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We present a novel method on dense stereo matching; both high accuracy results and a handling of occlusions can be achieved with the edge constraint we prove in this paper. Though a lot of efforts had been made to solve the problems such as occlusions and disparity discontinuities, dense stereo matching is still very challenging in the field of stereo vision. Variable window methods seem to be a good...
The digital volume pulse (DVP) is a physiological signal obtained from the photoplethysmography. Since the photoplethysmography measures volumetric changes of blood occurred by heart contraction and properties of blood vessels, DVP contains several feature parameters that reflect cardiovascular events, such as arterial stiffness. In order to extract these parameters, a number of algorithms have been...
This paper investigates the effects of feature selection via dimensionality reduction techniques for the task of object class recognition. Two filter-based algorithms are considered namely Correlation-based Feature Selection (CFS) and Principal Components Analysis (PCA). A Support Vector Machine is used to compare these two techniques against classical feature concatenation, based on the Graz02 dataset...
Intra-plaque neovascularization is considered to be an important indication for plaque vulnerability. We propose a semi-automatic algorithm that may supply quantitative analysis of neovasculature and reconstruction of its tree, thus enabling assessment of plaque vulnerability. An algorithm for automatic contrast spot detection and for tracking these spots using Multidimensional Dynamic Programming...
Enjoyment is a vital component in the business model of the game industry. Despite research on their relationship to the success or failure of a game, little attention has been paid to the effect of player performance on player enjoyment. This study investigates how player motivation, player performance, and player enjoyment are connected in Ever Quest II, a popular massively multiplayer online role-playing...
In this paper, we propose an advanced automatic modulation classification (AMC) method for cognitive radio (CR). Conventional AMC algorithms employ some pattern recognition algorithms such as hidden markov model (HMM) and support vector machine (SVM) to recognize the signal modulations through the characters of spectral correlation, e.g., a-profile, f-profile, average value, and etc. However, these...
Network traffic classification is an essential component for network management and security systems. To address the limitations of traditional port-based and payload-based methods, recent studies have been focusing on alternative approaches. One promising direction is applying machine learning techniques to classify traffic flows based on packet and flow level statistics. In particular, previous...
Selective sampling has been widely used in relevance feedback of image retrieval to alleviate the burden of labeling by selecting the most informative instances for user to label. Traditional sample selection scheme often selects a batch of instances each time and label them simultaneously, which ignores the correlation among instances and results in redundant labeling. In this paper, we propose an...
A novel feature selection method was proposed for electromyography (EMG)-based affective recognition. First of all, correlation analysis was used to reduce the dimension of original feature subset; then adaptive Tabu search algorithm combined with intensification and diversification strategies was adopted for feature selection, and mutation operator of genetic algorithm (GA) was implemented as the...
A fault detection system based on data mining techniques is developed in this work. A novel concept of feature selection based on the k-way correlation is introduced and used to detect redundant measures relevant features (strong and weak relevant) and/or redundant ones is introduced. The authors propose to apply STRASS, a contextual filter algorithm to identify the relevant features on simulated...
This paper describes research on the use of feature selection techniques to find correlation between single-nucleotide-polymorphism (SNP) in genes with the lupus disease in Genome-Wide Association (GWA) study. Feature selection is the process of selecting features that are correlated and discarding features that have no correlation in data mining. In this research, feature selection techniques will...
Feature selection (FS) is a classical combinatorial problem in pattern recognition and data mining. It finds major importance in classification and regression scenarios. In this paper, a hybrid approach that combines branch-and-bound (BB) search with Bhattacharya distance based feature selection is presented for classifying hyperspectral data using Support Vector Machine (SVM) classifiers. The performance...
Intruder is one of the most publicized threats to security. In recent years, intrusion detection has emerged as an important technique for network security. Data mining techniques have been applied as a new approach for intrusion detection. The quality of the feature selection methods is one of the important factors that affect the effectiveness of Intrusion Detection system (IDS). This paper evaluates...
A license plate identification system comprises three main parts: isolation of the license plate, extraction of the characters, and recognition of the characters. This paper is a contribution for solving the problems related to the first part, more specifically the license plate verification. We present a new license plate locating approach inspired from the context of the fingerprint with two steps...
In this paper, several clustering methodologies are investigated in order to group together wind parks with close statistical behaviour. The proposed approach is practically founded on a fast incremental algorithm. The latter requires the definition of an objective function which is based in the present case on the definition of a Pearson correlation coefficient level. The advantage of such a clustering...
This work presents a classifier capable of classifying websites accessed by a community of users based on characteristics of web traffic they generate. The classifier introduced here can be used for engineering web traffic. This classifier is capable of identifying a few different classes of websites based on intensity of bursts of web requests made to each website in both the long and the short run...
Extracting good features is critical to the performance of learning algorithms such as classifiers. Feature extraction selects and transforms original features to find information hidden in data. Due to the huge search space of selection and transformation of features, exhaustive search is computationally prohibitive and randomized search such as evolutionary algorithms (EA) are often used. In our...
Multi-stage classification algorithm is proposed for the special character of ship target high resolution range profile (HRRP). Range alignment, elimination of abnormal HRRP and noncoherent average are all used to process the ship HRRP before feature extraction. Multi-stage classification algorithm is designed for different feature to contribute their predominance in different stage. Classification...
A new neuron classification algorithm called NCA is proposed in the paper.The neurons of NCA divided the sample space non-linearly and adjusted themselves to cover the sample space optimally.After the noise data processed,the generalization of NCA has been enhanced.In the forecast,the introduction of law of attraction not only overcome the lack of Euclidean distance but also increases the accuracy...
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