Based on a unified description of neural algorithms for time-independent patternrecognition, the authors discuss the generalization ability of a three-layer perceptron for recurrent backpropagation depending on the n...
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Based on a unified description of neural algorithms for time-independent patternrecognition, the authors discuss the generalization ability of a three-layer perceptron for recurrent backpropagation depending on the number of learning epochs and the steepness of the neurons' threshold function. Moreover, the suitability of recurrent and feedforward backpropagation learning algorithms for implementation on multiprocessor systems is investigated. In contrast to the common belief that recurrent back-propagation is more computationally intensive than feedforward backpropagation, the present results for optical character recognition indicate that this need not be the case if the steepness of the threshold function is appropriately chosen. This makes recurrent backpropagation a suitable candidate for time-independent patternrecognition.< >
Emergence of novel techniques, such as the invention of MS Kinect, enables reliable extraction of human skeletons from action videos. Taking skeleton data as inputs, we propose an approach in this paper to extract the...
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Emergence of novel techniques, such as the invention of MS Kinect, enables reliable extraction of human skeletons from action videos. Taking skeleton data as inputs, we propose an approach in this paper to extract the discriminative patterns for efficient human action recognition. Each action is considered to consist of a series of unit actions, each of which is represented by a pattern. Given a skeleton sequence, we first automatically extract the key-frames for unit actions, and then label them as different patterns. We further use a statistical metric to evaluate the discriminative capability of each pattern, and define the bag of reliable patterns as local features for action recognition. Experimental results show that the extracted local descriptors could provide very high accuracy in the action recognition, which demonstrate the efficiency of our method in extracting discriminative patterns.
In this paper, a new integration scheme with multilayer perceptron (MLP) networks is proposed to solve handwritten Chinese character recognition problem. The idea of meta-synthesis is emphasized in this scheme, human ...
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ISBN:
(纸本)0769507506
In this paper, a new integration scheme with multilayer perceptron (MLP) networks is proposed to solve handwritten Chinese character recognition problem. The idea of meta-synthesis is emphasized in this scheme, human intelligence and computer capabilities are combined together through a procedure of two-step supervised learning. Compared with previous integration schemes, this scheme has much better performance and provides a promising way of applying MLP to large vocabulary classification.
A novel feature extraction method, namely monogenic binary pattern (MBP), is proposed in this paper based on the theory of monogenic signal analysis, and the histogram of MBP (HMBP) is subsequently presented for robus...
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ISBN:
(纸本)9781424475421
A novel feature extraction method, namely monogenic binary pattern (MBP), is proposed in this paper based on the theory of monogenic signal analysis, and the histogram of MBP (HMBP) is subsequently presented for robust face representation and recognition. MBP consists of two parts: one is monogenic magnitude encoded via uniform LBP, and the other is monogenic orientation encoded as quadrant-bit codes. The HMBP is established by concatenating the histograms of MBP of all sub-regions. Compared with the well-known and powerful Gabor filtering based LBP schemes, one clear advantage of HMBP is its lower time and space complexity because monogenic signal analysis needs fewer convolutions and generates more compact feature vectors. The experimental results on the AR and FERET face databases validate that the proposed MBP algorithm has better performance than or comparable performance with state-of-the-art local feature based methods but with significantly lower time and space complexity.
The extraction of essential news elements through the 5W1H framework (What, When, Where, Why, Who, and How) is critical for event extraction and text summarization. The advent of Large language models (LLMs) such as C...
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ISBN:
(纸本)9798350359329;9798350359312
The extraction of essential news elements through the 5W1H framework (What, When, Where, Why, Who, and How) is critical for event extraction and text summarization. The advent of Large language models (LLMs) such as ChatGPT presents an opportunity to address language-related tasks through simple prompts without fine-tuning models with much time. While ChatGPT has encountered challenges in processing longer news texts and analyzing specific attributes in context, especially answering questions about What, Why, and How. The effectiveness of extraction tasks is notably dependent on highquality human-annotated datasets. However, the absence of such datasets for the 5W1H extraction increases the difficulty of finetuning strategies based on open-source LLMs. To address these limitations, first, we annotate a high-quality 5W1H dataset based on four typical news corpora (CNN/DailyMail, XSum, NYT, RAMDS);second, we design several strategies from zero-shot/fewshot prompting to efficient fine-tuning to conduct 5W1H aspects extraction from the original news documents. The experimental results demonstrate that the performance of the fine-tuned models on our labelled dataset is superior to the performance of ChatGPT. Furthermore, we also explore the domain adaptation capability by testing the source-domain (e.g. NYT) models on the target domain corpus (e.g. CNN/DailyMail) for the task of 5W1H extraction.
In this study we propose a deformable patternrecognition method with CUDA implementation. In order to achieve the proper correspondence between foreground pixels of input and prototype images, a pair of distance maps...
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ISBN:
(纸本)9781424475421
In this study we propose a deformable patternrecognition method with CUDA implementation. In order to achieve the proper correspondence between foreground pixels of input and prototype images, a pair of distance maps are generated from input and prototype images, whose pixel values are given based on the distance to the nearest foreground pixel. Then a regularization technique computes the horizontal and vertical displacements based on these distance maps. The dissimilarity is measured based on the eight-directional derivative of input and prototype images in order to leverage characteristic information on the curvature of line segments that might be lost after the deformation. The prototype-parallel displacement computation on CUDA and the gradual prototype elimination technique are employed for reducing the computational time without sacrificing the accuracy. A simulation shows that the proposed method with the k-nearest neighbor classifier gives the error rate of 0.57% for the MNIST handwritten digit database.
Summary form only given. A study of the short-term memory requirements of temporal patternrecognition prompts the creation of a new model for neural computation. It is hypothesized that neural responses resemble hyst...
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Summary form only given. A study of the short-term memory requirements of temporal patternrecognition prompts the creation of a new model for neural computation. It is hypothesized that neural responses resemble hysteresis loops, instead of the simple sigmoid. The upper and lower halves of the hysteresis loop are described by two equations. Generalizing the two equations to two families of curves accommodates loops of various sizes. It is conjectured that this unit is capable of memorizing the entire history of its inputs.< >
Aiming at the aircraft target in visible light remote sensing image, this paper proposes a false alarm removal method for target detection under small sample training conditions. First, use data enhancement methods fo...
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This paper deals with the multi-objective definition of the feature selection problem for different patternrecognition domains. We use NSGA II the latest multi-objective algorithm developed for resolving problems of ...
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The present study aims 1. to automatically classify the heterogeneously perfused tumors using dynamic contrast-enhanced (DCE) MRI data from two patients diagnosed with malignant peripheral nerve sheath tumor (MPNST), ...
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