Search results for "Linear"
showing 10 items of 7165 documents
Classification Boundary Approximation by Using Combination of Training Steps for Real-Time Image Segmentation
2007
We propose a method of real-time implementation of an approximation of the support vector machine decision rule. The method uses an improvement of a supervised classification method based on hyperrectangles, which is useful for real-time image segmentation. We increase the classification and speed performances using a combination of classification methods: a support vector machine is used during a pre-processing step. We recall the principles of the classification methods and we evaluate the hardware implementation cost of each method. We present our learning step combination algorithm and results obtained using Gaussian distributions and an example of image segmentation coming from a part …
Real-time characterization of aspect flaws on warped surface by artificial vision
1997
Artificial vision is an efficient means of assuring the quality of a certain class of products. The vision system must respect the industrial constraints, in particular, the production rate. The geometrical features of flaws are pertinent information used for the acceptance of the controlled product. This article presents a real-time algorithm for the geometrical characterization of defects located on warped objects. The algorithms described enable the characterization of defects by their size and their 2-D shape. Both parameters are calculated in real time by simple reference to a look-up table. The 2-D shape is obtained by a geometrical transform and an interpolation. The efficiency of th…
Segmentation of Hyperspectral Images for the Detection of Rotten Mandarins
2008
The detection of rotten citrus in packing lines is carried out manually under ultraviolet illumination, which is dangerous for workers. Light emitted by the rotten region of the fruit due to the ultraviolet-induced fluorescence is used by the operator to detect the damages. This procedure is required because the low contrast between the damaged and sound skin under visible illumination difficult their detection. We study a set of techniques aimed to detect rottenness in citrususing visible and near infrared lighting trough an hyperspectral imaging system. Methods for selecting a proper set of wavelengths are investigated such as correlation analysis, mutual information, stepwise or genetic …
Multilevel system optimisation via nonlinearity management
2006
Nonlinearity management is explored as a multilevel tool to obtain maximum transmission reach in a WDM system. A technique for the fast calculation of the optimal dispersion pre-compensation in systems with distributed amplification is proposed.
Manifold Learning with High Dimensional Model Representations
2020
Manifold learning methods are very efficient methods for hyperspectral image (HSI) analysis but, unless specifically designed, they cannot provide an explicit embedding map readily applicable to out-of-sample data. A common assumption to deal with the problem is that the transformation between the high input dimensional space and the (typically low) latent space is linear. This is a particularly strong assumption, especially when dealing with hyperspectral images due to the well-known nonlinear nature of the data. To address this problem, a manifold learning method based on High Dimensional Model Representation (HDMR) is proposed, which enables to present a nonlinear embedding function to p…
Reconfigurable photonic routers based on multimode interference couplers
2015
We present a design approach for compact reconfigurable light routers with N access waveguides (WGs) based on multimode interference (MMI) couplers. The proposed devices comprise two MMI couplers, which are employed as power splitters and combiners, respectively, linked by an array of N single-mode WGs. When the effective refractive index of the WGs is modulated with the proper relative optical phase difference, the light can switch paths between the preset output channel and the remaining output WGs. Taking advantage of the transfer phases between the access ports of the MMI couplers, we derive very simple phase relations between the modulated WGs that enable the reconfiguration of the out…
Analyse des Visuellen Klassifikationssystems Durch Detektionsexperimente
1977
Summary Experiments on recognizing statistically distorted patterns show that the human visual system operates as a linear classifier. The spatial frequency range, within which features are extracted, is determined by the coupling in the area of sharpest vision (2°). The relevant features for classifying patterns are not produced by isotropic filtering
Hidden attractors on one path : Glukhovsky-Dolzhansky, Lorenz, and Rabinovich systems
2017
In this report, by the numerical continuation method we visualize and connect hidden chaotic sets in the Glukhovsky-Dolzhansky, Lorenz and Rabinovich systems using a certain path in the parameter space of a Lorenz-like system.
State Space-Vector Model of Linear Induction Motors Including End-effects and Iron Losses - Part II: Model Identification and Results
2020
This is the second part of an article, divided into two parts, dealing with the definition of a space-vector dynamic model of the linear induction motor (LIM) taking into consideration both the dynamic end-effects and the iron losses as well as the offline identification of its parameters. This second part is devoted to the description of an identification technique that has been suitably developed for the estimation of the electrical parameters of the LIM dynamic model accounting for both the dynamic end-effects and iron losses. Such an identification technique is strictly related to the state formulation of the proposed model and exploits genetic algorithms for minimizing a suitable cost …
The Elephant in the Machine: Proposing a New Metric of Data Reliability and its Application to a Medical Case to Assess Classification Reliability
2020
In this paper, we present and discuss a novel reliability metric to quantify the extent a ground truth, generated in multi-rater settings, as a reliable basis for the training and validation of machine learning predictive models. To define this metric, three dimensions are taken into account: agreement (that is, how much a group of raters mutually agree on a single case)