The Experts below are selected from a list of 9237 Experts worldwide ranked by ideXlab platform
Huw Smith - One of the best experts on this subject based on the ideXlab platform.
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Machine Vision Application to the detection of water-borne micro-organisms
Intelligent Decision Technologies, 2009Co-Authors: Hernando Fernandez-canque, Sorin Hintea, Gabor Csipkes, Sorin Bota, Huw SmithAbstract:The work presented in this paper uses a novel Machine Vision Application to detect and identify micro-organism oocysts on drinking water. This new concept of water borne micro-organism detection uses image processing to allow detailed inspection of parasite morphology to nanometre dimensions. The detection results are more reliable than existing manual methods. The Machine Vision proposed provides a 100% detection of cryptosporidium micro-organism as test case. Combining Normarski Differential Interface Contrast (DIC) and fluorescence microscopy using Fluorescein Isothiocyanate (FITC) and UV filters, the system provides a reliable detection of micro-organisms with a considerable reduction in time, cost and subjectivity over the current labour intensive time consuming manual method.
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Machine Vision Application to the detection of micro organism in drinking water
International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 2008Co-Authors: Hernando Fernandezcanque, Sorin Hintea, Gabor Csipkes, Allan Pellow, Huw SmithAbstract:The work presented in this paper uses a novel Machine Vision Application to detect and identify micro-organism oocysts on drinking water. This new concept of water borne micro-organism detection uses image processing to allow detailed inspection of parasite morphology to nanometre dimensions. The detection results are more reliable than existing manual methods. Combining Normarski Differential Interface Contrast (DIC) and fluorescence microscopy using Fluorescein Isothiocyanate (FITC) and UV filters, the system provides a reliable detection of micro-organisms with a considerable reduction in time, cost and subjectivity over the current labour intensive time consuming manual method.
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KES (3) - Machine Vision Application to the Detection of Micro-organism in Drinking Water
Lecture Notes in Computer Science, 1Co-Authors: Hernando Fernandez-canque, Sorin Hintea, Gabor Csipkes, Allan Pellow, Huw SmithAbstract:The work presented in this paper uses a novel Machine Vision Application to detect and identify micro-organism oocysts on drinking water. This new concept of water borne micro-organism detection uses image processing to allow detailed inspection of parasite morphology to nanometre dimensions. The detection results are more reliable than existing manual methods. Combining Normarski Differential Interface Contrast (DIC) and fluorescence microscopy using Fluorescein Isothiocyanate (FITC) and UV filters, the system provides a reliable detection of micro-organisms with a considerable reduction in time, cost and subjectivity over the current labour intensive time consuming manual method.
Hernando Fernandez-canque - One of the best experts on this subject based on the ideXlab platform.
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Machine Vision Application to Automatic Detection of Living Cells/Objects
Human-Centric Machine Vision, 2012Co-Authors: Hernando Fernandez-canqueAbstract:The concept of Machine Vision (MV) originates in the early 1950’s and practical MV Applications appeared in the early 1980’s. Early theoretical work suggested serious limitations in computational abilities as the main reason for inefficient use of MV. MV is a ‘simple’ processing system which receives and combines signals from cameras by manipulating images at the pixel level to extract information that can be used in a decision making activity to produce the output required. MV has several advantages over systems utilising conventional technologies. The attention span of human operators is relatively short (Butler, 1980). Camera images are detailed and therefore contain a large amount of information. This combined with the computer power available provides a huge potential for MV Applications. The chapter will provide an overview on the developments and historical evolution of the concept of MV Applications. This chapter will concentrate on MV Application to automatic detection of objects with ill defined shape, size, and colour with high variability. Objects that change with time have no fixed structure; can present extra problems on Application of automatic detection using MV. For this type of Application, current manual detection requires highly specialised and highly trained operators. The Application of MV will facilitate the detection of these objects with the advantage of fast response. It is easy to use, cost effective with consistent and reliable results. The detection of micro-organisms and the detection of suspicious activity in humans fall into this category. The first example examines development of an automatic system for microscopic examination of the recovered deposit for the detection and enumeration of the microorganism Cryptosporidium. The second example addresses the Application of MV to the task of intruder monitoring within the context of visual security systems. The chapter will present these two Applications to illustrate problems encountered in this type of detection.
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Machine Vision Application to the detection of water-borne micro-organisms
Intelligent Decision Technologies, 2009Co-Authors: Hernando Fernandez-canque, Sorin Hintea, Gabor Csipkes, Sorin Bota, Huw SmithAbstract:The work presented in this paper uses a novel Machine Vision Application to detect and identify micro-organism oocysts on drinking water. This new concept of water borne micro-organism detection uses image processing to allow detailed inspection of parasite morphology to nanometre dimensions. The detection results are more reliable than existing manual methods. The Machine Vision proposed provides a 100% detection of cryptosporidium micro-organism as test case. Combining Normarski Differential Interface Contrast (DIC) and fluorescence microscopy using Fluorescein Isothiocyanate (FITC) and UV filters, the system provides a reliable detection of micro-organisms with a considerable reduction in time, cost and subjectivity over the current labour intensive time consuming manual method.
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KES (2) - Machine Vision Application to Automatic Intruder Detection Using CCTV
Knowledge-Based and Intelligent Information and Engineering Systems, 2009Co-Authors: Hernando Fernandez-canque, Sorin Hintea, J A Freer, Ali AhmadiniaAbstract:The work presented in this paper addresses the Application of new technologies to the task of intruder monitoring. It presents an innovative Machine Vision Application to detect and track a person in a Closed Circuit TeleVision System (CCTV) identifying suspicious activity. Neural Network techniques are applied to identify suspicious activities from the trajectory path, speed, direction and risk areas for a person in a scene, as well as human posture. Results correlate well with operator determining suspicious activity. The automated system presented assists an operator to increase reliability and to monitor large numbers of surveillance cameras.
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KES (3) - Machine Vision Application to the Detection of Micro-organism in Drinking Water
Lecture Notes in Computer Science, 1Co-Authors: Hernando Fernandez-canque, Sorin Hintea, Gabor Csipkes, Allan Pellow, Huw SmithAbstract:The work presented in this paper uses a novel Machine Vision Application to detect and identify micro-organism oocysts on drinking water. This new concept of water borne micro-organism detection uses image processing to allow detailed inspection of parasite morphology to nanometre dimensions. The detection results are more reliable than existing manual methods. Combining Normarski Differential Interface Contrast (DIC) and fluorescence microscopy using Fluorescein Isothiocyanate (FITC) and UV filters, the system provides a reliable detection of micro-organisms with a considerable reduction in time, cost and subjectivity over the current labour intensive time consuming manual method.
Sorin Hintea - One of the best experts on this subject based on the ideXlab platform.
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Machine Vision Application to automatic intruder detection using cctv
International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 2009Co-Authors: Hernando Fernandezcanque, Sorin Hintea, J A Freer, Ali AhmadiniaAbstract:The work presented in this paper addresses the Application of new technologies to the task of intruder monitoring. It presents an innovative Machine Vision Application to detect and track a person in a Closed Circuit TeleVision System (CCTV) identifying suspicious activity. Neural Network techniques are applied to identify suspicious activities from the trajectory path, speed, direction and risk areas for a person in a scene, as well as human posture. Results correlate well with operator determining suspicious activity. The automated system presented assists an operator to increase reliability and to monitor large numbers of surveillance cameras.
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Machine Vision Application to the detection of water-borne micro-organisms
Intelligent Decision Technologies, 2009Co-Authors: Hernando Fernandez-canque, Sorin Hintea, Gabor Csipkes, Sorin Bota, Huw SmithAbstract:The work presented in this paper uses a novel Machine Vision Application to detect and identify micro-organism oocysts on drinking water. This new concept of water borne micro-organism detection uses image processing to allow detailed inspection of parasite morphology to nanometre dimensions. The detection results are more reliable than existing manual methods. The Machine Vision proposed provides a 100% detection of cryptosporidium micro-organism as test case. Combining Normarski Differential Interface Contrast (DIC) and fluorescence microscopy using Fluorescein Isothiocyanate (FITC) and UV filters, the system provides a reliable detection of micro-organisms with a considerable reduction in time, cost and subjectivity over the current labour intensive time consuming manual method.
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KES (2) - Machine Vision Application to Automatic Intruder Detection Using CCTV
Knowledge-Based and Intelligent Information and Engineering Systems, 2009Co-Authors: Hernando Fernandez-canque, Sorin Hintea, J A Freer, Ali AhmadiniaAbstract:The work presented in this paper addresses the Application of new technologies to the task of intruder monitoring. It presents an innovative Machine Vision Application to detect and track a person in a Closed Circuit TeleVision System (CCTV) identifying suspicious activity. Neural Network techniques are applied to identify suspicious activities from the trajectory path, speed, direction and risk areas for a person in a scene, as well as human posture. Results correlate well with operator determining suspicious activity. The automated system presented assists an operator to increase reliability and to monitor large numbers of surveillance cameras.
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Machine Vision Application to the detection of micro organism in drinking water
International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 2008Co-Authors: Hernando Fernandezcanque, Sorin Hintea, Gabor Csipkes, Allan Pellow, Huw SmithAbstract:The work presented in this paper uses a novel Machine Vision Application to detect and identify micro-organism oocysts on drinking water. This new concept of water borne micro-organism detection uses image processing to allow detailed inspection of parasite morphology to nanometre dimensions. The detection results are more reliable than existing manual methods. Combining Normarski Differential Interface Contrast (DIC) and fluorescence microscopy using Fluorescein Isothiocyanate (FITC) and UV filters, the system provides a reliable detection of micro-organisms with a considerable reduction in time, cost and subjectivity over the current labour intensive time consuming manual method.
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KES (3) - Machine Vision Application to the Detection of Micro-organism in Drinking Water
Lecture Notes in Computer Science, 1Co-Authors: Hernando Fernandez-canque, Sorin Hintea, Gabor Csipkes, Allan Pellow, Huw SmithAbstract:The work presented in this paper uses a novel Machine Vision Application to detect and identify micro-organism oocysts on drinking water. This new concept of water borne micro-organism detection uses image processing to allow detailed inspection of parasite morphology to nanometre dimensions. The detection results are more reliable than existing manual methods. Combining Normarski Differential Interface Contrast (DIC) and fluorescence microscopy using Fluorescein Isothiocyanate (FITC) and UV filters, the system provides a reliable detection of micro-organisms with a considerable reduction in time, cost and subjectivity over the current labour intensive time consuming manual method.
Gabor Csipkes - One of the best experts on this subject based on the ideXlab platform.
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Machine Vision Application to the detection of water-borne micro-organisms
Intelligent Decision Technologies, 2009Co-Authors: Hernando Fernandez-canque, Sorin Hintea, Gabor Csipkes, Sorin Bota, Huw SmithAbstract:The work presented in this paper uses a novel Machine Vision Application to detect and identify micro-organism oocysts on drinking water. This new concept of water borne micro-organism detection uses image processing to allow detailed inspection of parasite morphology to nanometre dimensions. The detection results are more reliable than existing manual methods. The Machine Vision proposed provides a 100% detection of cryptosporidium micro-organism as test case. Combining Normarski Differential Interface Contrast (DIC) and fluorescence microscopy using Fluorescein Isothiocyanate (FITC) and UV filters, the system provides a reliable detection of micro-organisms with a considerable reduction in time, cost and subjectivity over the current labour intensive time consuming manual method.
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Machine Vision Application to the detection of micro organism in drinking water
International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 2008Co-Authors: Hernando Fernandezcanque, Sorin Hintea, Gabor Csipkes, Allan Pellow, Huw SmithAbstract:The work presented in this paper uses a novel Machine Vision Application to detect and identify micro-organism oocysts on drinking water. This new concept of water borne micro-organism detection uses image processing to allow detailed inspection of parasite morphology to nanometre dimensions. The detection results are more reliable than existing manual methods. Combining Normarski Differential Interface Contrast (DIC) and fluorescence microscopy using Fluorescein Isothiocyanate (FITC) and UV filters, the system provides a reliable detection of micro-organisms with a considerable reduction in time, cost and subjectivity over the current labour intensive time consuming manual method.
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KES (3) - Machine Vision Application to the Detection of Micro-organism in Drinking Water
Lecture Notes in Computer Science, 1Co-Authors: Hernando Fernandez-canque, Sorin Hintea, Gabor Csipkes, Allan Pellow, Huw SmithAbstract:The work presented in this paper uses a novel Machine Vision Application to detect and identify micro-organism oocysts on drinking water. This new concept of water borne micro-organism detection uses image processing to allow detailed inspection of parasite morphology to nanometre dimensions. The detection results are more reliable than existing manual methods. Combining Normarski Differential Interface Contrast (DIC) and fluorescence microscopy using Fluorescein Isothiocyanate (FITC) and UV filters, the system provides a reliable detection of micro-organisms with a considerable reduction in time, cost and subjectivity over the current labour intensive time consuming manual method.
Allan Pellow - One of the best experts on this subject based on the ideXlab platform.
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Machine Vision Application to the detection of micro organism in drinking water
International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 2008Co-Authors: Hernando Fernandezcanque, Sorin Hintea, Gabor Csipkes, Allan Pellow, Huw SmithAbstract:The work presented in this paper uses a novel Machine Vision Application to detect and identify micro-organism oocysts on drinking water. This new concept of water borne micro-organism detection uses image processing to allow detailed inspection of parasite morphology to nanometre dimensions. The detection results are more reliable than existing manual methods. Combining Normarski Differential Interface Contrast (DIC) and fluorescence microscopy using Fluorescein Isothiocyanate (FITC) and UV filters, the system provides a reliable detection of micro-organisms with a considerable reduction in time, cost and subjectivity over the current labour intensive time consuming manual method.
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KES (3) - Machine Vision Application to the Detection of Micro-organism in Drinking Water
Lecture Notes in Computer Science, 1Co-Authors: Hernando Fernandez-canque, Sorin Hintea, Gabor Csipkes, Allan Pellow, Huw SmithAbstract:The work presented in this paper uses a novel Machine Vision Application to detect and identify micro-organism oocysts on drinking water. This new concept of water borne micro-organism detection uses image processing to allow detailed inspection of parasite morphology to nanometre dimensions. The detection results are more reliable than existing manual methods. Combining Normarski Differential Interface Contrast (DIC) and fluorescence microscopy using Fluorescein Isothiocyanate (FITC) and UV filters, the system provides a reliable detection of micro-organisms with a considerable reduction in time, cost and subjectivity over the current labour intensive time consuming manual method.
