Image processing with neural networks—a review

Egmont-Petersen, Michael; Ridder, Dick de; Handels, Heinz

doi:10.1016/s0031-3203(01)00178-9

Cited by 928 publications

(358 citation statements)

References 227 publications

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“…This means that they are suited to learning the salient characteristics of human perception [41] during the video quality estimation process. Existing image quality assessment schemes have demonstrated the benefits and feasibility of ANN-based models in predicting the quality level of images (not videos) [41][42][43][44][45][46].…”

Section: Related Workmentioning

confidence: 99%

A real-time video quality estimator for emerging wireless multimedia systems

et al. 2014

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Wireless Mesh Networks (WMNs) are increasingly deployed to enable thousands of users to share, create, and access live video streaming with different characteristics and content, such as video surveillance and football matches. In this context, there is a need for new mechanisms for assessing the quality level of videos because operators are seeking to control their delivery process and optimize their network resources, while increasing the user's satisfaction. However, the development of in-service and non-intrusive Quality of Experience assessment schemes for real-time Internet videos with different complexity and motion levels, Group of Picture lengths, and characteristics, remains a significant challenge. To address this issue, this article proposes a nonintrusive parametric real-time video quality estimator, called MultiQoE that correlates wireless networks' impairments, videos' characteristics, and users' perception into a predicted Mean Opinion Score. An instance of MultiQoE was implemented in WMNs and performance evaluation results demonstrate the efficiency and accuracy of MultiQoE in predicting the user's perception of live video streaming services when compared to subjective, objective, and well-known parametric solutions.

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Section: Related Workmentioning

confidence: 99%

A real-time video quality estimator for emerging wireless multimedia systems

et al. 2014

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“…Let the usefulness of 2LP (16) during the training be measured with its performance function "mse" according to the sum of squared errors [8], [9], [22], [23]. Finally, having preset the minimum performance gradient to 6 10  , let the number of epochs be 5000 in order to prevent long-dragging convergence of TP and to shorten the ultimate TP period for each pass.…”

Section: Description Of 2lp (6) Configuration For M6080ismentioning

confidence: 99%

Classifying Scaled-Turned-Shifted Objects with Optimal Pixel-to-Scale-Turn-Shift Standard Deviations Ratio in Training 2-Layer Perceptron on Scaled-Turned-Shifted 4800-Featured Objects under Normally Distributed Feature Distortion

Romanuke

2017

Electrical, Control and Communication Engineering

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-The problem of classifying diversely distorted objects is considered. The classifier is a 2-layer perceptron capable of classifying greater amounts of objects in a unit of time. This is an advantage of the 2-layer perceptron over more complex neural networks like the neocognitron, the convolutional neural network, and the deep learning neural networks. Distortion types are scaling, turning, and shifting. The object model is a monochrome 60 × 80 image of the enlarged English alphabet capital letter. Consequently, there are 26 classes of 4800-featured objects. Training sets have a parameter, which is the ratio of the pixel-to-scale-turn-shift standard deviations, which allows controlling normally distributed feature distortion. An optimal ratio is found, at which the performance of the 2-layer perceptron is still unsatisfactory. Then, the best classifier is further trained with additional 438 passes of training sets by increasing the training smoothness tenfold. This aids in decreasing the ultimate classification error percentage from 35.23 % down to 12.92 %. However, the expected practicable distortions are smaller, so the percentage corresponding to them becomes just 1.64 %, which means that only one object out of 61 is misclassified. Such a solution scheme is directly applied to other classification problems, where the number of features is a thousand or a few thousands by a few tens of classes.

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“…After years of development, neural network has been a popular tool for time-series prediction [11,12], feature extraction [13,14], pattern recognition [15,16], and classification [17,18]. Due to the ability of wavelet transformation for revealing the property of function in localize region, different types of wavelet neural network (WNN) which combine wavelets with neural networks have been proposed [19,20].…”

Section: Introductionmentioning

confidence: 99%

Radial Wavelet Neural Network with a Novel Self-Creating Disk-Cell-Splitting Algorithm for License Plate Character Recognition

Cheng

Bai

et al. 2015

Entropy

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Abstract:In this paper, a novel self-creating disk-cell-splitting (SCDCS) algorithm is proposed for training the radial wavelet neural network (RWNN) model. Combining with the least square (LS) method which determines the linear weight coefficients, SCDCS can create neurons adaptively on a disk according to the distribution of input data and learning goals. As a result, a disk map is made for input data as well as a RWNN model with proper architecture and parameters can be decided for the recognition task. The proposed SCDCS-LS based RWNN model is employed for the recognition of license plate characters. Compared to the classical radial-basis-function (RBF) network with K-means clustering and LS, the proposed model can make a better recognition performance even with fewer neurons.

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Image processing with neural networks—a review

Cited by 928 publications

References 227 publications

A real-time video quality estimator for emerging wireless multimedia systems

A real-time video quality estimator for emerging wireless multimedia systems

Classifying Scaled-Turned-Shifted Objects with Optimal Pixel-to-Scale-Turn-Shift Standard Deviations Ratio in Training 2-Layer Perceptron on Scaled-Turned-Shifted 4800-Featured Objects under Normally Distributed Feature Distortion

Radial Wavelet Neural Network with a Novel Self-Creating Disk-Cell-Splitting Algorithm for License Plate Character Recognition

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