The application of difference method to dim point target detection in infrared images
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This paper deals with the detection of dim point targets in infrared images. Dim point targets detection is always a difficulty in information processing. Researchers have proposed many effective methods; this paper introduces a new method. Whereas difference method has obtained good result in one dimensional signal processing, this paper manages to apply it to two dimensional signal processing, that is to say, dim point targets detection in infrared images of low SNR. The image background is color noise, and its column correlation is strong. So, ordinary methods probably lose the true targets because of the strong color noise, but unlike them, difference method can overcome this shortcoming, it can eliminate correlation noise and enhance useful information, finally pick out the probable targets from noise background. In the paper, the method was given a more extensive account. In order to improve the detection effect, we utilize prefilter. The prefilter is realized by the alpha filter. Because the same targets have same location in more than three frames, using alpha filter can utilize the information of adjacent frames, increase the SNR of the raw data and reduce the noise of the images.< Objective: To propose the robust face recognition system in front of two different challenges, pose variations and illumination variations. Methods/Statistical Analysis: Five different methods are used for feature extraction (1) Local binary pattern histogram (LBPH) from entire image (2) LBPH from image divided into 9 different regions (3) Local binary pattern image (LBPI) (4) Gabor features (GF) and (5) Two dimensional discrete wavelet transform (2D-DWT) using haar-3 wavelet. For the last four methods principal component analysis (PCA) is used for dimensionality reduction and classification is performed by two non linear functions, radial basis function (RBF) and polynomial function (PF) based on support vector machines (SVM's). Findings: Application/Improvements: For performing the experiments two databases are used, ORL face database and extended yale-B face database. For the former database it is the LBPH feature extracted from different facial regions outperforms the other features and for the second database LBPI feature outperforms the other features. Keywords: Gabor Features, Local Binary Patterns, Local Binary Pattern Histograms, Principal Component Analysis, Support Vector Machines, Two Dimensional Discrete Wavelet Transform
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Feature extraction, discriminant analysis, and classification rules are three crucial issues for face recognition. We present hybrid approaches to handle three issues together. For feature extraction, we apply the multiresolution wavelet transform to extract the waveletface. We also perform the linear discriminant analysis on waveletfaces to reinforce discriminant power. During classification, the nearest feature plane (NFP) and nearest feature space (NFS) classifiers are explored for robust decisions in presence of wide facial variations. Their relationships to conventional nearest neighbor and nearest feature line classifiers are demonstrated. In the experiments, the discriminant waveletface incorporated with the NFS classifier achieves the best face recognition performance.
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Feature weighting has attracted lots of researchers in pattern classification and data mining community. To select an appropriate subset of features, original feature set is first weighted according to its importance in determining its class label, and then using a threshold, a subset of features is obtained. Some adequate research works have been reported, which used feature weights for clustering by k-means algorithm. Few such algorithms have been developed, but as far as best of our knowledge, weighted pattern vectors have not been used by any pattern recognition algorithm especially in biometrics. In this paper, we formulate a unique pattern specific feature based weighting method, which weights the features of an individual pattern vector on the basis of their discriminative power to recognise that pattern. Proposed approach yields FAR = 0% at FRR = 0.2 for Euclidian distance and 0.02% and 0.3 respectively for cosine similarity measure.
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Feature extraction is an important processing step in texture classification. For feature extraction in contourlet domain, statistical features for blocks of subband are computed. In this paper, we present an efficient feature vector extraction method for texture classification. For more discriminative feature a canonical correlation analysis method is propose for feature vector fused to the different sample of texture in the same cluster. The KNN (K-Nearest Neighbor) classifier is utilizing to perform texture classification.
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Feature extraction is an important method in electromyography (EMG) pattern recognition. High-dimensional EMG features vector lead to redundancy of features. Redundancy of features results in a decrease in classification accuracy of EMG pattern recognition and an increase in computation time for classifier to classify the pattern of EMG signal. Many researchers used feature selection method to decrease the redundancy of features. Sequential forward selection (SFS) and particle swarm optimization (PSO) are widely used in feature selection. This study mainly discusses the effect of two different feature selection methods (SFS and PSO) on EMG pattern recognition. We proposed three methods to compare the different influences of different feature selection methods on EMG pattern recognition. They are support vector machine (SVM) combines with none feature selection method, SVM combines with SFS (SFSSVM) and SVM combines with PSO (PSOSVM). We used SVM, SFSSVM and PSOSVM to classify 11 arm movements respectively. By discussing the classification accuracy and computation time of the three methods, we discussed the different influences of different feature selection methods on EMG pattern recognition. The results showed that the PSOSVM outperformed SVM and SFSSVM. The result implied that PSO is a proper feature selection method for EMG pattern recognition.
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A discriminative face feature, i.e. multi-scale ICA texture pattern (MITP), is proposed for automatic gender recognition. First, independent component analysis (ICA) filters of various scales are learned using randomly collected face patches from training samples. Each face image is then encoded by sorting the responses of these filters. Finally, a histogram feature is formed based on the non-overlapping subregions of the encoded images. The newly proposed sparse classifiers are adopted for classification. Experiments on two benchmark face databases validate the effectiveness of MITP.
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