The detection of image segmented objects in video sequences is constrained by the a priori information available with a classifier. An object recogniser labels image regions based on texture and shape information abou...
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ISBN:
(纸本)0819435805
The detection of image segmented objects in video sequences is constrained by the a priori information available with a classifier. An object recogniser labels image regions based on texture and shape information about objects for which historical data is available. The introduction of a new object would culminate in its misclassification as the closest possible object known to the recogniser. neuralnetworks can be used to develop a strategy to automatically recognise new objects in image scenes that can be separated from other data for manual labelling. In this paper, one such strategy is presented for natural scene analysis of FLIR images. Appropriate threshold tests for classification are developed for separating known from unknown information. The results show that very high success rates can be obtained using neuralnetworks for the labelling of new objects in scene analysis.
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.
Systems for processing high resolution images need to be fast, compact, and efficient. imageprocessing systems that incorporate optics into its architecture can provide the speed and potentially the compactness to me...
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ISBN:
(纸本)081944815X
Systems for processing high resolution images need to be fast, compact, and efficient. imageprocessing systems that incorporate optics into its architecture can provide the speed and potentially the compactness to meet the demands of analyzing images. In this paper a hybrid approach to image analysis using Winner Take All neural network dynamics with optical and electronic implementation is discussed. Resulting images from the system simulations are explored for use in object and background discrimination for image segmentation tasks.
This paper describes a range of neural signal processing methods employed for B-Scan ultrsonic image enhancement and material identification. All approaches assume no a-priori knowledge of the environment. The potenti...
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ISBN:
(纸本)0852965311
This paper describes a range of neural signal processing methods employed for B-Scan ultrsonic image enhancement and material identification. All approaches assume no a-priori knowledge of the environment. The potential of a neural, sonar based material identification system has also been established.
Digital image correlation has been used to measure microscopic deformation in thermally stressed microelectronics devices. Displacement precisions of better than 0.03 pixels have been achieved by combining nonintegral...
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ISBN:
(纸本)0780367251
Digital image correlation has been used to measure microscopic deformation in thermally stressed microelectronics devices. Displacement precisions of better than 0.03 pixels have been achieved by combining nonintegral pixel shifting of subimages and artificialneuralnetworks (ANNs). The ANNs are trained to estimate the subpixel element of the object displacement from the digital correlation. Although similar accuracies can be obtained by curve-fitting to the correlation peaks and differentiating, the neural approach has the advantage that it allows fast subpixel displacement analysis over a range of object textures without knowledge of the analytical form of the correlation peaks.
Imaging techniques have been applied to a number of applications, such as translation and classification problems in medicine and defence. This paper examines the application of imaging techniques in digital forensics...
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ISBN:
(纸本)9780819465009
Imaging techniques have been applied to a number of applications, such as translation and classification problems in medicine and defence. This paper examines the application of imaging techniques in digital forensics investigation using neuralnetworks. A review of applications of digital imageprocessing is presented, whiles a Pedagogical analysis of computer forensics is also highlighted. A data set describing selected images in different forms are used in the simulation and experimentation.
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
The proposed system for CT image reconstruction is structured with three layers of neurons. In our previous work, we used the resilient backpropagation(Rprop) instead of the straight, BP to modify the network weights....
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ISBN:
(纸本)0819444081
The proposed system for CT image reconstruction is structured with three layers of neurons. In our previous work, we used the resilient backpropagation(Rprop) instead of the straight, BP to modify the network weights. The basic idea is to minimize the error between the projections of the original image and of the reconstructed image. We noticed that the system performance depends oil the initial status of the network. Based on this observation, we propose a novel approach for choosing optimal values of the connection weights. The experimental results indicate that the new method can find a satisfactory solution despite that only a few projections are available.
This paper presents a learning-based vehicle control system capable of navigating autonomously. Our approach is based on imageprocessing, road and navigable area recognition, template matching classification for navi...
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This paper presents a learning-based vehicle control system capable of navigating autonomously. Our approach is based on imageprocessing, road and navigable area recognition, template matching classification for navigation control, and trajectory selection based on GPS waypoints. The vehicle follows a trajectory defined by GPS points avoiding obstacles using a single monocular camera and maintaining the vehicle in the road lane. Different parts of the image, obtained from the camera, are classified into navigable and non-navigable regions of the environment using neuralnetworks. They provide steering and velocity control to the vehicle. Several experimental tests have been carried out under different environmental conditions to evaluate the proposed techniques. (C) 2013 Elsevier B.V. All rights reserved.
Deep learning methods have been effectively used to provide great improvement in various research fields such as machine learning, imageprocessing and computer vision. One of the most frequently used deep learning me...
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ISBN:
(纸本)9781509064946
Deep learning methods have been effectively used to provide great improvement in various research fields such as machine learning, imageprocessing and computer vision. One of the most frequently used deep learning methods in imageprocessing is the convolutional neuralnetworks. Compared to the traditional artificialneuralnetworks, convolutional neuralnetworks do not use the predefined kernels, but instead they learn data specific kernels. This property makes them to be used as feature extractors as well. In this study, we used a convolutional neural network including 60 million parameters and 650 thousand neurons to extract features to be used for image retrieval. The architecture of the network consists of five convolutional layers and three fully-connected layers. Extracted features, in comparison with Fisher vectors - which are one of the most widely used representation types - are tested on UCMerced Land Use dataset in terms of retrieval accuracies by using different hashing methods. Experimental results demostrate the superiority of the CNN features.
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