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There are several papers about pseudo dynamic methods used in signature authentication. Recently, the gray scale features local binary pattern(LBP) originate from texture analysis has been widely used in signature verification system with advantage of robustness to illumination change. The major problem of LBP is its sensitivity to noise, hence many solutions has been applied to solve this problem...
Traditional multi-class image classification needs a large number of training samples for building a classifier model. However, it is very time-consuming and costly to obtain labels for a large number of training samples from human experts. Active learning is a feasible solution. This paper proposes a maximum classification optimization method (MCO) for actively selecting unlabeled images to acquire...
Rising admissions in the South African institutions of higher education have enlarged student-to-lecturer ratios and increased the lecturer's workload, already burdened by administrative tasks. After marking tests, lecturers usually fill in a document called the cover page where the student's number, name and marks according to the questions are placed. Once this is done, they will have to recopy...
Signature verification has been widely applied in financial and legal transactions for authentication and has attracted much attention in the academia and industries. In this paper, a two-stage cascade verification system is proposed to minimize the cost of wrong verifications. In the first stage, an improved local mean K-Nearest Neighbor is applied with two reliable parameters to measure the confidence...
Though a variety of face recognition techniques have been proposed in the literature, only a few of them considered open set recognition problems, which involves the rejection of unregistered subjects in addition to identifying persons registered in the database. Transductive confidence machine (TCM) is a novel strategy for classification associated with valid confidence, with recognition reliability...
In the past few years, sparse representation classifier (SRC) has attracted great attention and widely used in human face recognition. Kernel sparse representation classifier (KSRC) based Metaface dictionary learning (MFL) is discussed in this paper. KSRC is a nonlinear extension of SRC. Through kernel trick, samples are mapped into an unknown kernel feature space first and then SRC will be used in...
Although a great success has been achieved for the situation of high quality images during the past decades, Character recognition in low quality images still remains a challenge. To tackle this challenge, in this paper a novel method in the SVM framework is proposed to recognize the characters in low quality document images by using local and global features. Firstly, a multi-scale sliding window...
A gender classification system uses human face from a given image to tell the gender of the given person. An effective gender classification approach is able to promote the improvement of many other applications, including image/video retrieval, security monitor, human-computer interaction, etc. In this paper, a method for gender classification task in frontal face images based on stacked-autoencoders...
In recent years, with the theory of compressed sensing being proposed and applied widely, the sparse representation method has become one of the hotspots to handle the superresolution problem. Usually, this kind of algorithms use only one dictionary pair for all low-resolution patches, which makes the recovered results less satisfied due to its bad adaptability. To overcome such problem, in this paper,...
‘Circle’ and ‘arrow’ traffic lights are both common at intersections in urban road environment. However, existing purely vision based systems are only focus on either ‘circle’ or ‘arrow’ traffic light recognition, which limits their real-world application. In this paper, A novel robust and real-time traffic light recognition system based on hierarchical vision architecture is carefully designed. The...
In the paper we present an increment coefficient method used in face recognition which is also a linear representation-based method. Different from traditional linear representation-based method, for every class, we develop a linear model representing a virtual sample as a linear representation of the class-specific training sample and the testing sample. In the model, the virtual sample is the mean...
As a key point of orthogonal frequency division multiplexing (OFDM) technology, carrier frequency offset (CFO) has a significant effect on OFDM system performance. This paper proposes a CFO estimation method by using complementary sequence (CS) as training sequence. The proposed CS-based method can operate integer CFO and fractional CFO estimations on the basis of the same training sequence. The fractional...
Small target detection is one of the crucial techniques of infrared search and tracking systems. A new infrared small target detection method based on the tensor model is proposed in this paper. Firstly, by cropping the real infrared images, small target samples are generated to a higher order tensor. Secondly, the N-mode SVD method is used to obtain the different characteristic matrices and the core...
A novel tracking method is developed based on logistic regression classifier and sparse representation in this paper. Firstly, the logistic regression classifier with online update is utilized to determine the searched image patches belonging to the potential targets or the false targets. Through the classification, a huge number of false targets can be removed from the searched patches. Then, the...
In this paper we present a method for roadside vegetation detection from video obtained from a moving vehicle with intended use in road infrastructure maintenance and traffic safety. While many published methods are using Near Infrared images which are suitable for vegetation detection, our method uses image features from the visible spectrum allowing the use of a common color camera. The presented...
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