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This paper describes a system for detection and classification of moving objects based on support vector machines (SVM) and using 3D data. Two kinds of camera systems are used to provide the classification system with 3D range images: time-of-flight (TOF) camera and stereo vision system. While the former uses the modulated infrared lighting source to provide the range information in each pixel of...
Topic-based mixture model (TBMM) is a learning algorithm for factored classification. In factored classification, the class label is factored into a vector of class features. For example, the class label for a personal Web page at a university might be described by two features: the academic discipline of the person, and their position (e.g., 'chemistry professor' or 'physics student'). An approach...
In this paper, a novel grey-based feature ranking method for feature subset selection is proposed. Experiments performed on various application domains are reported to demonstrate the performance of the proposed approach. It can be easily seen that the proposed approach yields high performance and is helpful for pattern classification
In many cases, data are represented as high dimensional feature vectors. It makes the feature selection necessary to reduce the computational burden, improve the generalization ability and the interpretability. In this paper, we present a novel feature selection method which is named least squares support feature machine (LS-SFM). In comparison with SVM and LS-SVM, this method has two outstanding...
Geodesic distance calculation is a crucial stage in the distance-based manifold learning algorithms. Owning to the nonparametric of Isomap, however, it is not economic and convenient to compute the geodesic distances for an unseen new sample via neighborhood graph. In this paper, we proposed a geodesic metric learning (GML) scheme based on iterative majorization, to obtain a parametric geodesic metric...
This paper presents a novel method for feature extraction based on the generalized entropy of the histogram formed by Euclidean distances, which is named distributive entropy of Euclidean distance (DEED in sort). DEED is a nonlinear measure for learning feature space, which provides the congregate and information measure of learning samples space. The ratio of between-class DEED to within-class DEED...
A novel model for independent radial basis function (IRBF) neural network employing Gabor-based kernel PCA with fractional power polynomial models for feature extraction is proposed in this paper. In the new model, a bank of Gabor filters is first built to extract Gabor face representations characterized by selected frequency, locality and orientation to cope with various illuminations, facial expression...
Linear discriminant analysis (LDA) is a popular feature extraction technique for face recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional face data. Some approaches have been proposed to overcome this problem, but they usually utilize all eigenvectors of null or range subspaces of within-class scatter matrix (Sw). However, experimental results...
A two-stage neural network architecture constructed by combining recurrent neural network (RNN) with kernel feature extraction is proposed for stock prices forecasting. In the first stage, kernel independent component analysis (KICA) and kernel principal component analysis (KPCA) are used as feature extraction. In the second stage, RNN with kernel feature extraction is used to regression estimation...
A robust environment map with 3D spatial natural landmarks that facilitates monocular vision based mobile robot for global localization is built. The highly distinctive multi-dimensional vector descriptors associated with the features extracted through scale invariant feature transform (SIFT) can be robustly matched despite changes in illumination, scale and viewpoint. The landmarks are 3D restructured...
To heighten the biometrics security level, the biometrics feature extraction and verification need to be performed within smart cards, not in external card readers. However, the smart card chip has very limited processing capability, and typical fingerprint feature extraction and verification algorithms may not be executed on a state-of-the-art smart card. Therefore, this paper presents a system-on-chip...
Shape information is an important distribution to content-base image retrieval systems. In this paper we present a principal components descriptor based on principal component analysis, as well as an enhanced principal components descriptor for shape-based image retrieval. Experiments are performed on the MPEG-7 CE1-B test database. The presented descriptors are statistically evaluated by comparing...
In this paper we address the problem by managing the insertion of new points and deletion of unnecessary points to better describe and track the object's boundary. In particular, our method uses more points in highly curved parts of the contour, and fewer points in less curved parts. The proposed algorithm can successfully define the contour of the object, and can track the contour in complex images...
In vein pattern biometrics, analysis of the shape of the vein pattern is the most critical task for person identification. One of best representations of the shape of vein patterns is the skeleton of the pattern. Many traditional skeletonization algorithms are based on binary images. In this paper, we propose a novel technique that utilizes the watershed algorithm to extract the skeletons of vein...
In order to verify whether or not the EEG patterns can be classified when the subjects perceive different types of geometric figures, we perform some EEG experiments. In this paper, the evoked potentials by three types of geometric figures are extracted and classified using a series of approaches. First, a two-stage source extraction algorithm is proposed to extract the evoked potentials from the...
Obstacle detection is very crucial for mobile robots. The algorithm presented in this paper makes use of motion cues in the video streams. Firstly, we calculate optical flow at feature points. Then rotation of the camera and FOE (focal of expansion) are evaluated separately. Rotation and FOE value are refined according to an iterative linear method. Finally, we get inverse TTC (time to contact) with...
Fuzzy c-means (FCM) clustering algorithm has been widely used in automated image segmentation. However, the standard FCM algorithm is sensitive to noise because of taking no into account the gray and spatial information of pixel. The paper proposes an improved FCM algorithm for image segmentation. We use the degree of gray similarity and distribution statistics of the neighbor pixels to form a new...
For feature extraction resulted from Fisher discriminant analysis (FDA), it is expected that the optimal feature space is as low-dimensional as possible while its linear separability among different classes is as large as possible. Note that the existing theoretical expectation on the optimal feature dimensionality may contradict with experimental results. Due to this, we address the optimal feature...
Video object extraction has been a hot topic in the field of modern information processing. This paper proposes a novel video object watershed segmenting strategy, which combines the merits of alternative sequential filtering by reconstruction and adaptive threshold algorithm. This strategy can automatically determine the structural element size of the morphological filter and the threshold value...
How to get high-resolution face image from low-resolution video sequence with time-saving demand is an important problem. In this paper, a new framework is described to extract high-resolution face image from video sequence. Firstly, the real-time face detection based on AdaBoost algorithm and Haar like feature is implemented. The detected face area is segmented from the background to form a face...
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