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In this paper, real-time recognition and tracking of multiple similar targets at 6-DOF motion is studied. A real-time multi-target recognition algorithm is proposed and implemented based on Marker to solve the difficult problem of distinguishing multiple similar targets. Because the lighting conditions of markers at 6-DOF motion are widely changeable, existing marker recognition algorithms are sensitive...
The traditional random sample consensus (RANSAC) algorithm is capable of estimating a model with fewer data points and almost unaffected by noise. There are several drawbacks of such algorithm including detection errors, unstable threshold and massive calculation. By analyzing the spatial relations of graphics pixels, a hypothetical circle is firstly formed with three hypothetical points which are...
Classifier fusion is a well-studied problem in which decisions from multiple classifiers are combined at the score, rank, or decision level to obtain better results than a single classifier. Subsequently, various techniques for combining classifiers at each of these levels have been proposed in the literature. Many popular methods entail scaling and normalizing the scores obtained by each classifier...
Image registration is an important and fundamental problem in computer vision and image processing. Although there are currently a large number of image registration algorithms such as RANSAC and its extensions, image registration under very noisy conditions remains difficult when it cannot obtain enough number of correct corresponding points. This paper solves this issue by introducing a random resample...
At present, due to advancement in communication technology and internet, people are sharing images over social networking and other sites very efficiently but it has created problems regarding copyright protection and authentication of the images. In past 10–15 years, digital watermarking has been becoming a popular solution for these problems. Besides of being popular, it creates distortions in host...
Neighborhood Covering Reduction (NCR) is an effective tool to learn rules from structural data for classification. However, the existing neighborhood covering model is not robust enough. A neighborhood is constructed according to the nearest heterogeneous samples. This strategy over focuses on the boundary samples and makes the model sensitive to noise. To tackle this problem, we proposed a Rough...
Kernel principal component analysis (kPCA) learns nonlinear modes of variation in the data by nonlinearly mapping the data to kernel feature space and performing (linear) PCA in the associated reproducing kernel Hilbert space (RKHS). However, several widely-used Mercer kernels map data to a Hilbert sphere in RKHS. For such directional data in RKHS, linear analyses can be unnatural or suboptimal. Hence,...
Text data present in scene images may be the important clue for indexing, automatic footnote, and indexing of images. Now-a-days extraction of text from images has become one of the fastest growing research areas in the field of computer vision. In scene images, text data are present with huge variations in font sizes, styles, alignments, and orientations. These variations make the task of detection...
This paper considers a decentralized projection free algorithm for non-convex optimization in high dimension. More specifically, we propose a Decentralized Frank-Wolfe (DeFW) algorithm which is suitable when high dimensional optimization constraints are difficult to handle by conventional projection/proximal-based gradient descent methods. We present conditions under which the DeFW algorithm converges...
Phasor measurement units (PMUs) are instrumental for grid monitoring thanks to high sampling rates and precise synchronization. However, collecting and analyzing the PMU data are challenging due to the large volume, computational burden, and missing measurements. In this work, a robust subspace clustering model is adopted to perform reconstruction of missing measurements while simultaneously detecting...
In recent years, nonnegative matrix factorization (NMF) attracts much attention in machine learning and signal processing fields due to its interpretability of data in a low dimensional subspace. For clustering problems, symmetric nonnegative matrix factorization (SNMF) as an extension of NMF factorizes the similarity matrix of data points directly and outperforms NMF when dealing with nonlinear data...
High-throughput technologies have enabled us to rapidly accumulate a wealth of diverse data types. These multi-view data contain much more information to uncover the cluster structure than single-view data, which draws raising attention in data mining and machine learning areas. On one hand, many features are extracted to provide enough information for better representations, on the other hand, such...
Saliency detection is to find the most important object automatically according to the human visual in the unknown scene. Most existing algorithms detect the salient object using various salient object features. In this paper, we present a novel saliency detection method by an iterated graph Laplacian based ranking on manifolds to determine whether the region is salient or not. Firstly, we segment...
Automated display testing for visual unpleasant and erroneous navigation sequences is an important step to preserve a high quality standard for premium vehicle manufacturers. This paper presents a novel error detection algorithm for navigation sequences based on novelty detection on motion parameters obtained from real world navigation sequences. Motion parameters are accumulated through key point...
Computational analysis of transcription factor binding site (TFBS) is one of the most challenging topics in bioinformatics. A set of TFBS sequences is a type of multiple sequence alignment (MSA). Thus, the hidden Markov model (HMM), as a powerful tool to model MSA, has been extensively applied in TFBS analysis. However, with the sizes of TFBS problems, training HMM in a deterministic way is computationally...
Accurate segmentation of breast lesions is among the several challenges in the development of a fully automatic Computer-Aided Diagnosis system for solid breast mass classification. Many high level segmentation methods rely heavily on proper initialization and the seed point selection is usually the necessary first step. In this paper, a fully automatic and robust seed point selection method is proposed...
A series of online multi-task learning (OMTL) algorithms have been proposed to avoid the expensive training cost and poor adaptability of traditional batch multi-task learning (MTL) algorithms in recent years. However, these OMTL algorithms usually assume that all tasks are closely related, which may not hold in practical scenarios. More importantly, their theoretical reliability is weakened due to...
Fingerprints are used in various fields for different purposes. A typical fingerprint systems database may consist of millions fingerprint templates. The performance of matching with a large database has been the focus of in the recent research. This paper proposes an efficient database indexing technique using four fast and lightweight effective filters in a circular pattern matching technique [1]...
Human Activity Recognition (HAR) is a powerful tool for understanding human behaviour. Pervasive sensors, such as wearable devices, have an increasing market penetration and generate a tremendous amount of data. The myriad of available clinical and consumer-grade wearables generate a continuous time series of a person's daily physical exertion and rest. Applying HAR to the activity time series can...
The xDAWN algorithm is well-known as a method for designing spatial filters to improve signal-to-noise ratio and to reduce the dimension of observed EEG signals. This paper proposes a method for spatially smoothing xDAWN spatial filters to give a robustness against small sample problem. The proposed method gives a subspace constraint to the parameter space of the spatial filters. This subspace is...
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