A Novel Building Worker Detection based on Cross Feature Pyramid Network
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The safety of building worker is important in construction industry. In order to realize the worker intelligent management, a novel building worker detection based on cross feature pyramid network is proposed to come true the real-time detection of workers. The proposed cross feature pyramid network uses cross feature of different layers to obtain robust feature of workers. The extracted feature may include high-level and low-level features. Experimental results indicate that the proposed algorithm obtain better performance than the traditional feature pyramid network.Keywords:
Pyramid (geometry)
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Accurate feature extraction plays a vital role in the fields of machine learning, pattern recognition and image processing. Feature extraction methods based on principal component analysis (PCA), independent component analysis (ICA), and linear discriminant analysis (LDA) are capable of improving the performances of classifiers. In this paper, we propose two features extraction approaches, which integrate with the extracted features of PCA and ICA through some statistical criterion. The performances of the proposed feature extraction approaches are evaluated on simulated data and three public data sets by using cross-validation accuracy of different classifiers that found in statistics and machine learning literature. Our experiment result shows that integrated with ICA and PCA feature is more effective than others in classification analysis.
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Extended Morphological Attribute Profiles (EAPs) are extension of Extended Morphological Profiles (EMPs). They are based on the more general Morphological Attribute Profiles (APs) rather than the conventional Morphological Profiles (MPs). EAPs are computed on few of the first principle components (PCs) extracted from the multi-/hyper-spectral data. In this paper, we propose to compute EAPs on features derived from supervised feature extraction techniques such as discriminant analysis feature extraction (DAFE), decision boundary feature extraction (DBFE) and non-parametric weighted feature extraction (NWFE)) instead of using unsupervised principal component analysis (PCA).
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The safety of building worker is important in construction industry. In order to realize the worker intelligent management, a novel building worker detection based on cross feature pyramid network is proposed to come true the real-time detection of workers. The proposed cross feature pyramid network uses cross feature of different layers to obtain robust feature of workers. The extracted feature may include high-level and low-level features. Experimental results indicate that the proposed algorithm obtain better performance than the traditional feature pyramid network.
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Feature extraction plays a central role in classification of hyperspectral data. We propose a clustering-based feature extraction (CBFE) method in this letter. The proposed method is supervised and only needs to calculate the first-order statistics. Thus, CBFE has better performance than some popular supervised feature extraction methods such as linear discriminant analysis, generalized discriminant analysis, and nonparametric weighted feature extraction in small sample size situation. In addition, CBFE works better than unsupervised approaches such as principal component analysis in classification applications. CBFE considers a vector associated with each band that is composed by the mean values of all classes in that band. Then, a clustering method such as k-means is run to group the similar bands in one cluster. The selected number of clusters is equal to the number of extracted features. Experiments carried out on two different hyperspectral data sets demonstrate that the CBFE has better performance in comparison with some conventional feature extraction methods.
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Constructing the pyramidal architecture for the feature is currently a very effective way to obtain feature information of objects at different scales. Although the feature pyramid can realize the recognition and detection of multi-scale objects in the object detection task well, it still has some limitations. Since the feature information of different levels is often not from the same layer of the network, it is difficult to obtain the feature of different objects information at a certain scale from a certain level feature map of the pyramid network. To solve this problem, we present a novel object detection architecture, named Enhanced Multi-scale Feature Fusion Pyramid Network (EMFFPNet). Our network consists of Enhanced Multi-scale Feature Fusion Module (EMFFM) and Predictor Optimization Module (POM). In EMFFM, Features at different levels can be fused into the Enhanced features as outputs, which are more representative and deterministic. In order to enable the enhanced features to play their respective roles in the pyramid network, we assign different weights to fusion features of different levels in POM. We perform the experiments on the COCO detection benchmark. The experimental results indicate that the performance of our model is much better than the state-of-the-art model.
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In face recognition, the dimensionality of raw data is very high, dimension reduction (Feature Extraction) should be applied before classification. There exist several feature extraction methods, commonly used are Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) techniques. In this paper, we present a comparative study of some feature extraction methods for face recognition in the same conditions. The methods evaluated here include Eigenfaces, Kernel Principal Component Analysis (KPCA), Fisherfaces, Direct Linear Discriminant Analysis (D-LDA), Regularized Linear Discriminant Analysis (R-LDA), and Kernel Direct Discriminant Analysis (KDDA). For the purpose of comparison on feature extraction methods, we adopt Nearest Neighbor (NN) algorithm from existed classifiers of face recognition, since this classifier is common and simpleness. Empirical studies are conducted to evaluate these feature extraction methods with images from ORL Face Database, and it is found that in most cases LDA-based methods are efficient than PCA-based ones.
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Partial discharge pattern recognition using multiscale feature extraction and support vector machine
An accurate interpretation of partial discharge (PD) signals in high voltage (HV) equipment provides crucial information for assessing the insulation conditions. To automate the interpretation process, feature extraction of PD signals and pattern recognition using the extracted features are required. This paper adopts discrete wavelet transform (DWT) and empirical mode decomposition (EMD) for signal decomposition and feature extraction on the PD signals obtained from different insulation defects. Support vector machine (SVM) is then used for classifying the features. Results indicate that features extracted from decomposed signals provide higher classification accuracy when compared with the conventional method that the features are extracted from original PD signals.
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With the increasing demands of visual surveillance systems, vehicle & people identification at a distance has gained more attention for the researchers recently. Extraction of Information from images and image sequences are vary important for the analysis according to the application. This research proposes feature extraction and classification method using Wavelet. The DWT is used to generate the feature images from individual wavelet sub bands. The feature images constructed from Wavelet Coefficients are used as a feature vector for the further process. The Principal Component Analysis (PCA) /Fisher Linear Discrimination analysis is used to reduce the dimensionality of the feature vector. Reduced feature vector are used for further classification using Euclidian distance classifier and neural network Classifier.
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In this paper, we propose and evaluate a novel object detection architecture called Cascaded Multi-Channel Feature Pyramid Network, or CM-FPN. The proposed network, which is based on Feature Pyramid Network by Lin et al., employs multi-stage cascaded top-down feature pyramid networks to extract more semantic multiresolution feature maps for object region proposal and object classification. Depths of the feature maps are adjusted so that the feature maps in the later stages of the cascades where they are more semantic have higher channel depths. Experimental evaluation of the proposed approach has shown that the proposed method produces higher object detection accuracy.
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Semantic feature
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