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Supervised classification techniques use labeled samples in order to train the classifier. In a hyperspectral image, usually the number of such samples is limited, and as the number of bands available increases, this limitation becomes more severe. Such consequences suggest the need for reducing the dimensionality via a preprocessing method. This reduction should enable the estimation of feature extraction...
Feature descriptors usually have high dimensionality to efficiently represent key points. Finding matches between large sets of descriptors is a basic step in many applications in computer vision and image processing. When the number of descriptors is large, detection of corresponding points can be extremely time-consuming. The goal of this paper is reducing the computational cost in the matching...
Robustly counting the number of people for surveillance systems has widespread applications. In this paper, we propose a robust and rapid head-shoulder detector for people counting. By combining the multilevel HOG (Histograms of Oriented Gradients) with the multilevel LBP (Local Binary Pattern) as the feature set, we can detect the head-shoulders of people robustly, even though there are partial occlusions...
A face recognition method based on improved principal components analysis (PCA) reconstruction is proposed. Firstly, PCA algorithm was performed on training samples of each pattern class to calculate the optimal projection transformation matrices. A point that should be mentioned was that we used median vector rather than mean vector in total scatter matrix. The feature vectors of testing sample could...
In this paper we present a new robust method for recognizing face images using a robust tensorial representation of binary gaussian jet maps (Tensor-Jet). This tensorial representation captures local appearance while retaining information about the spatial structure. During the tensors construction, each Gaussian Jet map is calculated with a Half Octave Gaussian Pyramid using a linear complexity algorithm...
Extracellular recording of neural signals records the action potentials of neurons adjacent to the electrode as well as the noise generated by the overall neural activity around the electrode. The spike sorting process, i.e., detection of the noisy spikes in the recorded digital signal, feature extraction, and clustering of the spikes has been investigated extensively since it is a challenging problem...
Complete design and implementation of a robust palm biometrics recognition and verification system has been presented. The paper attempts to scientifically develop a comprehensive set of hand geometry features and develop an original algorithm from a fundamental level to robustly compute a selected set of features so as to minimize palm placement effect. These features are combined with chrominance...
This paper proposes a robust faces recognition method based on the phase spectrum features of the local normalized image. The principal components analysis (PCA) and the support vector machine (SVM) are used in the classification stage. We evaluate how the proposed method is robust to illumination, occlusion and expressions using "AR face database", which includes the face images of 109...
Li and Chen (J. Amer. Statist. Assoc. 80 (1985) 759) proposed a method for principal components using projection-pursuit techniques. In classical principal components one searches for directions with maximal variance, and their approach consists of replacing this variance by a robust scale measure. Li and Chen showed that this estimator is consistent, qualitative robust and inherits the breakdown...
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