In this paper, we describe a genetic learning neural network system to vector quantize images directly to achieve data compression. The genetic learning algorithm is designed to have two levels: One is at the level of...
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ISBN:
(纸本)0819424412
In this paper, we describe a genetic learning neural network system to vector quantize images directly to achieve data compression. The genetic learning algorithm is designed to have two levels: One is at the level of code words in which each neural network is updated through reproduction every time an input vector is processed. The other is at the level of code-books in which five neuralnetworks are included in the gene pool. Extensive experiments on a group of image samples show that the genetic algorithm outperforms other vector quantization algorithms which include competitive learning, frequency sensitive learning and LBG.
Although done nearly effortlessly by humans, digital systems cannot easily recognize images or predictions from recent observations. Tackling these limitations by proposing novel algorithms to improve the performance ...
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Although done nearly effortlessly by humans, digital systems cannot easily recognize images or predictions from recent observations. Tackling these limitations by proposing novel algorithms to improve the performance of imageprocessing would have widespread implications in a variety of fields, including robotics, manufacturing, biomedicine, and automation. To provide a computer with this combined ability and transform it into an intelligent system, an algorithm must combine memory with an image decomposition procedure. artificialneuralnetworks (ANNs) are algorithms that aim to solve tasks such as classification, clustering, pattern recognition, and prediction by resembling brain connections. Specifically, three ANNs have excelled in specific areas: deep neuralnetworks (DNNs), which use intrinsic connections to create prediction maps;long short-term memory neuralnetworks (LSTMs), which use recurrent connections to emulate a type of memory;and convolutional neuralnetworks (CNNs), which can decompose complex data through layers for simpler analysis. Although these algorithms can solve certain tasks of image sequence prediction, they cannot easily solve entire problems on their own. Nevertheless, combining these networks may enable solving such problems with ease. Thus, this article evaluates the combination of ANNs into two novel algorithms developed with the aim of improving image sequence prediction: (i) a combination of CNNs and LSTMs to form a CLNN and (ii) a combination of CNNs, LSTMs, and DNNs to form a CLDNN. Although the developed algorithms require a longer training time, they require less training epochs to have better accuracy than their predecessors. Furthermore, both developed methods were capable of accurately performing the image sequence prediction task, outperforming each individual method, as well as predicting longer and greater numbers of sequences correctly. Overall, the developed algorithms were able to better decompose inputs, remember prev
Interesting perspectives in imageprocessing with cellular neuralnetworks can be emphasized from an investigation into the internal states dynamics of the model. Most of the cellular neuralnetworks design methods in...
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ISBN:
(纸本)3540660682
Interesting perspectives in imageprocessing with cellular neuralnetworks can be emphasized from an investigation into the internal states dynamics of the model. Most of the cellular neuralnetworks design methods intend to control internal states dynamics in order to pet a straight processing result. The present one involves some kind of internal states preprocessing so as to finally achieve processing otherwise unrealizable. applications of this principle to the building of complex processing schemes, gray level preserving segmentation and selective brightness variation are presented.
Our goal in this article is to present a quantitative study about speech recognition and the inherent problems of its applications and the computer processing. Our approach is characterized by independent speaker and ...
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ISBN:
(纸本)0819425915
Our goal in this article is to present a quantitative study about speech recognition and the inherent problems of its applications and the computer processing. Our approach is characterized by independent speaker and we made use of pre-processing the concept as Wavelets Transform and as pattern recognition an artificialneural Network (ANN - Multilayer Perceptron -Backpropagation Algorithm).
Scene analysis is an important area of research with the aim of identifying objects and their relationships in natural scenes. MINERVA benchmark has been recently introduced in this area for testing different image pr...
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Scene analysis is an important area of research with the aim of identifying objects and their relationships in natural scenes. MINERVA benchmark has been recently introduced in this area for testing different imageprocessing and classification schemes. In this paper we present results on the classification of eight natural objects in the complete set of 448 natural images using neuralnetworks. An exhaustive set of experiments with this benchmark has been conducted using four different segmentation methods and five texture-based feature extraction methods. The results in this paper show the performance of a neural network classifier on a tenfold cross-validation task. On the basis of the results produced, we are able to rank how well different image segmentation algorithms are suited to the task of region of interest identification in these images, and we also see how well texture extraction algorithms rank on the basis of classification results.
Detecting objects in images containing strong clutter is an important issue in a variety of applications such as medical imaging and automatic target recognition. artificialneuralnetworks are we used as non-parametr...
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ISBN:
(纸本)0819427470
Detecting objects in images containing strong clutter is an important issue in a variety of applications such as medical imaging and automatic target recognition. artificialneuralnetworks are we used as non-parametric pattern recognizers to cope with different problems due to their inherent ability to learn from training data. In this paper we propose a neural approach based on the Random neural Network (RNN) model (Gelenbe 1989, 1990, 1991, 1993(4,5,7,6)), to detect shaped targets with the help of multiple neuralnetworks whose outputs are combined for making decisions.
Many studies for computer-based chromosome analysis using artificialneural network (ANN) have shown that it is possible to classify chromosomes into 24 subgroups. It is important to select optimum features for traini...
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ISBN:
(纸本)0819444081
Many studies for computer-based chromosome analysis using artificialneural network (ANN) have shown that it is possible to classify chromosomes into 24 subgroups. It is important to select optimum features for training the ANN. Our purpose was to select features that had the low classification error and the best ability for human chromosome classification. We applied the medial axis transformation for the medial line, extended the line to the boundary and obtained relative length, relative area and centromeric index. The Giemsa-stained human chromosome has a sequence of banding pattern that is perpendicular to the medial axis of the chromosome. Density profile is a one-dimensional graph of the banding pattern property of the chromosome computed at a sequence of points along the possibly curved chromosome medial axis. Some studied used relative length, centromeric index and 62 density profile as features, but we prepared two data sets as features that one set was relative length, centromeric index and 80 density profile considered No. I chromosome's length and the other was relative length, centromeric index, the 80 density profile and relative area and compared classification error of each set. We found that the classification error showed to be decreased by adding relative area to the other features.
neuralnetworks and expert systems provide different ways to reduce the programming effort required to build complex systems. Adaptive neuralnetworks are programmed merely by training them with examples. Rule-based e...
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In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid develop...
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In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication ***,for IoT devices,however,processingimage information efficiently in real time is an essential task for the rapid transmission of semantic *** the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to *** contrast,the Pulse Coupled neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image ***,the parameters of PCNN are determined by trial and error,which limits its *** overcome this limitation,an Improved Pulse Coupled neuralnetworks(IPCNN)model is proposed in this *** IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of *** segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation *** IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.
This study focuses on the development of a robotic arm with three degrees of freedom designed for different applications, especially in industry, where the main contribution is the use of a low-cost robotic arm manipu...
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This study focuses on the development of a robotic arm with three degrees of freedom designed for different applications, especially in industry, where the main contribution is the use of a low-cost robotic arm manipulator, which with the proper adjustments, mainly of speed, aims to add the concept of collaborative robotics in its most advanced versions. This study employs some classical principles of robotics, such as acquisition, imageprocessing, and object manipulation. In this context, artificialneuralnetworks were used for two essential functionalities: camera calibration and inverse kinematics resolution. From the results obtained with the use of the artificialneuralnetworks (ANNs), it was possible to control the trajectory of the servo motors with the aid of software support. Also, by employing ANNs and using computer vision techniques, the robotic manipulator achieves the objectives of manipulating objects in space with a relatively acceptable error rate, considering the low-cost proposal applied. This manipulator, at first, has a didactic purpose, but the concepts and applications can be used in different areas of the industry.
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