledge into the segmentation of anatomic structures and describe in detail the concept and issues of knowledge-based segmentation. Typical sample applications will accompany the discussions throughout this chapter. We hope this will help an application developer to improve insights in the understanding and application of various computer vision approaches to solve real-world problems of medical image segmentation.

Sign in / Sign up

Export Citation Format

Share Document