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Fast and robust traffic sign recognition is very important but difficult for the safety driving assist systems. This study addresses the fast and robust traffic sign recognition to enhance safety driving. We first adopt the typical Hough transform methods to implement coarse-grained locating of the candidate regions (shapes of rectangle, triangle and circle, etc.) of the traffic signs; and then propose...
Locally normalized Histogram of Oriented Gradient (HOG) algorithm originally proposed by Dalal & Triggs presents excellent results for pedestrian detection. However, as the demand of accuracy and speed in real-time application increase, the detection speed and robustness of this method is becoming insufficient. Over the years, improvements have been proposed by different researchers in order to...
Human listeners are capable of recognizing speech in noisy environment, while most of the traditional speech recognition methods do not perform well in the presence of noise. Unlike traditional Mel-frequency cepstral coefficient (MFCC)-based method, this study proposes a phoneme classification technique using the neural responses of a physiologically-based computational model of the auditory periphery...
In this paper, we propose a robust proximal classifier via absolute value inequalities (AVIPC) for pattern classification. AVIPC determines K proximal planes by solving K optimization problems with absolute value inequalities. In AVIPC, each proximal plane is closer to one class and far away from the others. By using the absolute value inequalities, AVIPC is more robust and sparse than traditional...
The advance of high throughput biotechnology enables the generation of large amount of biomedical data. The microarray is increasingly a popular approach for the detection of genome-wide gene expression. Microarray data have thus increased significantly in public accessible database repositories, which provide valuable big data for scientific research. To deal with the challenge of microarray big...
In this paper, we consider the problem of unsupervised feature selection. Recently, spectral feature selection algorithms, which leverage both graph Laplacian and spectral regression, have received increasing attention. However, existing spectral feature selection algorithms suffer from two major problems: 1) since the graph Laplacian is constructed from the original feature space, noisy and irrelevant...
Most of the state-of-the-art methods for action recognition are very complex and variant to the geometric transformation like scaling, translation and rotation. Cuboid based method required all frames to extract the cuboid of action that's why cuboid based methods are expensive. Other methods use contour based approach for feature representation which is not robust to noise. So we require a very fast...
In response to the urgent need for learning tools tuned to big data analytics, the present paper introduces a feature selection approach to efficient clustering of high-dimensional vectors. The resultant method leverages random sampling and consensus (RANSAC) arguments, originally developed for robust regression tasks in computer vision, to yield novel dimensionality reduction schemes. The advocated...
In order to utilize identification to the best extent, we need robust and fast algorithms and systems to process the data. Having palmprint as a reliable and unique characteristic of every person, we extract and use its features based on its geometry, lines and angles. There are countless ways to define measures for the recognition task. To analyze a new point of view, we extracted textural features...
With the advent of sophisticated image editing softwares, it has become easy to forge digital images. Copy-move is a very common technique for image forgery; retouching tools are applied along with it which makes copy-move invisible to the naked eye. An image can be forged using copy-move technique to duplicate or hide undesirable objects. When some object is copy-moved with the help of geometrical...
Triple negative breast cancers (TNBC) are clinically heterogeneous, an aggressive subtype with poor diagnosis and strong resistance to therapy. There is a need to identify novel robust biomarkers with high specificity for early detection and therapeutic intervention. Microarray gene expression-based studies have offered significant advances in molecular classification and identification of diagnostic/prognostic...
Phonemes are the smallest units of sound produced by a human being. Automatic classification of phonemes is a well-researched topic in linguistics due to its potential for robust speech recognition. With the recent advancement of phonetic segmentation algorithms, it is now possible to generate datasets of millions of phonemes automatically. Phoneme classification on such datasets is a challenging...
In this paper, a novel one-class classification approach, namely, robust smooth one-class support vector machine (RSOCSVM) is proposed. The proposed method can efficiently enhance the anti-noise ability of the traditional one-class support vector machine (OCSVM). Utilizing the smooth technique, RSOCSVM reformulates the quadratic programming problem of OCSVM as an unstrained optimization format. Moreover,...
Despite the advent and popularity of low-cost commercial sensors (e.g., Microsoft Kinect), research in 3D vision still primarily focuses on the development of advanced algorithms geared towards high resolution data. This paper presents a comparative performance evaluation of renowned state-of-the-art 3D local surface descriptors for the task of registration of both high and low resolution range image...
Wireless sensor networks have attracted a lot of attention lately due to their wide range of applications from inventory management to battlefield surveillance. In most ca-ses, an accurate time synchronization is essential to provide a common time reference and facilitate coordinated actions. However, a particular challenge arises when it is considered that network nodes are clocked by low-cost, low-stability...
In this paper, we proposed a novel supervised feature extractor named as class-augmented independent component analysis (CA-ICA) whose performance can be maintained even after the input variables are varied, only if new input variables are still linear combinations of the same independent sources as old input variables were. This property can be useful in implementing an sEMG decoder robust to the...
The shape analysis of otoliths, which are calcified structures in the inner ear of teleostean fishes, is known to be particularly relevant to address species identification and stock discrimination. Generally, scientists use classical methodologies of statistical analysis and shape recognition such as Fourier shape descriptors and Principal Component Analysis (PCA). These methods are subject to several...
When packed loss caused by bit error occurs in the data transmission of IP network, one should retransmit the corrupted data to avoid information loss. However, this will take more damage for those business in which retransmission is difficult to realize or will limit the performance of system. In order to reduce this damage, this paper studied the checksum restriction mechanism of the IPv4 header...
Under the complex condition, the abnormal vibration of code tracking loop and carrier tracking loop which cause outliers will affect the position accuracy of the GNSS/INS integrated navigation system. To solve this problem, this paper puts forward a new adaptive robust Kalman filter restraining outliers. This algorithm can eliminate the outliers caused by the abnormal vibration of code tracking loop...
In rough set approaches, decision rules are induced from a given data table showing the relation between attribute values and classes of objects. The induced decision rules are used for the classification of new objects by their attribute values. However, some of new objects do not match any decision rule conditions because the given data table does not always include all possible patterns. In those...
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