Registration of point clouds is required in the processing of large biological data sets. The tradeoff between computation time and accuracy of the registration is the main challenge in this task. We present a novel m...
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The paper addresses the problem of using Japanese candlestick methodology to analyze stock or forex market data by neural nets. Self organizing maps are presented as tools for providing maps of known candlestick forma...
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ISBN:
(纸本)9783642132070
The paper addresses the problem of using Japanese candlestick methodology to analyze stock or forex market data by neural nets. Self organizing maps are presented as tools for providing maps of known candlestick formations. They may be used to visualize these patterns, and as inputs for more complex trading decision systems. in that case their role is preprocessing, coding and pre-classification of price data. An example of a profitable system based on this method is presented. Simplicity and efficiency of training and network simulating algorithms is emphasized in the context of processing streams of market data.
A robust face descriptor is an essential component for a good facial expression recognition system. In this paper, we analyze the performance of a new feature descriptor, Local Directional pattern (LDP), for the repre...
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ISBN:
(纸本)9781424479948
A robust face descriptor is an essential component for a good facial expression recognition system. In this paper, we analyze the performance of a new feature descriptor, Local Directional pattern (LDP), for the representation of facial expressions. LDP features are obtained by computing the edge response values in all eight directions at each pixel position and then a code is generated according to the relative magnitude's strength. Thus each expression is represented as a distribution of LDP codes. Different machine learning techniques are compared using Cohn-Kanade facial expression database for classification. Extensive experiments explicate the superiority of the proposed LDP based descriptor over other existing well known descriptors.
With periocular biometrics gaining attention recently, the goal of this paper is to investigate the effectiveness of local appearance features extracted from the periocular region images for soft biometric classificat...
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Detecting fall is a particular important task in security monitoring and healthcare applications of sensor networks. However traditional approaches suffer from either a high false positive rate or high false negative ...
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ISBN:
(纸本)9781618399267
Detecting fall is a particular important task in security monitoring and healthcare applications of sensor networks. However traditional approaches suffer from either a high false positive rate or high false negative rate, especially when the collected sensor data are unbalanced. Therefore, there is a lack of tradeoff between false alarms and misses for many traditional data mining methods to be applied. To solve this problem a novel fall detection algorithm based on patternrecognition and human posture analysis is presented in this paper. It firstly extracts thirty temporal features from the original data traces for different length adaptation of samples, and then exploits Hidden Markov Model (HMM) to filter the noisy character data and reduce the dimension of feature vectors. After that, it performs a closer classification with one-class Support Vector Machine (OCSVM) to filter the high false positive samples, and finally applies posture analysis to counteract the effects of high false negative samples until a satisfying accuracy is achieved. Simulation with real data demonstrates that the proposed algorithm outperforms other existing approaches.
Given a set of discrete points in a 2D digital image containing noise, we formulate our problem as robust digital line fitting. More precisely, we seek the maximum subset whose points are included in a digital line, c...
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The paper introduces a framework for clustering data objects in a similarity-based context. The aim is to cluster objects into a given number of classes without imposing a hard partition, but allowing for a soft assig...
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In a 3D environment, when a 2D figure is pho- tographed from different camera angles, the pictures have a relation that approximates an affine transformation. Detecting an affine transformation is an important problem...
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In a 3D environment, when a 2D figure is pho- tographed from different camera angles, the pictures have a relation that approximates an affine transformation. Detecting an affine transformation is an important problem in the area of computer vision. The log-polar transform is known to be a model of the central-fovea visual sensor in a creature's vision. One study described a method to detect the affine parameter by using a log-polar transform: however, this transform is not invariant to obiect position. so it is necessary to find beforehand the corresponding characteristic point. In this studv. a new log-polar transform method which is invariant to obiect position chance and affine transform is described. One amplitude feature in the frequency domain, the orientation of the 2D figure is retained but chance of position is not retained, so the orientation can be detected regardless of the position of the figure. By combination with edge extraction, the affine parameter can be detected with little effect by a background chance.
Aim at the problems occurring in a least square method model and a neural network model for flatness patternrecognition, a new approach of flatness patternrecognition based on the variable metric chaos optimization ...
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ISBN:
(数字)9783642161674
ISBN:
(纸本)9783642161667
Aim at the problems occurring in a least square method model and a neural network model for flatness patternrecognition, a new approach of flatness patternrecognition based on the variable metric chaos optimization neural network is proposed to meet the demand of high-precision flatness control for cold strip mill. The model is shown to fit the actual data pricisely and to overcome several disadvantages of the conventional BP neural network. Namely:slow convergence, low accuracy and difficulty in finding the global optimum. A series of tests have been conducted based on the data of the actual flatness pattern. The simulation results show that the speed and accuracy of the flatness patternrecognition model are obviously improved.
Facial aging can degrade the face recognition performance dramatically. Traditional face recognition studies focus on dealing with pose, illumination, and expression (PIE) changes. Considering a large span of age diff...
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