A Knowledge-Based Real-Time Vision System for Monitoring the Inside of a Fluid Bed Heat Exchanger Chamber

2018 ◽  
Vol 28 (09) ◽  
pp. 1249-1265
Author(s):  
HyungJun Kim
Keyword(s):  
Image Processing ◽  
Heat Exchanger ◽  
Real Time ◽  
Vision System ◽  
Thermal Power ◽  
Experimental Results ◽  
Image Analysis System ◽  
Real Time Monitoring ◽  
Knowledge Based ◽  
Fluid Bed

In this paper, we present a vision system with a special camera for knowledge-based real-time monitoring of the inside of a fluid bed heat exchanger (FBHE) chamber in a thermal power plant. With the proposed system, it is possible to monitor the internal flux condition and analyze the inner temperature of a chamber. Due to the fact that the mixture of coal and sand inside a chamber flows by very quickly, there is an immense amount of smoke and dust, which make it difficult to capture images and analyze an existing system. An adaptive average method is proposed here to observe the background internal environment of an FBHE chamber. The experimental results show that real-time monitoring is possible, even under hot and dusty conditions. Preliminary experimental results confirm expectations about the possibility and effectiveness of the developed device for commercialized real-time monitoring systems. They demonstrate that a single camera with embedded image processing software can concurrently analyze the degree of fluidization of a mixture and the temperature of the chamber inside, even in extremely harsh and hazardous conditions. We aim to eventually develop an image analysis system that combines image processing and knowledge engineering techniques.

scholarly journals OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

scholarly journals Real–Time Eye Tracking and Iris Localization

2012 ◽  
Author(s):  
Husniza Razalli ◽  
Rahmita Wirza O. K. Rahmat ◽  
Ramlan Mahmud
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Vision System ◽  
Main Task ◽  
Circle Fitting ◽  
Eye Tracker ◽  
Eye Detection ◽  
Iris Localization ◽  
Point Detection ◽  
Human Eyes

Masalah sistem pengesanan mata yang tegar tanpa sebarang gangguan adalah satu isu yang penting dan mencabar di dalam bidang visi komputer. Masalah ini bukan hanya mengurangkan masalah dalam carian ciri–ciri paras rupa untuk proses pengecaman tetapi juga boleh digunakan untuk memudahkan tugas pengenalpastian dan interaksi antara manusia dan sistem komputer. Walaupun kebanyakan hasil kerja terdahulu telah pun mempunyai keupayaan menentukan lokasi mata manusia tetapi objektif utama rencana ini bukan tertumpu kepada pengesanan mata sahaja. Objektif kajian adalah untuk merekabentuk sebuah sistem masa nyata dan terperinci, iaitu sistem pengesanan muka berskala dengan ciri–ciri petunjuk pergerakan mata berdasarkan pergerakan anak mata (iris) dengan mengunakan teknik penempatan yang terhasil daripada teknik pemprosesan imej dan teknik muatan bulatan. Hasil daripada kajian ini telah pun berjaya diimplimentasikan menggunakan kamera web dengan ralat yang minimum. Kata kunci: Pengesanan mata masa nyata; penempatan anak mata; pemprosesan imej; pengesanan bucu; muatan bulatan Robust, non–intrusive human eye detection problem has been a fundamental and challenging problem for computer vision area. Not only it is a problem of its own, it can be used to ease the problem of finding the locations of other facial features for recognition tasks and human–computer interaction purposes as well. Many previous works have the capability of determining the locations of the human eyes but the main task in this paper is not only a vision system with eye detection capability. Our aim is to design a real–time face tracker system and iris localization using edge point detection method indicates from image processing and circle fitting technique. As a result, our eye tracker system was successfully implemented using non–intrusive webcam with less error. Key words: Real–time face tracking; iris localization; image processing; edge detection; circle fitting

A Robotic Facial Expression Recognition System Using Real-Time Vision System

2008 ◽  
Vol 381-382 ◽  
pp. 375-378
Author(s):  
K.T. Song ◽  
M.J. Han ◽  
F.Y. Chang ◽  
S.H. Chang
Keyword(s):  
Image Processing ◽  
Facial Expression ◽  
Real Time ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Vision System ◽  
Recognition System ◽  
Expression Recognition ◽  
On Line ◽  
Processing Platform

The capability of recognizing human facial expression plays an important role in advanced human-robot interaction development. Through recognizing facial expressions, a robot can interact with a user in a more natural and friendly manner. In this paper, we proposed a facial expression recognition system based on an embedded image processing platform to classify different facial expressions on-line in real time. A low-cost embedded vision system has been designed and realized for robotic applications using a CMOS image sensor and digital signal processor (DSP). The current design acquires thirty 640x480 image frames per second (30 fps). The proposed emotion recognition algorithm has been successfully implemented on the real-time vision system. Experimental results on a pet robot show that the robot can interact with a person in a responding manner. The developed image processing platform is effective for accelerating the recognition speed to 25 recognitions per second with an average on-line recognition rate of 74.4% for five facial expressions.

Study on Tracking in Interested Moving Objects

2011 ◽  
Vol 382 ◽  
pp. 418-421
Author(s):  
Dong Yan Cui ◽  
Zai Xing Xie
Keyword(s):  
Computational Complexity ◽  
Real Time ◽  
Monitoring System ◽  
Moving Objects ◽  
Moving Object ◽  
Experimental Results ◽  
Real Time Monitoring ◽  
Automatic Program ◽  
Inter Frame

This paper presents an automatic program to track in moving objects, using segmentation algorithm quickly and efficiently after the division of a moving object, in the follow-up frame through the establishment of inter-frame vectors to track moving objects of interest. Experimental results show that the algorithm can accurately and effectively track moving objects of interest, and because the algorithm is simple, the computational complexity is small, can be well positioned to meet real-time monitoring system in the extraction of moving objects of interest and tracking needs.

Autonomous Real-Time Monitoring of Crops in Controlled Environment Agriculture

2019 ◽  
Author(s):  
Saba Faryadi ◽  
Mohammadreza Davoodi ◽  
Javad Mohammadpour Velni
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Directed Graphs ◽  
Controlled Environment ◽  
Real Time Monitoring ◽  
Collection System ◽  
Image Processing Algorithm ◽  
Ground Vehicle ◽  
Controlled Environment Agriculture ◽  
And Performance

Abstract In this work, we develop a system that can be used for real-time monitoring of multiple important areas in controlled environment agriculture (and in particular greenhouses) using an autonomous ground vehicle (AGV). To model the greenhouse layout, as well as the tasks that should be accomplished by the AGV, we generate two weighted directed graphs. Based on those graphs, an algorithm is then proposed for finding the optimal (in the sense of traveled distance) trajectory of the vehicle with the goal of precisely monitoring important areas in the greenhouse. Furthermore, a data collection system and image processing algorithm is proposed and implemented so that the vehicle: (i) can capture images and detect changes that have occurred on the crops in real time, and (ii) construct (if needed) a map of the plant rows, when arriving at each one of the important areas. Based on this work, the images can either be stitched onboard the vehicle and then sent to a server or be sent directly to the server and then processed (stitched) there. Both simulation and experimental results are provided to demonstrate the effectiveness and performance of the proposed system.

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