Machine Vision for the Inspection of Natural Products

Machine vision technology has revolutionised the process of automated inspection in manufacturing. The specialist techniques required for inspection of natural products, such as food, leather, textiles and stone is still a challenging area of research. Topological variations make image processing al...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Graves, Mark (Επιμελητής έκδοσης), Batchelor, Bruce (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: London : Springer London, 2003.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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245 1 0 |a Machine Vision for the Inspection of Natural Products  |h [electronic resource] /  |c edited by Mark Graves, Bruce Batchelor. 
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505 0 |a Like Two Peas in a Pod -- Basic Machine Vision Techniques -- Intelligent Image Processing -- Using Natural Phenomena to Aid Food Produce Inspection -- Colour Sorting in the Food Industry -- Surface Defect Detection on Ceramics -- On-line Automated Visual Grading of Fruit: Practical Challenges -- Vision-based Quality Control in Poultry Processing -- Quality Classification of Wooden Surfaces Using Gabor Filters and Genetic Feature Optimisation -- An Intelligent Approach to Fabric Defect Detection in Textile Processes -- Automated Cutting of Natural Products: A Practical Packing Strategy -- Model-based Stereo Imaging for Estimating the Biomass of Live Fish -- A System for Estimating the Size and Shape of Live Pigs -- Sheep Pelt Inspection -- Design of Object Location Algorithms and Their Use for Food and Cereals Inspection -- X-ray Bone Detection in Further Processed Poultry Production -- Final Remarks. 
520 |a Machine vision technology has revolutionised the process of automated inspection in manufacturing. The specialist techniques required for inspection of natural products, such as food, leather, textiles and stone is still a challenging area of research. Topological variations make image processing algorithm development, system integration and mechanical handling issues much more complex. The practical issues of making machine vision systems operate robustly in often hostile environments together with the latest technological advancements are reviewed in this volume. Features: - Case studies based on real-world problems to demonstrate the practical application of machine vision systems. - In-depth description of system components including image processing, illumination, real-time hardware, mechanical handling, sensing and on-line testing. - Systems-level integration of constituent technologies for bespoke applications across a variety of industries. - A diverse range of example applications that a system may be required to handle from live fish to ceramic tiles. Machine Vision for the Inspection of Natural Products will be a valuable resource for researchers developing innovative machine vision systems in collaboration with food technology, textile and agriculture sectors. It will also appeal to practising engineers and managers in industries where the application of machine vision can enhance product safety and process efficiency. 
650 0 |a Engineering. 
650 0 |a Image processing. 
650 0 |a Pattern recognition. 
650 0 |a Control engineering. 
650 0 |a Robotics. 
650 0 |a Mechatronics. 
650 0 |a Quality control. 
650 0 |a Reliability. 
650 0 |a Industrial safety. 
650 0 |a Microwaves. 
650 0 |a Optical engineering. 
650 1 4 |a Engineering. 
650 2 4 |a Quality Control, Reliability, Safety and Risk. 
650 2 4 |a Microwaves, RF and Optical Engineering. 
650 2 4 |a Image Processing and Computer Vision. 
650 2 4 |a Pattern Recognition. 
650 2 4 |a Control, Robotics, Mechatronics. 
700 1 |a Graves, Mark.  |e editor. 
700 1 |a Batchelor, Bruce.  |e editor. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9781852335250 
856 4 0 |u http://dx.doi.org/10.1007/b97526  |z Full Text via HEAL-Link 
912 |a ZDB-2-ENG 
912 |a ZDB-2-BAE 
950 |a Engineering (Springer-11647)