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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...
Existing maximum-margin support vector machines (SVMs) generate a hyperplane which produces the clearest separation between positive and negative feature vectors. These SVMs are effective when datasets are large. However, when few training samples are available, the hyperplane is easily influenced by outliers that are geometrically located in the opposite class. We propose a modified SVM which weights...
In recent years, deep architectures have been used for transfer learning with state-of-the-art performance in many datasets. The properties of their features remain, however, largely unstudied under the transfer perspective. In this work, we present an extensive analysis of the resiliency of feature vectors extracted from deep models, with special focus on the trade-off between performance and compression...
In this paper we propose a multi-view method to recognize hand gestures using point cloud. The main idea of this paper is to project point cloud into view images and hand gestures are described by extracting and fusing features in view images. The conversion of feature space increases the inner-class similarity and meanwhile reduces the inter-class similarity. The features of view images are extracted...
Starting from an object's location in a video frame, tracking-by-detection methods find the location of that object in a subsequent video frame. The tracker's detection step may produce multiple false positives during short-term occlusions, which can result in loss of track. We propose a tracking-by-detection method that is robust to short-term occlusions and false positives. Here, we extend the Struck...
In this paper, we present a robust Extremal Region (ER) based scene text detection system. To eliminate the vast non-text components generated by ER operator, a three-stage cascaded filter is proposed. In the first stage, a powerful character classifier enhanced by recursive local search is introduced to separate text components from noises. Then, an efficient heuristic pruning method is designed...
Speech therapy is essential to help children with speech sound disorders. While some computer tools for speech therapy have been proposed, most focus on articulation disorders. Another important aspect of speech therapy is voice quality but not much research has been developed on this issue. As a contribution to fill this gap, we propose a robust scoring model for voice exercises often used in speech...
In this paper a robust beamforming method is proposed, which can effectively overcome the influence of DOA(direction of arrival) mismatch. The proposed method exhibits the enhanced robustness of the beamformers based on analysis of linear constrained minimum variance beamformer. Then the support vector regression (SVR) algorithm is applied to the robust beamforming, which is based on the principle...
We consider the detection of the control or idle state in an asynchronous Steady-state visually evoked potential (SSVEP)-based brain computer interface system. We propose a likelihood ratio test using Canonical Correlation Analysis (CCA) scores calculated from the EEG measurements. The test exploits the state-specific distributions of CCA scores. The algorithm was tested on offline measurements from...
Fully automatic localization of lumbar vertebrae from clinical X-ray images is very challenging due to the variation of X-ray quality, scale, contrast, number of visible vertebrae, etc. To overcome these challenges, we present a novel framework, where we accelerate a scale-invariant object detection method using Support Vector Machines (SVM) trained on Histogram of Oriented Gradients (HOG) features...
The performance of the myoelectric pattern recognition system sharply decreases when working in various limb positions. The issue can be solved by cumbersome training procedure that can anticipate all possible future situations. However, this procedure will sacrifice the comfort of the user. In addition, many unpredictable scenarios may be met in the future. This paper proposed a new adaptive myoelectric...
This paper presents a study on hand gesture distinguish ability between Speeded Up Robust Features(SURF) and Scale Invariant Feature Transform(SIFT) feature descriptors of hand images. Then bag of visual words are to map these descriptors to a dimension vector and support vector machine(SVM) classifer is trained to recognize hand gesture. Experimental results demonstrate that SURF feature descriptors...
Support vector machine (SVM) plays an important part in fault diagnosis of chemical plant, and intelligent optimization algorithms are used to optimize the SVM parameters, including the penalty parameter C and parameter g of different kernel function, to improve performance of its faults classification. To assess SVM faults classification capability based on diverse optimization algorithms and various...
Drift is the most difficult issue in object visual tracking based on framework of “tracking-by-detection”. Due to the self-taught learning, the mis-aligned samples are potentially to be incorporated in learning and degrade the discrimination of the tracker. This paper proposes a new tracking approach that resolves this problem by three multi-level collaborative components: a high-level global appearance...
According to the operation of the automaton transient impact, nonlinear, non-stationary signal, a method which is based on the time-frequency characteristics and PCA-SVM automaton fault diagnosis is proposed. Firstly, this paper uses statistical analysis and overall empirical mode decomposition method to construct high dimensional mixed domain initial feature vector from the characteristics of different...
Detecting infrared pedestrian in outdoor smart video surveillance is always a challenging and difficult problem. Although there have been many methods based on histograms of oriented gradients (HOG) to solve this problem, they would probably fail because of shelter and poor quality of image. To overcome this problem, we propose a robust feature to describe pedestrian which is called entropy-edge weighted...
In this study, a new algorithm for Content Based Image Retrieval (CBIR) using bi-cubic interpolation (BCI)with color coding (CC) and different level of discrete wavelet transform (DWT). In this paper the techniques of CBIR are discussed, analyzed and compared. BCI is used to scale the query image and database images. CC is used for color feature extraction. Apply DWT on each level plane of an image...
A query image based scene/image retrieval system is a system that analyzes the properties of a query image and identifies the class in which the image belongs and retrieves a number of images which are most alike and relevant to the query image. A scene/image classifier provides the first stage for this system. Scene classification is the process that analyzes the properties of various image features...
Object classification is an important task within the field of computer vision. It is the process of labeling objects into predefined and semantically meaningful categories using trained datasets. A classification is made using a segment of image which is actually a single pixel or a group of pixels which is called a classification unit. Many researchers are working in this area to improve the accuracy...
In this paper, we propose a unified classification framework for 3D urban point clouds. First of all, an efficient segmentation approach is utilized to segment 3D point clouds. For comparison, we employed two recently developed point clouds segmentation approaches. The first one is a region-growing-based segmentation algorithm by using robust saliency features and another one is a hierarchical-clustering-based...
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