The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
We present a model for the autonomous and simultaneous learning of smooth pursuit and vergence eye movements based on principles of efficient coding. The model accounts for the joint development of visual encoding and eye movement control. Sparse coding models encode the incoming data and capture the statistics of the input in spatio-temporal basis functions while a reinforcement learner generates...
With the rapid advance in information technology, more and more information exchange platforms appear. People can freely exchange information on these platforms. However, not all information is reliable. To make correct decisions, it is necessary to detect and remove unreliable information. The main purpose of this study is to improve the reliability of hotel ranking by detecting and deleting outlier...
A novel approach which generates so-called ”potential genuine signatures” by timely nonlinear sampling from an original on-line signature is presented. Velocity vectors of these potential genuine signatures are exploited to construct an user-dependent overcomplete dictionary. Finally, sparse coefficients which served as features are used for verification. There are two main advantages: 1) The DTW...
In this paper, tracking problem is considered as a sparse approximation of target by templates created during video process. In addition, some trivial templates are used to avoid the effects of noise and illumination changes. Each candidate is sparsely represented by the template set. This goal is achieved by solving an l1- regularized least-square equation. To find tracking result, a candidate with...
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,...
To improve the ship detection performance in polarimetric synthetic aperture radar (PolSAR) images under low signal-to-clutter ratio (SCR), this paper presents a new PolSAR ship detection method based on the low-rank dictionary learning and sparse representation. For each pixel, the scattering mechanism information is described via a feature vector formed by the polarimetric covariance matrix elements...
We consider the problem of estimating a finite sum of cisoids via the use of a sparsifying Fourier dictionary (problem that may be of use in many radar applications). Numerous signal sparse representation (SSR) techniques can be found in the literature regarding this problem. However, they are usually very sensitive to grid mismatch. In this paper, we present a new Bayesian model robust towards grid...
The Broadband is a notable trend of the TT&C system, which will be certain to lead to high speed sampling pressure and massive data problem. Theory of compressive sensing can solve the issue. However, signal sparsity is an important prerequisite for compressive sensing. On basis of the dictionary learning, the sparsity of DS TT&C signal was studied preliminarily. Through in-depth analysis...
Dictionary learning which is based on the sparse coding has been frequently employed to many tasks related to remote sensing images such as classification, reconstruction and change detection. Recently, many new dictionary learning algorithms which are on an non-analytic dictionary had been proposed. Online Dictionary Learning is the famous one which can be applied to process large-scale images. But...
We consider the dictionary learning problem in sparse representations based on an analysis model with noisy observations. A typical limitation associated with several existing analysis dictionary learning (ADL) algorithms, such as Analysis K-SVD, is their slow convergence due to the procedure used to pre-estimate the source signal from the noisy measurements when updating the dictionary atoms in each...
A new ground moving target indication method for single synthetic aperture radar (SAR) image is presented, it is based on sparse representation. According to the Range Doppler imaging of static scene, a ground moving target imaging model is achieved. Based on the model, the over-complete dictionary of targets sample images with different speeds is constructed. Then the test SAR images are blocked...
Place categorization addresses the problem of determining the semantic label of the current position of a robot, given a snapshot of the environment as well as previously labeled information about different places that the robot has already seen. State-of-the-art approaches use machine learning techniques that require extensive and often time consuming training. This work proposes a novel formulation...
We present an approach for object class learning using a part-based shape categorization in RGB-augmented 3D point clouds captured from cluttered indoor scenes with a Kinect-like sensor. We propose an unsupervised hierarchical learning procedure which allows to symbolically classify shape parts by different specificity levels of detailedness of their surface-structural appearance. Further, a hierarchical...
Availability of a single training sample (STS) or degraded set (DS) of training and testing samples restricts the success of face recognition in real-world applications. We propose a unified framework for handling both these challenges simultaneously by using a data dictionary, which is a combination of training dictionary and intra-class variation dictionary. The training dictionary is assembled...
This paper presents a promising and novel onset detection algorithm. Instead of traditional musical signal processing methods based on orthogonal transform such as Fourier Transform and Wavelet Transform, we focused on sparse representation with learned dictionary by K-SVD. We discussed the theorem and the methods of K-SVD for onset detection. The experimental results indicted that the proposed approach...
Hashtags are useful for categorizing and discovering content and conversations in online social networks. However, assigning hashtags requires additional user effort, hampering their widespread adoption. Therefore, in this paper, we introduce a novel approach for hashtag recommendation, targeting English language tweets on Twitter. First, we make use of a skip-gram model to learn distributed word...
Recently the theories of sparse representation (SR) and dictionary learning (DL) have brought much attention and become powerful tools for pattern recognition and computer vision. Due to the fact that images can be represented in a sparse and compressible way with respect to some dictionaries, these theories have shown successful applications in many different areas including face recognition, image...
Between the growth of Internet or World Wide Web (WWW) and the emersion of the social networking site like Friendster, Myspace etc., information society started facing exhilarating challenges in language technology applications such as Machine Translation (MT) and Information Retrieval (IR). Nevertheless, there were researchers working in Machine Translation that deal with real time information for...
Previous work has developed a visual tracking algorithm, based on sparsity, that represents a target as a superposition of templates from a gallery in a fashion that the coefficients are sparsely populated. When occlusions occur, sparsity is maintained by bringing additional trivial templates (identity bases) into that gallery. While reported desirable results in visual tracking applications, several...
The problem of multidimensional data reconstruction from an incomplete set of observations is addressed in this paper. It has been recently shown that learned dictionaries are very effective in image denoising and inpainting applications. Here we extend the core idea in image inpainting to the case of 3-D data. Our main objective is to exploit both spatial and spectral/temporal information for recovering...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.