Search results for "Convolution"
showing 10 items of 334 documents
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
2016
This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative networks for efficient texture synthesis. While deep neural network approaches have recently demonstrated remarkable results in terms of synthesis quality, they still come at considerable computational costs (minutes of run-time for low-res images). Our paper addresses this efficiency issue. Instead of a numerical deconvolution in previous work, we precompute a feed-forward, strided convolutional network that captures the feature statistics of Markovian patches and is able to directly generate outputs of arbitrary dimensions. Such network can directly decode brown noise to realistic textu…
Optoelectronic morphological image processor.
2009
A morphological optoelectronic image processor based on the threshold decomposition concept is described and demonstrated. Binary slices of a gray-scale input image are optically convolved with a binary structuring element of arbitrary size and shape in a noncoherent convolver. The slices are displayed on a liquid-crystal spatial light modulator of 320 × 264 pixels. The kernels are implemented as modifications of the system impulse response. The processor’s convolution patterns are recorded with a CCD camera and fed into a PC by a frame grabber. Subsequent elementary morphological operations are looped. Examples of processing an input image of 256 × 256 pixels and 16 gray levels with kernel…
2021
Abstract We propose a signal deconvolution procedure for imaging spectrometer data, where a measured point spread function (PSF) is deconvolved itself before being used for deconvolution of the signal. We evaluate the effectiveness of our procedure for improvement of the spatio-spectral signal, as well as our target application, i.e. estimation of sun-induced fluorescence (SIF). Imaging spectrometers are well established instruments for remote sensing. When used for scientific purposes these instruments are usually calibrated on a regular basis. In our case the point spread function of the optics is measured in an elaborate procedure with a tunable monochromator point light source. PSFs are…
A Model for the Description, Simulation, and Deconvolution of Skewed Chromatographic Peaks
1997
A family of models is proposed for the description of skewed chromatographic peaks, based on the modification of the standard deviation of a pure Gaussian peak, by the use of a polynomial function, h(t) = He-(1/2)([t-tR]/[s0+s1(t-tR)+s2(t-tR)2+...])2, where H and tR are the height and time at the peak maximum, respectively. The model has demonstrated a high flexibility with peaks of a wide range of asymmetry and can be used to accurately predict the profile of asymmetrical peaks, using the values of efficiency and asymmetry factor measured on experimental chromatograms. This possibility permits the simulation of chromatograms and the optimization of the separation of mixtures of compounds p…
Diagonal space time hadamard codes with erasure decoding algorithm
2005
A major challenge in the area of space time (ST) codes is to find codes suitable for efficient decoding, thus overcoming the problem of many existing ST code designs which require maximum-likelihood (ML) decoding. A solution could be to apply single-input single-output (SISO) channel codes and theory over temporal channel fading to the multi-input single-output (MISO) code construction and classical suboptimum decoding methods. For these purposes, an ST code construction which allows the use of efficient decoding algorithms is described. We propose a concatenated code, where the inner code is the diagonal ST Hadamard (D-STH) code with Paley constructions and the outer code is an algebraic b…
Convolutional Neural Network-Based Human Movement Recognition Algorithm in Sports Analysis
2021
In order to analyse the sports psychology of athletes and to identify the psychology of athletes in their movements, a human action recognition (HAR) algorithm has been designed in this study. First, a HAR model is established based on the convolutional neural network (CNN) to classify the current action state by analysing the action information of a task in the collected videos. Secondly, the psychology of basketball players displaying fake actions during the offensive and defensive process is investigated by combining with related sports psychological theories. Then, the psychology of athletes is also analysed through the collected videos, so as to predict the next response action of the …
Hyperspectral image classification using CNN: Application to industrial food packaging
2021
Abstract During food tray packaging, some contamination may exist due to the presence of undesired objects. It is essential to detect anomalies during the packaging process in order to discard the faulty tray and avoid human consumption. This study demonstrates the on-line classification feasibility when using hyperspectral imaging systems for real-time food packaging control by using Convolutional Neural Networks (CNN) as a classifier in heat-sealed food trays. A hyperspectral camera is used to capture individual food tray information and fed to a CNN classifier to detect faulty food trays with object contamination. The proposed system is able to detect up to eleven different contamination…
On symplectically rigid local systems of rank four and Calabi–Yau operators
2013
AbstractWe classify all Sp4(C)-rigid, quasi-unipotent local systems and show that all of them have geometric origin. Furthermore, we investigate which of those having a maximal unipotent element are induced by fourth order Calabi–Yau operators. Via this approach, we reconstruct all known Calabi–Yau operators inducing an Sp4(C)-rigid monodromy tuple and obtain closed formulae for special solutions of them.
Invariant Markov semigroups on quantum homogeneous spaces
2019
Invariance properties of linear functionals and linear maps on algebras of functions on quantum homogeneous spaces are studied, in particular for the special case of expected coideal *-subalgebras. Several one-to-one correspondences between such invariant functionals are established. Adding a positivity condition, this yields one-to-one correspondences of invariant quantum Markov semigroups acting on expected coideal *-subalgebras and certain convolution semigroups of states on the underlying compact quantum group. This gives an approach to classifying invariant quantum Markov semigroups on these quantum homogeneous spaces. The generators of these semigroups are viewed as Laplace operators …