Current industrial surface inspection systems are capable of detecting various defects and producing images of them. The defect images are collected into large image databases. Effective retrieval methods are necessar...
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Current industrial surface inspection systems are capable of detecting various defects and producing images of them. The defect images are collected into large image databases. Effective retrieval methods are necessary to analyse the defects stored in the database. In this work, a novel shape descriptor for surface defect image retrieval is presented. The descriptor is based on the Fourier transform of the defect boundary. The proposed method, angular radius Fourier descriptor, combines the boundary function with the directional angle of the boundary line. The proposed method outperforms ordinary Fourier descriptors in the retrieval of paper defect shapes without increasing computational cost.
Here, an application of a set of auto-association networks with linear output neurons and sigmoidal hidden neurons for classified image compression is carried out. Simulations and statistical analysis of this type of ...
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Here, an application of a set of auto-association networks with linear output neurons and sigmoidal hidden neurons for classified image compression is carried out. Simulations and statistical analysis of this type of network have shown that, at convergence, the hidden neurons operate mainly in their linear region. The nearly linear behaviour of the hidden neurons is exploited in finding out the minimum number of hidden neurons needed to reconstruct image data within a certain error threshold. Four optimally structured auto-association networks are set up so that each network is trained to encode a certain variance-based class of image blocks. Results have shown excellent performance of the proposed architecture in reproducing high-quality images at a low bit rate.
Detection and localisation of hidden magnetic objects is studied. The concomitant disturbance of the earth magnetic field is exploited. Magnetic fields and or gradients are measured with a number of sensors. Two point...
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Detection and localisation of hidden magnetic objects is studied. The concomitant disturbance of the earth magnetic field is exploited. Magnetic fields and or gradients are measured with a number of sensors. Two point models for magnetic fields are selected for the analysis: the magnetic dipole and its quadrupole extension. It is shown how to apply least squares analysis of the data in order to detect and localise magnetised objects. The theoretical framework is tested in simulations, including noise. Two methods to do the minimisation are chosen. The formalism performs well in the simulations. Detection and localisation of the model objects is successful. No decisive differences in performance of the two minimisation methods are found. Results for the position parameters are reliable in any case. We have evaluated the goodness of fit in our simulations. Theoretical and simulated chi-square distributions are compared for different fit models and noise distributions with satisfactory results. The obtained parameter distributions are Gaussian. Measurements are performed to test the algorithms on real data collected for three ferromagnetic objects: a magnet, pipe and block. The magnet is easily localised, the pipe reasonably well. The block cannot be localised: its field variations cannot be distinguished from background field variations. Subsequent simulations show that using gradiometers improves the performances of the algorithms. The effects of a number of controllable parameters are evaluated.
A research roadmap to many of the best known, and most used, contributions to visual tracking is set out. The scope includes simple appearance models, active contours and spatiotemporal filtering, and briefly points t...
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A research roadmap to many of the best known, and most used, contributions to visual tracking is set out. The scope includes simple appearance models, active contours and spatiotemporal filtering, and briefly points to important further topics in tracking.
A NEW GENERATION OF vision ENABLED ROBOTS IS ABLE TO MAKE DECISIONS BASED ON SENSORY FEEDBACK FROM THEIR ENVIRONMENT. THEY CAN ADAPT TO PERHAPS ANY MANUFACTURING SCENARIO.
A NEW GENERATION OF vision ENABLED ROBOTS IS ABLE TO MAKE DECISIONS BASED ON SENSORY FEEDBACK FROM THEIR ENVIRONMENT. THEY CAN ADAPT TO PERHAPS ANY MANUFACTURING SCENARIO.
In the 1980s, machine vision surface inspection developed a bad name, because many application demands were greater than available software and hardware techniques. However, the situation has now changed. vision syste...
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In the 1980s, machine vision surface inspection developed a bad name, because many application demands were greater than available software and hardware techniques. However, the situation has now changed. vision systems easily out-perform human inspection. The pc has become the choice of system platforms for running a surface inspection vision system.
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