提出了一种基于特征提取和验证相结合的人脸检测方法。
Based on facial features extraction and verification, we present a new human face detection method.
提出了一种基于纹理特征提取的图像处理技术和神经网络结合进行钢丝绳检测的新方法。
A new method of picking up the texture characteristics and using neural network to inspect the condition of steel wire rope is provided.
本文将这两种技术有机地结合起来，提出了一种新的掌纹特征提取方法。
These two techniques are effectively combined in the paper, and a new palmprint feature extraction approach is proposed.
在应用本文所采用的几种信号特征提取方法的基础上，结合2d -HMM模型，验证了基于2d - HMM的离心泵故障诊断方法的有效性。
This paper verified the effectiveness of the fault diagnosis method based on 2d-hmm for centrifugal pump, by applying several signal feature extraction methods combining 2d-hmm model.
文章提出了利用短时能量分析与人的主观听觉相结合的方法进行船舶辐射噪声特征提取。
This paper applies a short-term energy analysis method and subjective hearing characteristics to analyze ship-radiated noise.
特征提取采用底层信息和高层语意相结合的方法，弥补了相互的不足；
Take high level semantic information and low level picture visual characters as design patent characters, the two kinds of information cut down each other's shortage.
提出曲元分析(CCA)和自组织特征映射(SOFM)相结合的方法用于轴承的故障诊断特征提取。
The combination of curvilinear component analysis (CCA) and self-organizing feature map (SOFM) were applied to a diagnosis for fault feature extraction of bearing.
以理论分析和对比实验相结合的方式对上述分析方法进行信号特征提取可行性进行验证。
Methods in this article are proved feasible to be used in signal feature extracting by theoretical analysis and comparing experimentation.
利用被动声纳目标辐射噪声特征主要集中在低频部分的特点，结合听觉系统识别声音信号的原理，提出了一种被动声纳目标临界频带频谱能量的特征提取方法。
By studying the principle of hearing system in identification of sound signal, a method of feature extraction for passive sonar target based on critical band spectrum energy is proposed.
通过分析研究虹膜上丰富的纹理特征，结合不同的特征提取方法和分类器可以实现虹膜的身份认证。
Through analyzing the iris feature and combining with different feature extraction methods, we can realize iris recognition.
本论文结合几何特征提取方法和神经网络方法，提出了一种新的人脸识别方法。
A new approach to face recognition combining geometry feature distilling and neural networks methods is proposed.
实验结果表明，结合核主成份分析的特征提取，支持向量机方法是一种很有前景的多目标图像分割技术。
This paper investigates the segmentation of multi-target image based on SVM approach combining feature extraction of kernel PCA.
根据新的目标函数，设计了一种重要点和自底向上分割相结合的时间序列分段线性化趋势特征提取方法。
The existing algorithms to extract trend features based on time series piecewise linearization representation cannot extract completely correct basic trend features of time series.
最后，提出了一种基于子频带特征提取和二叉树结构支持向量机相结合的电能质量多分类方法。
Finally, a method based on sub-band feature extraction and Support Vector Machine with Binary Tree Architecture (SVM-BTA) is presented for power quality disturbances multi-classification.
该方法在对SPOT5遥感影像进行纹理特征提取的基础上，构建了结合多窗口纹理的SVM模型。
In this method, the SVM classification model combined with texture analysis is established on the basis of texture extraction from SPOT5 remote sensing image.
采用LBP算法与直接线性判别分析算法相结合的人耳特征提取方法在识别率方面与原始图像特征提取方法相比有明显提升。
The algorithm based on LBP and direct linear discriminant analysis have distinct advance than the extraction algorithm using the original image in recognition rate.
采用LBP算法与直接线性判别分析算法相结合的人耳特征提取方法在识别率方面与原始图像特征提取方法相比有明显提升。
The algorithm based on LBP and direct linear discriminant analysis have distinct advance than the extraction algorithm using the original image in recognition rate.
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